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Добірка наукової літератури з теми "Robots de terrain"

Автор: Grafiati
Опубліковано: 21 грудня 2024
Оновлено: 19 липня 2025

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Статті в журналах з теми "Robots de terrain"

1

Luneckas, Tomas, Mindaugas Luneckas, and Dainius Udris. "Terrain Irregularity Sensing by Evaluating Feet Coordinate Standard Deviation." Applied Sciences 15, no. 1 (2025): 411. https://doi.org/10.3390/app15010411.

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Анотація:
Locomotion over rough terrain is still a problem yet to be solved for legged robots. One of the problems arises from the inability to identify terrain roughness during locomotion, which could be crucial for decision-making and successful task completion. Our proposed terrain roughness method is inspired by the observation that humans can sense their limb position in space without looking at them, which allows us to estimate obstacle heights. This method is based on robot feet coordinate standard deviation (further referred to as SD) parameter evaluation. SD values could be categorized to represent different terrain roughness, and such categories could be useful for selecting different gaits for different terrains. In this paper, we investigate the possibility of using already known feet coordinates to evaluate terrain roughness by calculating their standard deviation (SD). We present simulation results that show that the SD value only depends on terrain roughness and is not influenced by large terrain slopes. Experiments were conducted with real robots while walking over obstacles with different gaits to validate the method. This research mainly aims to test how robot gaits influence SD parameters for terrain roughness evaluation. The experimental results showed that the SD parameter calculated from the robot’s foot coordinates can be used to evaluate terrain roughness. The robot’s gaits have little to no influence on the SD parameter.
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2

Žák, Marek, Jaroslav Rozman, and František V. Zbořil. "Design and Control of 7-DOF Omni-directional Hexapod Robot." Open Computer Science 11, no. 1 (2020): 80–89. http://dx.doi.org/10.1515/comp-2020-0189.

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Анотація:
AbstractLegged robots have great potential to travel across various types of terrain. Their many degrees of freedom enable them to navigate through difficult terrains, narrow spaces or various obstacles and they can move even after losing a leg. However, legged robots mostly move quite slowly. This paper deals with the design and construction of an omni-directional seven degrees of freedom hexapod (i.e., six-legged) robot, which is equipped with omnidirectional wheels (two degrees of freedom are used, one for turning the wheel and one for the wheel itself) usable on flat terrain to increase travel speed and an additional coxa joint that makes the robot more robust when climbing inclined terrains. This unique combination of omnidirectional wheels and additional coxa joint makes the robot not only much faster but also more robust in rough terrains and allows the robot to ride inclined terrains up to 40 degrees and remain statically stable in slopes up to 50 degrees. The robot is controlled by a terrain adaptive movement controller which adjusts the movement speed and the gait of the robot according to terrain conditions.
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3

Zhang, Yinglong, Baoru Huang, Meng Hong, Chao Huang, Guan Wang, and Min Guo. "A Terrain Classification Method for Quadruped Robots with Proprioception." Electronics 14, no. 6 (2025): 1231. https://doi.org/10.3390/electronics14061231.

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Анотація:
Acquiring terrain information during robot locomotion is essential for autonomous navigation, gait selection, and trajectory planning. Quadruped robots, due to their biomimetic structures, demonstrate enhanced traversability over complex terrains compared to other robotic platforms. Furthermore, the internal sensors of quadruped robots acquire rich terrain-related data during locomotion across diverse terrains. This study investigates the relationship between terrain characteristics and quadruped robots based on proprioception sensor data, and proposes a simple, efficient, and motion-independent terrain classification method by integrating multiple sensor signals. The sensors referred to in the text only include the IMU sensor and joint encoders, which means that the method has a wide range of applicability while requiring sufficiently low hardware cost. The Convolutional Neural Network will serve as the backbone of the algorithm. In addition, the control command about its own control information will serve as supporting information to eliminate the impact of motion patterns on the results. Employing a multi-label classification algorithm, the complex terrains are classified by multiple physical feature labels like roughness, slippage, softness, and slope, which depict terrain attributes. A feature-labeled terrain dataset is established by abstracting diverse terrain features across various terrains. Unlike semantic labels (e.g., grassland, sand, gravel) that are comprehensible only to humans, feature labels provide a more helpful and precise terrain characterization, including broader terrain attributes.
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4

ZHANG, HE, RUI WU, CHANGLE LI, et al. "ADAPTIVE MOTION PLANNING FOR HITCR-II HEXAPOD ROBOT." Journal of Mechanics in Medicine and Biology 17, no. 07 (2017): 1740040. http://dx.doi.org/10.1142/s0219519417400401.

