Journal articles: 'Hidden robot'

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Nourian Zavareh, Mahdi, Fahimeh Nazarimehr, Karthikeyan Rajagopal, and Sajad Jafari. "Hidden Attractor in a Passive Motion Model of Compass-Gait Robot." International Journal of Bifurcation and Chaos 28, no. 14 (December 30, 2018): 1850171. http://dx.doi.org/10.1142/s0218127418501717.

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Many studies have been done on different aspects of biped robots such as motion, path planning, control and stability. Dynamical properties of biped robot on a sloping surface such as equilibria and their stabilities, bifurcations and basin of attraction are investigated in this paper. Basin of attraction is an important property since it can determine the unseen conditions which affect the attractor of the system with multistabilities. By the help of basin of attractions, the paper claims that the strange attractors of compass-gait robot are hidden.

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Wu, Hongmin, Yisheng Guan, and Juan Rojas. "Analysis of multimodal Bayesian nonparametric autoregressive hidden Markov models for process monitoring in robotic contact tasks." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141983484. http://dx.doi.org/10.1177/1729881419834840.

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Robot introspection aids robots to understand what they do and how they do it. Previous robot introspection techniques have often used parametric hidden Markov models or supervised learning techniques, implying that the number of hidden states or classes is defined a priori and fixed through the entire modeling process. Fixed parameterizations limit the modeling power of a process to properly encode the data. Furthermore, first-order Markov models are limited in their ability to model complex data sequences that represent highly dynamic behaviors as they assume observations are conditionally independent given the state. In this work, we contribute a Bayesian nonparametric autoregressive Hidden Markov model for the monitoring of robot contact tasks, which are characterized by complex dynamical data that are hard to model. We used a nonparametric prior that endows our hidden Markov models with an unbounded number of hidden states for a given robot skill (or subtask). We use a hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model of wrench signatures and end-effector pose for the manipulation contact tasks. The proposed scheme monitors both nominal skill execution and anomalous behaviors. Two contact tasks are used to measure the effectiveness of our approach: (i) a traditional pick-and-place task composed of four skills and (ii) a cantilever snap assembly task (also composed of four skills). The modeling performance or our approach was compared with other methods, and classification accuracy measures were computed for skill and anomaly identification. The hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model was shown to have excellent process monitoring performance with higher identification rates and monitoring ability.

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Chen, Tianyan, Jinsong Lin, Deyu Wu, and Haibin Wu. "Research of Calibration Method for Industrial Robot Based on Error Model of Position." Applied Sciences 11, no. 3 (January 31, 2021): 1287. http://dx.doi.org/10.3390/app11031287.

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Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.

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Kheddar, A., C. Tzafestas, and P. Coiffet. "Hidden robot concept — High level abstraction teleoperation." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 433. http://dx.doi.org/10.1016/s0920-5489(99)90875-9.

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Kheddar, A. "Teleoperation based on the hidden robot concept." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 31, no. 1 (2001): 1–13. http://dx.doi.org/10.1109/3468.903862.

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Fox, Maria, Malik Ghallab, Guillaume Infantes, and Derek Long. "Robot introspection through learned hidden Markov models." Artificial Intelligence 170, no. 2 (February 2006): 59–113. http://dx.doi.org/10.1016/j.artint.2005.05.007.

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Savage, Jesus, Oscar Fuentes, Luis Contreras, and Marco Negrete. "Map representation using hidden markov models for mobile robot localization." MATEC Web of Conferences 161 (2018): 03011. http://dx.doi.org/10.1051/matecconf/201816103011.

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This paper describes a map representation and localization system for a mobile robot based on Hidden Markov Models. These models are used not only to find a region where a mobile robot is, but also they find the orientation that it has. It is shown that an estimation of the region where the robot is located can be found using the Viterbi algorithm with quantized laser readings, i.e. symbol observations, of a Hidden Markov Model.

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Durdu, Akif, Aydan M. Erkmen, and Alper Yilmaz. "Reshaping human intention in Human-Robot Interactions by robot moves." Interaction Studies 20, no. 3 (November 18, 2019): 530–60. http://dx.doi.org/10.1075/is.18068.dur.

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Abstract This paper outlines the methodology and experiments associated with the reshaping of human intentions based on robot movements within Human-Robot Interactions (HRIs). Although studies on estimating human intentions are well studied in the literature, reshaping intentions through robot-initiated interactions is a new significant branching in the field of HRI. In this paper, we analyze how estimated human intentions can intentionally change through cooperation with mobile robots in real Human-Robot environments. This paper proposes an intention-reshaping system that includes either the Observable Operator Models (OOMs) or Hidden Markov Models (HMMs) to estimate human intention and decide which moves a robot should perform to reshape previously estimated human intentions into desired ones. At the low level, the system needs to track the locations of all mobile agents using cameras. We test our system on videos taken in a real HRI environment that has been developed as our experimental setup. The results show that OOMs are faster than HMMs and both models give correct decisions for testing sequences.

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Murao, Hajime, and Shinzo Kitamura. "Building up Embodiment in Learning Agents Using A Gaussian Radial Basis Function Neural Network." Journal of Robotics and Mechatronics 12, no. 6 (December 20, 2000): 656–63. http://dx.doi.org/10.20965/jrm.2000.p0656.