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Анотація:
Multi-legged robots have the ability to traverse rugged terrain and can surmount the obstacles, which are impossible for being overcome by wheeled robots. In this regard, six-legged (hexapod) robots are considered to provide the best combination of adequate adaptability and control complexity. Their motion planning envisages calculating sequences of footsteps and body posture, accounting for the influence of terrain shape, in order to produce the appropriate foot-end trajectory and ensure stable and flexible motion of hexapod robots on the rugged terrain. In this study, a high-order polynomial is used to describe the trajectory model, and a new motion planning theory is proposed, which is aimed at the adaptation of hexapod robots to more complex terrains. An attempt is made to elaborate the adaptive motion planning and perform its experimental verification for a novel hexapod robot HITCR-II, demonstrating its applicability for walking on the unstructured terrain.
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5

Hao, Qian, Zhaoba Wang, Junzheng Wang, and Guangrong Chen. "Stability-Guaranteed and High Terrain Adaptability Static Gait for Quadruped Robots." Sensors 20, no. 17 (2020): 4911. http://dx.doi.org/10.3390/s20174911.

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Анотація:
Stability is a prerequisite for legged robots to execute tasks and traverse rough terrains. To guarantee the stability of quadruped locomotion and improve the terrain adaptability of quadruped robots, a stability-guaranteed and high terrain adaptability static gait for quadruped robots is addressed. Firstly, three chosen stability-guaranteed static gaits: intermittent gait 1&2 and coordinated gait are investigated. In addition, then the static gait: intermittent gait 1, which is with the biggest stability margin, is chosen to do a further research about quadruped robots walking on rough terrains. Secondly, a position/force based impedance control is employed to achieve a compliant behavior of quadruped robots on rough terrains. Thirdly, an exploratory gait planning method on uneven terrains with touch sensing and an attitude-position adjustment strategy with terrain estimation are proposed to improve the terrain adaptability of quadruped robots. Finally, the proposed methods are validated by simulations.
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6

Cruz Ulloa, Christyan, Lourdes Sánchez, Jaime Del Cerro, and Antonio Barrientos. "Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation." Biomimetics 8, no. 3 (2023): 289. http://dx.doi.org/10.3390/biomimetics8030289.

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Анотація:
Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Rescue Task UPM Robot) quadruped robot based on a central pattern generator (CPG), and the automatic identification of terrain and characterization of its obstacles (number, size, position and superability analysis) through convolutional neural networks for pattern regulation. To develop this method, a study of dog gait patterns was carried out, with validation and adjustment through simulation on the robot model in ROS-Gazebo and subsequent transfer to the real robot. Outdoor tests were carried out to evaluate and validate the efficiency of the proposed method in terms of its percentage of success in overcoming stretches of unstructured terrains, as well as the kinematic and dynamic variables of the robot. The main results show that the proposed method has an efficiency of over 93% for terrain characterization (identification of terrain, segmentation and obstacle characterization) and over 91% success in overcoming unstructured terrains. This work was also compared against main developments in state-of-the-art and benchmark models.
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7

Sutar, Amey V., B. V. Hubballi, and Akash S. Bhosale. "Design and Development of a Four-Wheeled Mobile Robot (WMR) for Any Terrain." Journal of Mechanical Robotics 10, no. 1 (2025): 13–20. https://doi.org/10.46610/jomr.2025.v10i01.002.

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Анотація:
This paper presents the design, development, and analysis of an all-terrain Wheeled Mobile Robot (WMR). A Wheeled Mobile Robot (WMR) is an autonomous robot that uses wheels for locomotion, allowing it to move efficiently on flat surfaces. These robots are commonly used in various applications, from industrial automation to service robots and research platforms. The robot aims to achieve high mobility on diverse terrains, remote teleoperation, and an effective payload handling capability. The research includes the design and implementation of the mechanical structure, electronic components, control systems, and robot performance analysis. Special focus is given to the kinematic behavior, vibration analysis, and simulation of various components. The robot can carry loads up to 5 kg and navigate complex terrains, including stair climbing. The methodology followed includes structure design, integration of electronic components, assembly, and subsequent trials and simulations. The results demonstrate the robot's potential for practical applications in diverse fields such as exploration, rescue operations, and industrial automation. In summary, wheeled mobile robots are versatile, efficient, and widely used in various industries. Their design carefully balances mechanical, electronic, and software components to achieve reliable and autonomous operation. As technology advances, WMRs become more capable and adaptable, expanding their range of applications.
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Hashimoto, Kenji, Yusuke Sugahara, Hun-Ok Lim, and Atsuo Takanishi. "Biped Landing Pattern Modification Method and Walking Experiments in Outdoor Environment." Journal of Robotics and Mechatronics 20, no. 5 (2008): 775–84. http://dx.doi.org/10.20965/jrm.2008.p0775.