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In this paper, we propose actor-critic learning with adaptive state space construction. The gaussian radial basis function neural network is employed for both the actor and the critic modules, where each hidden neuron covers a subspace of the sensor space and so the hidden layer corresponds to the state space. In the proposed algorithm, a robot starts without any states and a new state is generated incrementally by adding a new hidden neuron. One clear advantage of the proposed algorithm to others is the performance improvement by the minimal training after adding a new state, i.e. the adjustment of the connective strength between the new neuron and others is only required after adding a new hidden neuron. This provides an efficient method to construct the state space during the learning in the real world. Resulting state space represents aspects of the environment in which the robot works and the characteristic of the robot itself. In this sense, the obtained state space is said to represent the embodiment of the robot.

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ALNAJJAR, FADY, and KAZUYUKI MURASE. "SELF-ORGANIZATION OF SPIKING NEURAL NETWORK THAT GENERATES AUTONOMOUS BEHAVIOR IN A REAL MOBILE ROBOT." International Journal of Neural Systems 16, no. 04 (August 2006): 229–39. http://dx.doi.org/10.1142/s0129065706000640.

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In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot for generation of adoptive and goal-directed behavior. First, we formulated a SNN model whose inputs and outputs were analog and the hidden unites are interconnected each other. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use-dependent synaptic potentiation and depotentiation at synapses of input layer to hidden layer and of hidden layer to output layer was formulated and implemented into the robot. In the environment, the robot incrementally organized the network and the given tasks were successfully performed. The time needed to acquire the desired adoptive and goal-directed behavior using the proposed self-organization method was much less than that with the genetic evolution, approximately one fifth.

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Hu, Xue, Lun Xie, Xin Liu, and Zhiliang Wang. "Emotion Expression of Robot with Personality." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/132735.

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A robot emotional expression model based on Hidden Markov Model (HMM) is built to enable robots which have different personalities to response in a more satisfactory emotional level. Gross emotion regulation theory and Five Factors Model (FFM) which are the theoretical basis are firstly described. And then the importance of the personality effect on the emotion expression process is proposed, and how to make the effect quantization is discussed. After that, the algorithm of HMM is used to describe the process of emotional state transition and expression, and the performance transferring probability affected by personality is calculated. At last, the algorithm model is simulated and applied in a robot platform. The results prove that the emotional expression model can acquire humanlike expressions and improve the human-computer interaction.

12

Zhou, Faliang, Xiaojun Xu, Haijun Xu, Yukang Chang, Qi Wang, and Jinzhou Chen. "Implementation of a Reconfigurable Robot to Achieve Multimodal Locomotion Based on Three Rules of Configuration." Robotica 38, no. 8 (November 25, 2019): 1478–94. http://dx.doi.org/10.1017/s0263574719001589.

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SUMMARYIn this paper, we focus on the configuration design of a reconfigurable robot that merges the functions of wheels, tracks, and legs together. A deformable rim is utilized to make the robot wheel reconfigurable to change its locomotion mode. Three rules of configuration design to achieve reconfiguration between different modes are proposed: (1) in wheel mode, the track wheel set should be hidden inside the wheel rim; (2) in track/leg mode, the folded wheel rim should be hidden inside the caterpillar loop; (3) the circumference of the wheel rim in wheel mode should be equal to the length of the track ring in track mode. According to these rules, the configuration of the deformable rim, track wheel set, and telescopic spoke are analyzed and designed. A prototype of the reconfigurable wheel is fabricated by three-dimensional printing, and its functions of locomotion in different modes, the switch between different modes, and its load-bearing ability are tested, verifying the effectiveness of the configuration design. Furthermore, a prototype of the reconfigurable robot is manufactured by computerized numerical control (CNC) machining to verify the structural design of the reconfigurable wheel. Compared to traditional hybrid robots with separate wheels, tracks, and legs, this reconfigurable design lends the multimodal robot both excellent terrain adaptability and a compact structure; thus, it can be widely used as a universal mobile platform in search and rescue missions and explosive object disposal missions.

13

Bai, Yun, and YuanBin Hou. "Research of Pose Control Algorithm of Coal Mine Rescue Snake Robot." Mathematical Problems in Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/4751245.

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Aiming at how to achieve optimal control of joint pitch angles in the process of the robot surmounting obstacle, taking the developed coal mine rescue snake robot as an experimental platform, a pose control algorithm based on particle swarm optimization weight coefficient of extreme learning machine (PSOELM) is proposed. In order to obtain the optimized hidden layer matrix of the extreme learning machine (ELM), particle swarm optimization (PSO) is applied to optimize the weight coefficient of hidden layer matrix. The simulation and experiment results prove that, compared with the ELM algorithm, the smaller mean square error (MSE) between the joint pitch angles of robot and the expected values is acquired by the PSOELM, which overcomes the shortcoming that traditional extreme learning machine cannot reach the best performance because of the random selection of the parameters of the hidden layer nodes. PSOELM is superior to ELM algorithm in control accuracy, fast searching for the optimal and stability. Optimal control of robot’s joint pitch angles is achieved. The algorithm is applied to the surmounting obstacle control of the developed snake robot, and it lays the foundation for further implement of the coal mine rescue.

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Li, C. James, and Taehee Kim. "A New Feedforward Neural Network Structural Learning Algorithm—Augmentation by Training With Residuals." Journal of Dynamic Systems, Measurement, and Control 117, no. 3 (September 1, 1995): 411–15. http://dx.doi.org/10.1115/1.2799132.