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Анотація:
Many researchers have studied walking stability control for biped robots, most of which involve highly precise acceleration controls based on robot model mechanics. Modeling error, however, makes the control algorithms used difficult to apply to biped walking robots intended to transport human users. The “landing pattern modification method” we propose is based on nonlinear admittance control. Theoretical compliance displacement calculated from walking patterns is compared to actual compliance displacement, when a robot's foot contacts slightly uneven terrain. Terrain height is detected and the preset walking pattern is modified accordingly. The new biped foot we also propose forms larger support polygons on uneven terrain than conventional biped foot systems do. Combining our new modification method and foot, a human-carrying biped robot can traverse uneven terrain, as confirmed in walking experiments.
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9

Xue, Yuquan, Liming Wang, Bi He, Yonghui Zhao, Yang Wang, and Longmei Li. "Research on Environmental Adaptability of Force–Position Hybrid Control for Quadruped Robots Based on Model Predictive Control." Electronics 14, no. 8 (2025): 1604. https://doi.org/10.3390/electronics14081604.

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Анотація:
This study proposes a force–position hybrid control method for quadruped robots based on the Model Predictive Control (MPC) algorithm, aiming to address the challenges of stability and adaptability in complex terrain environments. Traditional control methods for quadruped robots are often based on simplified models, neglecting the impact of complex terrains and unstructured environments on control performance. To enhance the real-world performance of quadruped robots, this paper employs the MPC algorithm to integrate force and position control to achieve precise force–position hybrid regulation. By transforming foot-end forces into joint torques and optimizing them using kinematic inverse solutions, the robot’s stability and motion accuracy in challenging terrains is further enhanced. In this study, a Kalman filter-based state estimation method is adopted to estimate the robot’s state in real time, enabling closed-loop control through the MPC framework, combined with kinematic inverse solutions for hybrid control. The experimental results demonstrate that the proposed MPC algorithm significantly improves the robot’s adaptability and control accuracy across various terrains. In particular, it exhibits superior performance and robustness in multi-contact and uneven terrain scenarios. This research provides a novel approach for deploying quadruped robots in dynamic and complex environments and offers strong support for further optimization of motion control strategies.
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Chen, Yang, Yao Wu, Wei Zeng, and Shaoyi Du. "Kinematics Model Estimation of 4W Skid-Steering Mobile Robots Using Visual Terrain Classification." Journal of Robotics 2023 (October 11, 2023): 1–12. http://dx.doi.org/10.1155/2023/1632563.

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Анотація:
Accurate real-time kinematics model is very important for the control of a skid-steering mobile robot. In this study, the kinematics model of the skid-steering mobile robots was first designed based on instantaneous rotation centers (ICRs). Then, the extended Kalman filter (EKF) technique was applied to obtain the parameters of ICRs under the same specific terrain online. To adapt to different terrain environments, the fractal dimension-based SFTA (segmentation-based fractal texture analysis) method was used to extract features of different terrains, and the k-nearest neighbor (KNN) method was used to classify the terrains. In the case of real-time terrain recognition, the filter parameters of the EKF for estimating the ICRs are adjusted adaptively. Experiments on a real skid-steering mobile robot show that this method can quickly estimate the kinematics model of the robot in the case of terrain changes, and can meet the needs of practical applications. The average error of odometer estimation based on visual terrain classification is 0.06 m, while the average error of odometer estimation without terrain classification is 0.14 m.
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Більше джерел

Дисертації з теми "Robots de terrain"

1

Iagnemma, Karl Dubowsky S. "Mobile robots in rough terrain : estimation, motion planning, and control with application to planetary rovers /." Berlin ; New York : Springer, 2004. http://www.loc.gov/catdir/toc/fy0606/2004106986.html.

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2

Caurin, Glauco Augusto de Paula. "Control of walking robots on natural terrain /." [S.l.] : [s.n.], 1994. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10898.

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3

FAHMI, AHMED MOHAMED SHAMEL BAHAAELDEEN. "On Terrain-Aware Locomotion for Legged Robots." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1045132.