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A fully automatic feedforward neural network structural and weight learning algorithm is described. The Augmentation by Training with Residuals, ATR, requires neither guess of initial weight values nor the number of neurons in the hidden layer from users. The algorithm takes an incremental approach in which a hidden neuron is trained to model the mapping between the input and output of current exemplars, and is augmented to the existing network. The exemplars are then made orthogonal to the newly identified hidden neuron and used for the training of next hidden neuron. The improvement continues until a desired accuracy is reached. This new structural and weight learning algorithm is applied to the identification of a two-degree-of-freedom planar robot, a Van der Pol oscillator and a Mackay-Glass equation. The algorithm is shown to be effective in modeling all three systems and is far superior to a linear modeling scheme in the case of the robot.

15

Sailaja, M., and R. D. V. Prasad. "Back Propagation Method of Artificial Neural Networks for Finding the Position Control of Stanford Manipulator and Direct Kinematic Analysis of Elbow Manipulator." International Journal of Emerging Research in Management and Technology 7, no. 1 (June 11, 2018): 46. http://dx.doi.org/10.23956/ijermt.v7i1.23.

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Nowadays the robot technology is advancing rapidly and the use of robots in industries has been increasing. In designing a robot manipulator, kinematicsplays a vital role. The kinematic problem of manipulator control is divided into two types, direct kinematics and inverse kinematics. Robot inverse kinematics, which is important in robot path planning, is a fundamental problem in robotic control. Past solutions for this problem have been through the use of various algebraic or algorithmic procedures, which may be less accurate and time consuming. Artificial neural networks have the ability to approximate highly non-linear functions applied in robot control. The neural network approach deserves examination because of the fundamental properties of computation speed, and they can generalize untrained solutions. In the present work an attempt has been made to evaluate the problemof robot inverse kinematics of Stanford manipulator using artificial neural network approach. Finally two programs are written using C language to solve inverse kinematic problem of Stanford manipulator using Back propagation method of artificial neural network. In this network, the input layer has six nodes, the hidden layer has three nodes, and the output layer has two nodes. And also Elbow manipulator was modelled and its direct kinematics was analysed.

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Irfan, Syahid Al, and Nuryono Satya Widodo. "Application of Deep Learning Convolution Neural Network Method on KRSBI Humanoid R-SCUAD Robot." Buletin Ilmiah Sarjana Teknik Elektro 2, no. 1 (May 14, 2020): 40. http://dx.doi.org/10.12928/biste.v2i1.985.

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In a soccer game the ability of humanoid robots that one needs to have is to see the ball object in real time. Development of the ability of humanoid robots to see the ball has been developed but the level of accuracy of object recognition and adaptation during matches still needs to be improved. The architecture designed in this study is Convolutional Neural Network or CNN which is designed to have 6 hidden layers with implementation of the robot program using the Tensorflow library. The pictures taken are used in the training process to have 9 types of images based on where the pictures were taken. Each type of image is divided into 2 classes, namely 2000 images for ball object classes and 2000 images for non-ball object classes. The test is done in real time using a white ball on green grass. From the architectural design and white ball detection test results obtained a success rate of 67%, five of the nine models managed to recognize the ball. The model can recognize objects with an image processing speed of a maximum of 13 FPS.Dalam pertandingan sepak bola kemampuan robot humanoid yang perlu dimiliki salah satunya adalah melihat objek bola secara real time. Pengembangan kemampuan robot humanoid untuk melihat bola telah dikembangkan tetapi tingkat akurasi pengenalan objek dan adaptasi saat pertandingan masih perlu ditingkatkan. Arsitektur yang dirancang pada penelitian ini yaitu Convolutional Neural Network atau CNN yang dirancang memiliki 6 hidden layer dengan implementasi pada program robot menggunakan library Tensorflow. Gambar yang diambil digunakan dalam proses training memiliki 9 jenis gambar berdasarkan tempat pengambilan gambar. Tiap jenis gambar terbagi menjadi 2 class yaitu 2000 gambar untuk class objek bola dan 2000 gambar untuk class objek bukan bola. Pengujian dilakukan secara real time dengan menggunakan bola berwarna putih di atas rumput hijau. Dari perancangan arsitektur dan hasil pengujian pendeteksian bola putih didapatkan persentase keberhasilan 67% yaitu lima dari sembilan model berhasil mengenali bola. Model dapat mengenali objek dengan kecepatan pengolahan gambar adalah maksimal 13 FPS.

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Tao, Chongben, and Guodong Liu. "A Multilayer Hidden Markov Models-Based Method for Human-Robot Interaction." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/384865.

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To achieve Human-Robot Interaction (HRI) by using gestures, a continuous gesture recognition approach based on Multilayer Hidden Markov Models (MHMMs) is proposed, which consists of two parts. One part is gesture spotting and segment module, the other part is continuous gesture recognition module. Firstly, a Kinect sensor is used to capture 3D acceleration and 3D angular velocity data of hand gestures. And then, a Feed-forward Neural Networks (FNNs) and a threshold criterion are used for gesture spotting and segment, respectively. Afterwards, the segmented gesture signals are respectively preprocessed and vector symbolized by a sliding window and a K-means clustering method. Finally, symbolized data are sent into Lower Hidden Markov Models (LHMMs) to identify individual gestures, and then, a Bayesian filter with sequential constraints among gestures in Upper Hidden Markov Models (UHMMs) is used to correct recognition errors created in LHMMs. Five predefined gestures are used to interact with a Kinect mobile robot in experiments. The experimental results show that the proposed method not only has good effectiveness and accuracy, but also has favorable real-time performance.

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Guo, Jianwen, Xiaoyan Li, Zhenpeng Lao, Yandong Luo, Jiapeng Wu, and Shaohui Zhang. "Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer." Advances in Mechanical Engineering 13, no. 5 (May 2021): 168781402110195. http://dx.doi.org/10.1177/16878140211019540.

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Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

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Nascimento, Hugo, Martin Mujica, and Mourad Benoussaad. "Collision Avoidance Interaction Between Human and a Hidden Robot Based on Kinect and Robot Data Fusion." IEEE Robotics and Automation Letters 6, no. 1 (January 2021): 88–94. http://dx.doi.org/10.1109/lra.2020.3032104.

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Vassileva, Daniela, George Boiadjiev, Haruhisa Kawasaki, and Tetsuya Mouri. "Force Compensating Trajectories for Redundant Robots: Experimental Results." Journal of Robotics and Mechatronics 21, no. 1 (February 20, 2009): 104–12. http://dx.doi.org/10.20965/jrm.2009.p0104.

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We proposed a new approach for redundant robots trajectories planning, based on the Null space (or Kernel) features. The Null space (Kernel) exists only in the case of redundant robots and it describes these joints motion which do not affect the robot end-effector motion in the sense of both position and orientation. Based on this “hidden motion” realized in the configuration space, which does not affect the motion in the working zone, we can control independently the robot end-effector position and orientation motions, or just maintain its state while some external force is applied to it. The proposed control strategy is simple, no additional penalty functions are used to restraint the end-effector motion as in the case of the conventional methods. No pseudo inverse kinematics calculations are required; the desired trajectories are generated directly in the configuration space. No complicated control schemes are introduced, the proposed method is based on solving algebraic systems of equations and finding eigenvectors and eigenvalues. In the paper the results from simulations and experiments based on the proposed method are presented and discussed.

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Meng, Liang, Xiao Dong Zhang, and Kai Yang Li. "Design on Real-Time Control System of Lower Limb Exoskeleton Robot Based on Optical Fiber." Advanced Materials Research 588-589 (November 2012): 1499–502. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1499.

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In the exoskeleton robot motion capture system, the sensor characteristics not only should be small in size, light in weight, low power consumption, but also requires easy to wear, reliable real-time. In this article, an exoskeleton robot real-time control system is designed based on the existing fiber-optic sensor. The experimental data is analyzed by hidden Markov model. The system can distinguish between the human bodies six sports: rest, walk, run, squat, stand , on the slope, and it can realize the real-time control on the exoskeleton robot.

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Fu, Jian, Cong Li, Xiang Teng, Fan Luo, and Boqun Li. "Compound Heuristic Information Guided Policy Improvement for Robot Motor Skill Acquisition." Applied Sciences 10, no. 15 (August 3, 2020): 5346. http://dx.doi.org/10.3390/app10155346.

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Discovering the implicit pattern and using it as heuristic information to guide the policy search is one of the core factors to speed up the procedure of robot motor skill acquisition. This paper proposes a compound heuristic information guided reinforcement learning algorithm PI2-CMA-KCCA for policy improvement. Its structure and workflow are similar to a double closed-loop control system. The outer loop realized by Kernel Canonical Correlation Analysis (KCCA) infers the implicit nonlinear heuristic information between the joints of the robot. In addition, the inner loop operated by Covariance Matrix Adaptation (CMA) discovers the hidden linear correlations between the basis functions within the joint of the robot. These patterns which are good for learning the new task can automatically determine the mean and variance of the exploring perturbation for Path Integral Policy Improvement (PI2). Compared with classical PI2, PI2-CMA, and PI2-KCCA, PI2-CMA-KCCA can not only endow the robot with the ability to realize transfer learning of trajectory planning from the demonstration to the new task, but also complete it more efficiently. The classical via-point experiments based on SCARA and Swayer robots have validated that the proposed method has fast learning convergence and can find a solution for the new task.

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OH, SE-YOUNG, WEON-CHANG SHIN, and HYO-GYU KIM. "NEURAL NETWORK BASED DYNAMIC CONTROLLERS FOR INDUSTRIAL ROBOTS." International Journal of Neural Systems 06, no. 03 (September 1995): 257–71. http://dx.doi.org/10.1142/s0129065795000196.

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The industrial robot’s dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller’s excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

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Briot, Sébastien, Victor Rosenzveig, Philippe Martinet, Erol Özgür, and Nicolas Bouton. "Minimal representation for the control of parallel robots via leg observation considering a hidden robot model." Mechanism and Machine Theory 106 (December 2016): 115–47. http://dx.doi.org/10.1016/j.mechmachtheory.2016.08.013.

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Hao, Wu, Jiao Menglin, Tian Guohui, Ma Qing, and Liu Guoliang. "R-KG: A Novel Method for Implementing a Robot Intelligent Service." AI 1, no. 1 (March 2, 2020): 117–40. http://dx.doi.org/10.3390/ai1010006.

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Aiming to solve the problem of environmental information being difficult to characterize when an intelligent service is used, knowledge graphs are used to express environmental information when performing intelligent services. Here, we specially design a kind of knowledge graph for environment expression referred to as a robot knowledge graph (R-KG). The main work of a R-KG is to integrate the diverse semantic information in the environment and pay attention to the relationship at the instance level. Also, through the efficient knowledge organization of a R-KG, robots can fully understand the environment. The R-KG firstly integrates knowledge from different sources to form a unified and standardized representation of a knowledge graph. Then, the deep logical relationship hidden in the knowledge graph is explored. To this end, a knowledge reasoning model based on a Markov logic network is proposed to realize the self-developmental ability of the knowledge graph and to further enrich it. Finally, as the strength of environment expression directly affects the efficiency of robots performing services, in order to verify the efficiency of the R-KG, it is used here as the semantic map that can be directly used by a robot for performing intelligent services. The final results prove that the R-KG can effectively express environmental information.