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Анотація:
Legged robots are advancing towards being fully autonomous as can be seen by the recent developments in academia and industry. To accomplish breakthroughs in dynamic whole-body locomotion, and to be robust while traversing unexplored complex environments, legged robots have to be terrain aware. Terrain-Aware Locomotion (TAL) implies that the robot can perceive the terrain with its sensors, and can take decisions based on this information. The decisions can either be in planning, control, or in state estimation, and the terrain may vary in geometry or in its physical properties. TAL can be categorized into Proprioceptive Terrain-Aware Locomotion (PTAL), which relies on the internal robot measurements to negotiate the terrain, and Exteroceptive Terrain-Aware Locomotion (ETAL) that relies on the robot’s vision to perceive the terrain. This thesis presents TAL strategies both from a proprioceptive and an exteroceptive perspective. The strategies are implemented at the level of locomotion planning, control, and state estimation, and are using optimization and learning techniques. The first part of this thesis focuses on PTAL strategies that help the robot adapt to the terrain geometry and properties. At the Whole-Body Control (WBC) level, achieving dynamic TAL requires reasoning about the robot dynamics, actuation and kinematic limits as well as the terrain interaction. For that, we introduce a Passive Whole-Body Control (pWBC) framework that allows the robot to stabilize and walk over challenging terrain while taking into account the terrain geometry (inclination) and friction properties. The pWBC relies on rigid contact assumptions which makes it suitable only for stiff terrain. As a consequence, we introduce Soft Terrain Adaptation aNd Compliance Estimation (STANCE) which is a soft terrain adaptation algorithm that generalizes beyond rigid terrain. STANCE consists of a Compliant Contact Consistent Whole-Body Control (c3WBC) that adapts the locomotion strategies based on the terrain impedance, and an online Terrain Compliance Estimator (TCE) that senses and learns the terrain impedance properties to provide it to the c 3WBC. Additionally, we demonstrate the effects of terrains with different impedances on state estimation for legged robots. The second part of the thesis focuses on ETAL strategies that makes the robot aware of the terrain geometry using visual (exteroceptive) information. To do so, we present Vision-Based Terrain-Aware Locomotion (ViTAL) which is a locomotion planning strategy. ViTAL consists of a Vision-Based Pose Adaptation (VPA) algorithm to plan the robot’s body pose, and a Vision-Based Foothold Adaptation (VFA) algorithm to select the robot’s footholds. The VFA is an extension to the state of the art in foothold selection planning strategies. Most importantly, the VPA algorithm introduces a different paradigm for vision-based pose adaptation. ViTAL relies on a set of robot skills that characterizes the capabilities of the robot and its legs. These skills are then learned via self-supervised learning using Convolutional Neural Networks (CNNs). The skills include (but are not limited to) the robot’s ability to assess the terrain’s geometry, avoid leg collisions, and to avoid reaching kinematic limits. As a result, we contribute with an online vision-based locomotion planning strategy that selects the footholds based on the robot capabilities, and the robot pose that maximizes the chances of the robot succeeding in reaching these footholds. Our strategies are extensively validated on the quadruped robots HyQ and HyQReal in simulation and experiment. We show that with the help of these strategies, we can push dynamic legged robots one step closer towards being fully autonomous and terrain aware.
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4

Häselich, Marcel [Verfasser]. "Markov random field terrain classification for autonomous robots in unstructured terrain / Marcel Häselich." Koblenz : Universitätsbibliothek Koblenz, 2015. http://d-nb.info/1064986544/34.

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5

Guedes, Magno Edgar da Silva. "Vision based obstacle detection for all-terrain robots." Master's thesis, FCT - UNL, 2009. http://hdl.handle.net/10362/3650.

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Анотація:
Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores<br>This dissertation presents a solution to the problem of obstacle detection in all-terrain environments,with particular interest for mobile robots equipped with a stereo vision sensor. Despite the advantages of vision, over other kind of sensors, such as low cost, light weight and reduced energetic footprint, its usage still presents a series of challenges. These include the difficulty in dealing with the considerable amount of generated data, and the robustness required to manage high levels of noise. Such problems can be diminished by making hard assumptions, like considering that the terrain in front of the robot is planar. Although computation can be considerably saved, such simplifications are not necessarily acceptable in more complex environments, where the terrain may be considerably uneven. This dissertation proposes to extend a well known obstacle detector that relaxes the aforementioned planar terrain assumption, thus rendering it more adequate for unstructured environments. The proposed extensions involve: (1) the introduction of a visual saliency mechanism to focus the detection in regions most likely to contain obstacles; (2) voting filters to diminish sensibility to noise; and (3) the fusion of the detector with a complementary method to create a hybrid solution, and thus, more robust. Experimental results obtained with demanding all-terrain images show that, with the proposed extensions, an increment in terms of robustness and computational efficiency over the original algorithm is observed
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6

Alves, Nelson Miguel Rosa. "Vision based trail detection for all-terrain robots." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/5015.