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Piyathilaka, Lasitha, and Sarath Kodagoda. "Learning Hidden Human Context in 3D Office Scenes by Mapping Affordances Through Virtual Humans." Unmanned Systems 03, no. 04 (October 2015): 299–310. http://dx.doi.org/10.1142/s2301385015400063.

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Ability to learn human context in an environment could be one of the most desired fundamental abilities that a robot should have when sharing a workspace with human co-workers. Arguably, a robot with appropriate human context awareness could lead to a better human–robot interaction. In this paper, we address the problem of learning human context in an office environment by only using 3D point cloud data. Our approach is based on the concept of affordance-map, which involves mapping latent human actions in a given environment by looking at geometric features of the environment. This enables us to learn the human context in the environment without observing real human behaviors which themselves are a nontrivial task to detect. Once learned, affordance-map allows us to assign an affordance cost value for each grid location of the map. These cost maps are later used to develop an active object search strategy and to develop a context-aware global path planning strategy.

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Zhu, Q. "Hidden Markov model for dynamic obstacle avoidance of mobile robot navigation." IEEE Transactions on Robotics and Automation 7, no. 3 (June 1991): 390–97. http://dx.doi.org/10.1109/70.88149.

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Dawood, Farhan, and Chu Kiong Loo. "Robot behaviour learning using Topological Gaussian Adaptive Resonance Hidden Markov Model." Neural Computing and Applications 27, no. 8 (August 25, 2015): 2509–22. http://dx.doi.org/10.1007/s00521-015-2021-x.

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Chen, Jin Jun, and Ting Xiang. "Robot Grasp Pattern Recognition Based on Wavlet and BP Neural Network." Applied Mechanics and Materials 331 (July 2013): 290–93. http://dx.doi.org/10.4028/www.scientific.net/amm.331.290.

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Tactile sensation is one of essential perceptions for a functional robot hand to monitor slip states, grasp objects with proper force, and distinguish different properties of objects etc. A practical tactile sensor based on acoustic-electric converting principle is introduced. The grasp signals of objects of three sort materials are collected by the tactile sensor. The power spectrum feature vectors of them are taken as learning sample book set. Transfer function of neurons in hidden layer is tangent function and that in output layer is logarithmic function. L-M algorithm is selected and convergence precision set is as 0.0001. The hidden layer nodes are taken by experiments as 13. When neural network structure is 8-13-4, BP neural network has the fastest convergence rate and short running time of milliseconds.

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KELLEY, RICHARD, CHRISTOPHER KING, ALIREZA TAVAKKOLI, MIRCEA NICOLESCU, MONICA NICOLESCU, and GEORGE BEBIS. "AN ARCHITECTURE FOR UNDERSTANDING INTENT USING A NOVEL HIDDEN MARKOV FORMULATION." International Journal of Humanoid Robotics 05, no. 02 (June 2008): 203–24. http://dx.doi.org/10.1142/s0219843608001418.

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Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant to situations that involve collaboration among multiple agents or detection of situations that can pose a particular threat. In this paper, we propose an approach that allows a physical robot to detect the intent of others based on experience acquired through its own sensory–motor capabilities, then use this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of hidden Markov models (HMMs) designed to model a robot's experience and interaction with the world when performing various actions. The robot's capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a nonparametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities.

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NERGUI, MYAGMARBAYAR, YUKI YOSHIDA, and WENWEI YU. "HUMAN GAIT BEHAVIOR INTERPRETATION BY A MOBILE HOME HEALTHCARE ROBOT." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240021. http://dx.doi.org/10.1142/s0219519412400210.

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The ultimate goal of this study is to develop autonomous mobile home healthcare robots which closely monitor and evaluate the patients' motor function, and their at-home training therapy process, providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs), and meanwhile, relieve therapists from great burden in canonical rehabilitation. In order to achieve this goal, we have developed the following programs/algorithms for monitoring human activities and recognizing human behaviors: (1) control programs for a mobile robot to track and follow a human subject by three different viewpoints; (2) algorithms for analyzing lower limb joint angles from RGB-D images from a Kinect sensor setup at a mobile robot; and (3) algorithms for recognizing human gait behavior. In (1), side viewpoint, front/back viewpoint and a middle angle viewpoint (between two former viewpoints) tracking were developed. In (2), depth image compensation with colored markers was implemented to deal with the skeleton point extraction error caused by mixing-up and frame flying of depth image during tracking and following human subjects by the mobile robot. In (3), we have proposed a hidden Markov model (HMM) based human behavior recognition using lower limb joint angles and trunk angle. Experimental results showed that joint trajectory could be measured and analyzed with high accuracy compared to a motion tracking system, and human behavior could be recognized from the joint trajectory.

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Pio-Lopez, Léo, Ange Nizard, Karl Friston, and Giovanni Pezzulo. "Active inference and robot control: a case study." Journal of The Royal Society Interface 13, no. 122 (September 2016): 20160616. http://dx.doi.org/10.1098/rsif.2016.0616.

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Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e.g. the dynamics of the hidden states that are inferred during the inference) sheds light on key aspects of the framework such as the quintessentially multimodal nature of control and the differential roles of proprioception and vision. In the discussion, we consider the potential importance of being able to implement active inference in robots. In particular, we briefly review the opportunities for modelling psychophysiological phenomena such as sensory attenuation and related failures of gain control, of the sort seen in Parkinson's disease. We also consider the fundamental difference between active inference and optimal control formulations, showing that in the former the heavy lifting shifts from solving a dynamical inverse problem to creating deep forward or generative models with dynamics, whose attracting sets prescribe desired behaviours.

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Nishio, Kosuke, Fumiko Harada, and Hiromitsu Shimakawa. "Finding Features of Actions Efficiently Synchronized with Dishwashing Robot." Advances in Social Sciences Research Journal 8, no. 2 (February 16, 2021): 206–24. http://dx.doi.org/10.14738/assrj.82.9751.

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In this study, we propose a method for extracting the characteristics of body motions that contribute to reducing the takt time in a cooperative task between a dishwashing robot and a human operator. The proposed method collects the takt time and motion data from novice operators until they become experienced using an inexpensive acceleration sensor. The operation data is classified into experienced and novice periods using the variance value of the takt time. In addition, the Hidden Markov Model is generated to classify the motion data into multiple motion phases. The motion features of the operator are extracted for each phase from the generated model. The proposed method finds the motion features whose difference between the experienced and novice periods are similar to the takt time transition. It uses them as important variables. We verified the effectiveness of the proposed method by conducting experiments that simulate actual work at a restaurant. The Hidden Markov Model classified the operation phases into three categories with the AUC of 0.9. In all samples, we were able to extract the motion characteristics of the experienced operators. This study showed the potential to improve the speed of novice's progress by the extracted motion characteristics to improve education guidelines and to show operators how they should physically move.

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Shatkay, H., and L. P. Kaelbling. "Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap." Journal of Artificial Intelligence Research 16 (March 1, 2002): 167–207. http://dx.doi.org/10.1613/jair.874.

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Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning HMMs/POMDPs can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.

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Nguyen-Duc-Thanh, Nhan, Sungyoung Lee, and Donghan Kim. "Two-Stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction." International Journal of Advanced Robotic Systems 9, no. 2 (January 2012): 39. http://dx.doi.org/10.5772/50204.

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Berg, Julia, Tim Reckordt, Christoph Richter, and Gunther Reinhart. "Action Recognition in Assembly for Human-Robot-Cooperation using Hidden Markov Models." Procedia CIRP 76 (2018): 205–10. http://dx.doi.org/10.1016/j.procir.2018.02.029.

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Tanwani, Ajay Kumar, and Sylvain Calinon. "Learning Robot Manipulation Tasks With Task-Parameterized Semitied Hidden Semi-Markov Model." IEEE Robotics and Automation Letters 1, no. 1 (January 2016): 235–42. http://dx.doi.org/10.1109/lra.2016.2517825.

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Sharif, Mohammadreza. "Particle Filters vs Hidden Markov Models for Prosthetic Robot Hand Grasp Selection." International Journal of Robotic Computing 1, no. 2 (December 1, 2019): 98–122. http://dx.doi.org/10.35708/rc1868-126253.

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Robotic prosthetic hands are commonly controlled using electromyography (EMG) signals as a means of inferring user intention. However, relying on EMG signals alone, although provides very good results in lab settings, is not sufficiently robust to real-life conditions. For this reason, taking advantage of other contextual clues are proposed in previous works. In this work, we propose a method for intention inference based on particle filtering (PF) based on user hand's trajectory information. Our methodology, also provides an estimate of time-to-arrive, i.e. time left until reaching to the object, which is an essential variable in successful grasping of objects. The proposed probabilistic framework can incorporate available sources of information to improve the inference process. We also provide a data-driven method based on hidden Markov model (HMM) as a baseline for intention inference. HMM is widely used for human gesture classification. The algorithms were tested (and trained) with regards to 160 reaching trajectories collected from 10 subjects reaching to one of four objects at a time.

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Jung, Seul, and T. C. Hsia. "A study of neural network control of robot manipulators." Robotica 14, no. 1 (January 1996): 7–15. http://dx.doi.org/10.1017/s0263574700018890.

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SummaryThe basic robot control technique is the model based computer-torque control which is known to suffer performance degradation due to model uncertainties. Adding a neural network (NN) controller in the control system is one effective way to compensate for the ill effects of these uncertainties. In this paper a systematic study of NN controller for a robot manipulator under a unified computed-torque control framework is presented. Both feedforward and feedback NN control schemes are studied and compared using a common back-propagation training algorithm. Effects on system performance for different choices of NN input types, hidden neurons, weight update rates, and initial weight values are also investigated. Extensive simulation studies for trajectory tracking are carried out and compared with other established robot control schemes.

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Lee, Moa, and Joon-Hyuk Chang. "Augmented Latent Features of Deep Neural Network-Based Automatic Speech Recognition for Motor-Driven Robots." Applied Sciences 10, no. 13 (July 2, 2020): 4602. http://dx.doi.org/10.3390/app10134602.

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Speech recognition for intelligent robots seems to suffer from performance degradation due to ego-noise. The ego-noise is caused by the motors, fans, and mechanical parts inside the intelligent robots especially when the robot moves or shakes its body. To overcome the problems caused by the ego-noise, we propose a robust speech recognition algorithm that uses motor-state information of the robot as an auxiliary feature. For this, we use two deep neural networks (DNN) in this paper. Firstly, we design the latent features using a bottleneck layer, one of the internal layers having a smaller number of hidden units relative to the other layers, to represent whether the motor is operating or not. The latent features maximizing the representation of the motor-state information are generated by taking the motor data and acoustic features as the input of the first DNN. Secondly, once the motor-state dependent latent features are designed at the first DNN, the second DNN, accounting for acoustic modeling, receives the latent features as the input along with the acoustic features. We evaluated the proposed system on LibriSpeech database. The proposed network enables efficient compression of the acoustic and motor-state information, and the resulting word error rate (WER) are superior to that of a conventional speech recognition system.