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Анотація:
Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores<br>Esta dissertação propõe um modelo para detecção de trilhos baseado na observação de que estes são estruturas salientes no campo visual do robô. Devido à complexidade dos ambientes naturais, uma aplicação directa dos modelos tradicionais de saliência visual não é suficientemente robusta para prever a localização dos trilhos. Tal como noutras tarefas de detecção, a robustez pode ser aumentada através da modulação da computação da saliência com conhecimento implícito acerca das características visuais (e.g. cor) que permitem uma melhor representação do objecto a encontrar. Esta dissertação propõe o uso da estrutura global do objecto, sendo esta uma característica mais estável e previsível para o caso de trilhos naturais. Esta nova componente de conhecimento implícito é especificada em termos de regras de percepção activa, que controlam o comportamento de agentes simples que se comportam em conjunto para computar o mapa de saliência da imagem de entrada. Para o propósito de acumulação de informação histórica acerca da localização do trilho é utilizado um campo neuronal dinâmico com compensação de movimento. Resultados experimentais num conjunto de dados vasto revelam a habilidade do modelo de produzir uma taxa de sucesso de 91% a 20Hz. O modelo demonstra ser robusto em situações onde outros detectores falhariam, tal como quando o trilho não emerge da parte de baixo da imagem, ou quando se encontra consideravelmente interrompido.
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7

Elanjimattathil, Vijayan Aravind. "Dynamic Locomotion of Quadrupedal Robots over Rough Terrain." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240409.

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Анотація:
Previous works have enabled locomotion of quadrupedal robots usingthe ZMP-based motion optimization framework on flat terrain withvarious gait patterns. Locomotion over rough terrain brings in newchallenges such as planning safe footholds for the robot, ensuring kinematicstability during locomotion and avoiding foot slippage over roughterrain etc. In this work, terrain perception is integrated into the ZMPbasedmotion optimization framework to enable robots to perform dynamiclocomotion over rough terrain.In a first step, we extend the foothold optimization framework touse processed terrain information to avoid planning unsafe footholdpositions while traversing over rugged terrain. Further, to avoid kinematicviolations during locomotion over rugged terrain, we presentadditional constraints to the ZMP-based motion optimization frameworkto solve for kinematically feasible motion plans in real-time. Weadd nonlinear kinematic constraints to existing nonlinear ZMP motionoptimization framework and solve a Sequential Quadratic Programming(SQP) problem to obtain feasible motion plans. Lastly, to avoidfoot contact slippage, we drop the approximated terrain normal anduse measured terrain normal at foot contact position to compute thefriction polygon constraints.The proposed algorithms are tested in simulation and on hardwarewith dynamic gaits to validate the effectiveness of this approach toenable quadrupedal robots to traverse rugged terrain safely. The computationaltime and performance of the proposed algorithms were analyzedunder various scenarios and presented as part of this thesis.<br>Tidigare forskning har gjort det möjligt att fyrfotade robotar kan rö- ra sig med hjälp av det ZMP-baserade rörelseoptimeringsramverket på platt terräng med olika gångartsmönster. Nya utmaningar före- kommer med förflyttning över grov terräng såsom planering av säk- ra fotfäste för roboten, säkerställning av kinematiskt stabilitet under rörelse, undvikande av fotglidning på grov terräng, och så vidare. I det här verket är terränguppfattning integrerad i det ZMP-baserade rörelseoptimeringsverket så att robotar kan utföra dynamisk rörelse över grov terräng. I första steget utökar vi fotfästeoptimeringsram- verket för att använda bearbetad information om terrängen med syf- tet att undvika planeringen av osäkra fotfästeplaceringar under för- flyttning över grov terräng. För att undvika kinematiska överträdel- ser under förflyttning över grov terräng introducerar vi ytterligare begränsningar till det ZMP-baserade rörelseoptimeringsramverket för att lösa ut kinematiskt rimliga rörelseplaner i realtid. Vi introducerar icke-linjära kinematiska begränsningar till det existerande icke-linjära ZMP-baserade rörelseoptimeringsramverket och löser ett sekventiellt kvadratiskt programmeringsproblem (SQP problem) för att få rimli- ga rörelseplaner. Med syftet att undvika fotkontaktglidning släpper vi den approximerade terrängnormalen och använder den mätta ter- rängnormalen vid fotkontaktläge för att beräkna friktionspolygonbe- gränsningarna. De föreslagna algoritmerna testas i simulering samt på hårdvara med dynamiska gångarter för att bekräfta denna metods ef- fektivitet att tillåta fyrfotade robotar att flytta sig över grov terräng på ett säkert sätt. Algoritmernas beräkningsperiod och prestanda analy- serades i olika fall och redovisades som en del av detta examensarbete.
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8

Vijaykumar, R. "Motion planning for legged locomotion systems on uneven terrain /." The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487335992904418.

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9

Weiss, Christian. "Self-Localization and terrain classification for mobile outdoor robots /." München : Verl. Dr. Hut, 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017311174&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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10

Ward, Christopher Charles. "Terrain sensing and estimation for dynamic outdoor mobile robots." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42419.

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Анотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.<br>Includes bibliographical references (p. 120-125).<br>In many applications, mobile robots are required to travel on outdoor terrain at high speed. Compared to traditional low-speed, laboratory-based robots, outdoor scenarios pose increased perception and mobility challenges which must be considered to achieve high performance. Additionally, high-speed driving produces dynamic robot-terrain interactions which are normally negligible in low speed driving. This thesis presents algorithms for estimating wheel slip and detecting robot immobilization on outdoor terrain, and for estimating traversed terrain profile and classifying terrain type. Both sets of algorithms utilize common onboard sensors. Two methods are presented for robot immobilization detection. The first method utilizes a dynamic vehicle model to estimate robot velocity and explicitly estimate longitudinal wheel slip. The vehicle model utilizes a novel simplified tire traction/braking force model in addition to estimating external resistive disturbance forces acting on the robot. The dynamic model is combined with sensor measurements in an extended Kalman filter framework. A preliminary algorithm for adapting the tire model parameters is presented. The second, model-free method takes a signal recognition-based approach to analyze inertial measurements to detect robot immobilization. Both approaches are experimentally validated on a robotic platform traveling on a variety of outdoor terrains. Two detector fusion techniques are proposed and experimentally validated which combine multiple detectors to increase detection speed and accuracy. An algorithm is presented to classify outdoor terrain for high-speed mobile robots using a suspension mounted accelerometer. The algorithm utilizes a dynamic vehicle model to estimate the terrain profile and classifies the terrain based on spatial frequency components of the estimated profile. The classification algorithm is validated using experimental results collected with a commercial automobile driving in real-world conditions.<br>by Christopher Charles Ward.<br>S.M.
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Книги з теми "Robots de terrain"

1

Iagnemma, Karl, and Steven Dubowsky. Mobile Robots in Rough Terrain. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b94718.

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Lamon, Pierre. 3D-position tracking and control for all-terrain robots. Springer, 2008.

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Lamon, Pierre. 3D-Position Tracking and Control for All-Terrain Robots. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78287-2.

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Iagnemma, Karl. Mobile robots in rough terrain: Estimation, motion planning, and control with application to planetary rovers. Springer, 2010.

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5

Kwak, Se-Hung. Rule-based motion coordination for the Adaptive Suspension Vehicle on ternary-type terrain. Naval Postgraduate School, 1990.

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6

Kudriashov, Andrii, Tomasz Buratowski, Mariusz Giergiel, and Piotr Małka. SLAM Techniques Application for Mobile Robot in Rough Terrain. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48981-6.

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7

Rickenbach, Mark Douglas. Correction of inertial navigation system drift errors for an autonomous land vehicle using optical radar terrain data. Naval Postgraduate School, 1987.

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8

Gurshtein, Ksenya, and Simonyi, eds. Experimental Cinemas in State Socialist Eastern Europe. Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789462982994.

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Was there experimental cinema behind the Iron Curtain? What forms did experiments with film take in state socialist Eastern Europe? Who conducted them, where, how, and why? These are the questions answered in this volume, the first of its kind in any language. Bringing together scholars from different disciplines, the book offers case studies from Bulgaria, Czech Republic, former East Germany, Hungary, Poland, Romania, and former Yugoslavia. Together, these contributions demonstrate the variety of makers, production contexts, and aesthetic approaches that shaped a surprisingly robust and diverse experimental film output in the region. The book maps out the terrain of our present-day knowledge of cinematic experimentalism in Eastern Europe, suggests directions for further research, and will be of interest to scholars of film and media, art historians, cultural historians of Eastern Europe, and anyone concerned with questions of how alternative cultures emerge and function under repressive political conditions.
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9

A general model of legged locomotion on natural terrain. Kluwer Academic Publishers, 1992.

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Lamon, Pierre. 3D-Position Tracking and Control for All-Terrain Robots. Springer, 2008.