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MCCLAIN, MATTHEW, and STEPHEN LEVINSON. "SEMANTIC BASED LEARNING OF SYNTAX IN AN AUTONOMOUS ROBOT." International Journal of Humanoid Robotics 04, no. 02 (June 2007): 321–46. http://dx.doi.org/10.1142/s0219843607001023.

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It is the goal of the Language Acquisition Group at the University of Illinois at Urbana-Champaign (LAR-UIUC) to build a robot that is able to learn language as well as humans through embodied sensori-motor interaction with the physical world. This paper proposes cognitive structures to enable an autonomous robot to learn the syntax of two-word sentences using its understanding of lexical semantics. A production rule of syntax in Chomsky Normal Form will be explicitly represented using a hidden Markov Model. Results of robotic experiments show that these models can learn representations of syntax in this form and that they can be used to produce novel sentences.

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Viidalepp, Auli. "Representations of robots in science fiction film narratives as signifiers of human identity." Információs Társadalom 20, no. 4 (December 31, 2020): 19. http://dx.doi.org/10.22503/inftars.xx.2020.4.2.

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Recent science fiction has brought anthropomorphic robots from an imaginary far-future to contemporary spacetime. Employing semiotic concepts of semiosis, unpredictability and art as a modelling system, this study demonstrates how the artificial characters in four recent series have greater analogy with human behaviour than that of machines. Through Ricoeur’s notion of identity, this research frames the films’ narratives as typical literary and thought experiments with human identity. However, the familiar sociotopes and technoscientific details included in the narratives concerning data, privacy and human–machine interaction blur the boundary between the human and the machine in both fictional and real-world discourse. Additionally, utilising Haynes’ scientist stereotypes, the research puts the robot makers into focus, revealing their secret agendas and hidden agency behind the artificial creatures.

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Wu, Hongmin, Yisheng Guan, and Juan Rojas. "A Latent State-Based Multimodal Execution Monitor with Anomaly Detection and Classification for Robot Introspection." Applied Sciences 9, no. 6 (March 14, 2019): 1072. http://dx.doi.org/10.3390/app9061072.

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Robot introspection is expected to greatly aid longer-term autonomy of autonomous manipulation systems. By equipping robots with abilities that allow them to assess the quality of their sensory data, robots can detect and classify anomalies and recover appropriately from common anomalies. This work builds on our previous Sense-Plan-Act-Introspect-Recover (SPAIR) system. We introduce an improved anomaly detector that exploits latent states to monitor anomaly occurrence when robots collaborate with humans in shared workspaces, but also present a multiclass classifier that is activated with anomaly detection. Both implementations are derived from Bayesian non-parametric methods with strong modeling capabilities for learning and inference of multivariate time series with complex and uncertain behavior patterns. In particular, we explore the use of a hierarchical Dirichlet stochastic process prior to learning a Hidden Markov Model (HMM) with a switching vector auto-regressive observation model (sHDP-VAR-HMM). The detector uses a dynamic log-likelihood threshold that varies by latent state for anomaly detection and the anomaly classifier is implemented by calculating the cumulative log-likelihood of testing observation based on trained models. The purpose of our work is to equip the robot with anomaly detection and anomaly classification for the full set of skills associated with a given manipulation task. We consider a human–robot cooperation task to verify our work and measure the robustness and accuracy of each skill. Our improved detector succeeded in detecting 136 common anomalies and 368 nominal executions with a total accuracy of 91.0%. An overall anomaly classification accuracy of 97.1% is derived by performing the anomaly classification on an anomaly dataset that consists of 7 kinds of detected anomalies from a total of 136 anomalies samples.

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Korayem, M. H., S. Azargoshasb, A. H. Korayem, and Sh Tabibian. "Design and Implementation of the Voice Command Recognition and the Sound Source Localization System for Human–Robot Interaction." Robotica 39, no. 10 (March 15, 2021): 1779–90. http://dx.doi.org/10.1017/s0263574720001496.

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SUMMARYHuman–robot interaction (HRI) is becoming more important nowadays. In this paper, a low-cost communication system for HRI is designed and implemented on the Scout robot and a robotic face. A hidden Markov model-based voice command detection system is proposed and a non-native database has been collected by Persian speakers, which contains 10 desired English commands. The experimental results confirm that the proposed system is capable to recognize the voice commands, and properly performs the task or expresses the right answer. Comparing the system with a trained system on the Julius native database shows a better true detection (about 10%).

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Kilicaslan, Yilmaz, and Gurkan Tuna. "An Nlp-Based Approach for Improving Human-Robot Interaction." Journal of Artificial Intelligence and Soft Computing Research 3, no. 3 (July 1, 2013): 189–200. http://dx.doi.org/10.2478/jaiscr-2014-0013.

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Abstract This study aims to explore the possibility of improving human-robot interaction (HRI) by exploiting natural language resources and using natural language processing (NLP) methods. The theoretical basis of the study rests on the claim that effective and efficient human robot interaction requires linguistic and ontological agreement. A further claim is that the required ontology is implicitly present in the lexical and grammatical structure of natural language. The paper offers some NLP techniques to uncover (fragments of) the ontology hidden in natural language and to generate semantic representations of natural language sentences using that ontology. The paper also presents the implementation details of an NLP module capable of parsing English and Turkish along with an overview of the architecture of a robotic interface that makes use of this module for expressing the spatial motions of objects observed by a robot

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Kuromiya, Yusuke, Satoshi Ashizawa, Daiki Ando, and Takeo Oomichi. "Development of Detection and Scanning Sensor Mechanism for the Concealed Objects." Journal of Robotics and Mechatronics 22, no. 3 (June 20, 2010): 253–61. http://dx.doi.org/10.20965/jrm.2010.p0253.

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Various detection methods for locating non-visible or concealed objects have been developed, but the accuracy of these has been limited to rough presumptions of position. Now, a dismantling robot with highly accurate positioning has been developed. This robot is designed to cut plasterboard that is fixed to light gauge steel with screws hidden under the plasterboard. A sensor with magnets has been designed to detect and trace this light gauge steel. The sensor incorporates a detecting mode and tracing mode to keep high position accuracy and high scanning speed. It was installed in the dismantling robot and tested for its ability to detect and trace the light gauge steel. Testing has proven this sensing system and design method to be highly successful and suitable for practical use.

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Garvie, Michael, Ittai Flascher, Andrew Philippides, Adrian Thompson, and Phil Husbands. "Evolved Transistor Array Robot Controllers." Evolutionary Computation 28, no. 4 (December 2020): 677–708. http://dx.doi.org/10.1162/evco_a_00272.

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For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.

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Nakadai, Kazuhiro, Ken-ichi Hidai, Hiroshi G. Okuno, Hiroshi Mizoguchi, and Hiroaki Kitano. "Real-time Auditory and Visual Multiple-speaker Tracking For Human-robot Interaction." Journal of Robotics and Mechatronics 14, no. 5 (October 20, 2002): 479–89. http://dx.doi.org/10.20965/jrm.2002.p0479.

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This paper addresses real-time multiple speaker tracking because it is essential in robot perception and human-robot social interaction. The difficulty lies in treating a mixture of sounds, occlusion (some speakers are hidden) and real-time processing. Our approach consists of three components: (1) the extraction of the direction of each speaker by using interaural phase difference and interaural intensity difference, (2) the resolution of each speakers direction by multimodal integration of audition, vision and motion with canceling inevitable motor noises in motion in case of an unseen or silent speaker, and (3) the distributed implementation to three PCs connected by TCP/IP network to attain real-time processing. As a result, we attain robust real-time speaker tracking with 200 ms delay in a non-anechoic room, even when multiple speakers exist and the tracking person is visually occluded. In addition, the feasibility of social interaction is shown through application of our technique to a receptionist robot and a companion robot at a party.

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Наталія Максименко and Валентина Козлова. "ТЕОРЕТИКО-МЕТОДИЧНІ АСПЕКТИ ВИКЛАДАННЯ ОСНОВ БЕЗПЕКИ ЖИТТЄДІЯЛЬНОСТІ В СПЕЦІАЛЬНІЙ ШКОЛІ." International Academy Journal Web of Scholar 2, no. 9(39) (September 30, 2019): 11–16. http://dx.doi.org/10.31435/rsglobal_wos/30092019/6690.

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Nowadays, it’s necessary to bring the problem of security without search, to keep up to date, stay for an hour, all the more, and more to be ashamed. І hoha in ukrainskіy pedagogic training practice naviovannya to wear more academic knowledge, post є the problem of reconciliation spivvіdnoshennya osvitnі dosyagnen ditini to real life, and adaptacii itself to a non-bezpepechny mid-residence.The disadvantage of theoretical development is the power of the core of the robot in the method of representation and on the standards of practical training. Bagato vchitelіv special shots rozіmіyut vіlivіst korektsіyno-vihovno ї roboti for navchannnu uchennіv pushenennyi intertekualnogo development. However, without a systemic reasoning, the ryznі priyomi on the other stages of the robot on uroci і tse not to lead to positive results.The direct development of core development is mainly for the psychic development of children’s interruption of inter-branch development. Under the influence of such a psychic development of schoolchildren to condemn the positive positive patterns, one-time the formation of knowledge and knowledge is consumed. Potentially, the robot is a core robot that is based on the tilting of the interactive development, and is hidden in the direction of the week and the future of the integrated development of the special characteristics.At the dan_y of the statistics of razglânutі okremі results and analysis of the pedagogical practice of forming the basics of the bezpekí zhittєdіyalnost in the classes for the children of torsive development. Pay attention to the importance of this subject, as a self-sufficient, self- righteous one with the most important objects and as methodologically- oriented, directly correctively and robustly. Visklyuyutsya okremі metodnі porady for vchitelіv-defectologіv on rozshirennyu pokrashchennyu koriktsіyno-privivalno ї that swiftly-cyclical robot with the form of a leisureless behavior in schoolchildren.

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Mhatre, Kavita, Nikita Jha, Sonali Dhurway, and Jugnum Parimal. "War Field Robot with Night Vision Camera." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 53–56. http://dx.doi.org/10.35940/ijeat.e2587.0610521.

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Considering the current scenario of warfare between India and China, we got inspired by the thought of various ways technology can help our hardworking soldiers by reducing the human loss by using an application that can spy on the enemy, and also for security purpose. This project’s main purpose is to deal with difficult situations like where humans cannot go through scenarios like darkness, entering narrow areas and detecting hidden bombs etc. The robot serves as a perfect machine for the defense sector in order to reduce the human life loss and will also help in prevention of illegal activities. The robot is self-powered, with a backtracking facility, in case a situation arises where there is connection loss from the base station. Wireless cameras sends back real-time video and audio inputs that can be seen on a monitor in the base station and action can be taken accordingly.

Journal articles on the topic 'Hidden robot'

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