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Частини книг з теми "Robots de terrain"

1

Hert, Susan, Sanjay Tiwari, and Vladimir Lumelsky. "A Terrain-Covering Algorithm for an AUV." In Underwater Robots. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1419-6_2.

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Svennebring, Jonas, and Sven Koenig. "Towards Building Terrain-Covering Ant Robots." In Ant Algorithms. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45724-0_17.

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3

Bhatti, Jawaad, Pejman Iravani, Andrew R. Plummer, and M. Necip Sahinkaya. "Towards Running Robots for Discontinuous Terrain." In Advances in Autonomous Robotics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32527-4_59.

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4

Chocron, Olivier. "Evolving Modular Robots for Rough Terrain Exploration." In Mobile Robots: The Evolutionary Approach. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-49720-2_2.

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Kennedy, Brett, Avi Okon, Hrand Aghazarian, et al. "Lemur IIb: a Robotic System for Steep Terrain Access." In Climbing and Walking Robots. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26415-9_129.

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Zhu, Xiaorui, Youngshik Kim, Mark Andrew Minor, and Chunxin Qiu. "Terrain-Inclination–Based Localization and Mapping." In Autonomous Mobile Robots in Unknown Outdoor Environments. CRC Press, 2017. http://dx.doi.org/10.1201/9781315151496-9.

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Nabulsi, S., M. Armada, and H. Montes. "Multiple Terrain Adaptation Approach Using Ultrasonic Sensors for Legged Robots." In Climbing and Walking Robots. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-26415-9_47.

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Palis, Rusin, Schumucker, Schneider, and Zavgorodniy. "Legged Robot with Articulated Body in Locomotion Over Complex Terrain." In Climbing and Walking Robots. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-29461-9_30.

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Fries, Terrence P. "Evolutionary Navigation of Autonomous Robots Under Varying Terrain Conditions." In Mobile Robots: The Evolutionary Approach. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-49720-2_3.

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Mohseni-Vahed, Shahram, and Yun Qin. "Effect of Different Terrain Parameters on Walking." In Advances in Reconfigurable Mechanisms and Robots I. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4141-9_35.

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Тези доповідей конференцій з теми "Robots de terrain"

1

Manoharan, Amith, Aditya Sharma, Himani Belsare, Kaustab Pal, K. Madhava Krishna, and Arun Kumar Singh. "Bi-level Trajectory Optimization on Uneven Terrains with Differentiable Wheel-Terrain Interaction Model." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802848.

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Datar, Aniket, Chenhui Pan, Mohammad Nazeri, Anuj Pokhrel, and Xuesu Xiao. "Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801650.

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Muenprasitivej, Kasidit, Jesse Jiang, Abdulaziz Shamsah, Samuel Coogan, and Ye Zhao. "Bipedal Safe Navigation over Uncertain Rough Terrain: Unifying Terrain Mapping and Locomotion Stability." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802816.

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4

Werner, Lennart, Pedro Proença, Andreas Nüchter, and Roland Brockers. "Covariance Based Terrain Mapping for Autonomous Mobile Robots." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610010.

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5

Wang, Guan, Xingyu Liu, Yinglong Zhang, and Min Guo. "Classifying terrain for quadruped robots based on acoustic features." In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). IEEE, 2024. https://doi.org/10.1109/aiim64537.2024.10934443.

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6

Rochmanto, Raditya Artha, Bambang Supriyo, Achmad Fahrul Aji, Suryono, and Vinda Setya Kartika. "Edge Computing Based Terrain Detection System for SAR Robots." In 2025 International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI). IEEE, 2025. https://doi.org/10.1109/icocseti63724.2025.11019552.

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7

DuPont, Edmond M., Rodney G. Roberts, Majura F. Selekwa, Carl A. Moore, and Emmanual G. Collins. "Online Terrain Classification for Mobile Robots." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81659.

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Today’s autonomous vehicles operate in an increasingly general set of circumstances. In particular, unmanned ground vehicles (UGV’s) must be able to travel on whatever terrain the mission offers, including sand, mud, or even snow. These terrains can affect the performance and controllability of the vehicle. Like a human driver who feels his vehicle’s response to the terrain and takes appropriate steps to compensate, a UGV that can autonomously perceive its terrain can also make necessary changes to its control strategy. This article focuses on the development and application of a terrain detection algorithm based on terrain induced vehicle vibration. The dominant vibration frequencies are extracted and used by a probabilistic neural network to identify the terrain. Experimental results based on iRobot’s ATRV Jr (Fig. 1) demonstrate that the algorithm is able to identify with high accuracy multi-differentiated terrains broadly classified as sand, grass, asphalt, and gravel.
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8

Arunkumar, V., Devika Rajasekar, and N. Aishwarya. "A Review Paper on Mobile Robots Applications in Search and Rescue Operations." In International Conference on Future Technologies in Manufacturing, Automation, Design and Energy. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-ip2l3t.

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Mobile robots have gained popularity in recent decades, owing to its capacity to be deployed in dangerous environments without jeopardizing humans. Mobile robotic vehicles are frequently used today to carry out tasks including environmental recognition, inspecting urbanized and industrial terrains, for search and rescue activities. Presently, search and rescue robot technology is progressing from experimental and theoretical studies towards applicability. The proper execution of a mobile robotic movement in a working environment depends on being aware of the nearby obstacles and avoiding any collisions that may occur. Robots today are integrated with several smart technologies that are necessary to model the environment and localize their position, control the movements, identify obstructions, and avoid obstacles based on the terrain and surface they are employed on by applying navigational procedures. This paper explores the various mobile robotics systems and their working currently in place utilized for rescue and search operations.
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HOEPFLINGER, MARK A., C. DAVID REMY, MARCO HUTTER, STEFAN HAAG, and ROLAND SIEGWART. "HAPTIC TERRAIN CLASSIFICATION ON NATURAL TERRAINS FOR LEGGED ROBOTS." In Proceedings of the 13th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814329927_0097.

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Medeiros, Vivian Suzano, and Marco Antonio Meggiolaro. "Trajectory Optimization for Hybrid Wheeled-Legged Robots in Challenging Terrain." In VIII Workshop de Teses e Dissertações em Robótica/Concurso de Teses e Dissertações em Robótica. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wtdr_ctdr.2020.14960.

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Wheeled-legged robots are a promising solution for agile locomotion in challenging terrain, combining the speed of the wheels with the ability of the legs to cope with unstructured environments. This paper presents a trajectory optimization framework that allows wheeled-legged robots to navigate in challenging terrain, e.g., steps, slopes, gaps, while negotiating these obstacles with dynamic motions. The framework generates the robot’s base motion as well as the wheels’ positions and contact forces along the trajectory, accounting for the terrain map and the dynamics of the robot. The knowledge of the terrain map allows the optimizer to generate feasible motions for obstacle negotiation in a dynamic manner, at higher speeds. To take full advantage of the hybrid nature of wheeled-legged robots, driving and stepping motions are both considered in a single planning problem that can generate trajectories with purely driving motions or hybrid driving-stepping motions. The optimization is formulated as a Nonlinear Programming Problem (NLP) employing a phase-based parametrization to optimize over the wheels’ motion and contact forces. The reference trajectories are tracked by a hierarchical whole-body controller that computes the torque actuation commands for the robot. The motion plans are verified on the quadrupedal robot ANYmal equipped with non-steerable torque-controlled wheels in simulations and experimental tests. Agile hybrid motions are demonstrated in simulations with discontinuous obstacles, such as floating steps and gaps, at an average speed of 0.75 m/s.
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Звіти організацій з теми "Robots de terrain"

1

Celmins, Aivars. Terrain Exploration by Autonomous Robots. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada383123.

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2

Choset, Howie. Towards Snakes and Snake Robots on Grannular Terrain. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada582230.

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Fuentes, Anthony, Michelle Michaels, and Sally Shoop. Methodology for the analysis of geospatial and vehicle datasets in the R language. Cold Regions Research and Engineering Laboratory (U.S.), 2021. http://dx.doi.org/10.21079/11681/42422.

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The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.
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Whittaker, William. High performance robotic traverse of desert terrain. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/919198.

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Celmins, Aivars. Multimap Procedures for Robot Route Finding in Open Terrain. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada361084.

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Beer, Randall D. A Cockroach-Like Hexapod Robot for Natural Terrain Locomotion. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada326911.

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Beer, Randall D., Roger Quinn, Roy Ritzmann, and Hillel Chiel. A Cockroach-Like Hexapod Robot for Natural Terrain Locomotion. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada333320.

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Udengaard, Martin, and Karl Iagnemma. Design Of An Omnidirectional Mobile Robot For Rough Terrain. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada510606.

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Beer, Randall, Roger Quinn, Roy Ritzmann, and Hillel Chiel. A Cockroach-Like Hexapod Robot for Natural Terrain Locomotion. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada347557.

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Beer, Randall D. A Cockroach-Like Hexapod Robot for Natural Terrain Locomotion. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada358415.

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