Relevant bibliographies by topics / Robot planning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Robot planning'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Robot planning"

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Llopis-Albert, Carlos, Francisco Rubio, and Francisco Valero. "Optimization approaches for robot trajectory planning." Multidisciplinary Journal for Education, Social and Technological Sciences 5, no. 1 (March 29, 2018): 1. http://dx.doi.org/10.4995/muse.2018.9867.

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<p class="Textoindependiente21">The development of optimal trajectory planning algorithms for autonomous robots is a key issue in order to efficiently perform the robot tasks. This problem is hampered by the complex environment regarding the kinematics and dynamics of robots with several arms and/or degrees of freedom (dof), the design of collision-free trajectories and the physical limitations of the robots. This paper presents a review about the existing robot motion planning techniques and discusses their pros and cons regarding completeness, optimality, efficiency, accuracy, smoothness, stability, safety and scalability.</p>
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Bae, Hyansu, Gidong Kim, Jonguk Kim, Dianwei Qian, and Sukgyu Lee. "Multi-Robot Path Planning Method Using Reinforcement Learning." Applied Sciences 9, no. 15 (July 29, 2019): 3057. http://dx.doi.org/10.3390/app9153057.

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This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments.
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Chen, Hai Long, Xiao Wu, Jun Du, and Jin Ping Tang. "Biped Walking Robot Gait Planning Research." Advanced Materials Research 706-708 (June 2013): 674–77. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.674.

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This paper uses biped walking robot as the research object, and designs robots original system, based on the requirements of Biped Walking Robot Competition of China. According to the biped walking robots characteristics of multi-joints, many degrees of freedom, multivariable, strong coupling and nonlinearity [, we can build system model using the Denavi - Hartenberg coordinate, describe the system model by the homogeneous coordinate transformation theory, and then plan on system gait based on ZMP stability . Finally, we can solve for the joint trajectory of the system by using computer-aided software.
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Karpas, Erez, and Daniele Magazzeni. "Automated Planning for Robotics." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 417–39. http://dx.doi.org/10.1146/annurev-control-082619-100135.

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Modern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.
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Noborio, Hiroshi, and Takashi Tsubouchi. "Special Issue on Robot Motion Planning." Journal of Robotics and Mechatronics 8, no. 1 (February 20, 1996): 1. http://dx.doi.org/10.20965/jrm.1996.p0001.

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This special issue is devoted to robot motion planning. The main scope of this issue covers research work on mobile robotics. Motion planning is necessary when the robot determines its own actions. For the last decade, the paradigm of motion planning in mobile robotics has shifted from off-line motion planning to on-line motion planning and from planning in a static environment to planning in a time-varying environment. Recent progress of computational power has enabled this paradigm shift, since on-line motion planning and planning in time-varying environments require repeated computation based on sensory information which is always renewed. The guest editors organized this special issue in order to highlight those two new paradigms. We present two survey papers: One is a survey of on-line motion planning for a sensor-based navigation of a mobile robot, and the other is a survey of motion planning for mobile robots in a time-varying environment. Along with the survey papers, distinguished technical papers are provided in this special issue. Concerning path planning, a paper describing a case study on motion planning with teaching is included (Ogata et al). Motion planning based on Fuzzy logic is one approach, and three papers from Maeda, Ishikawa et al. and Nagata et al. also belong to this category. To offer a case study on reactive motion decision making, one paper by Ando et al. is included. A recently emerging subject is related to motion planning for cooperation of multiple mobile robots or a single robot among multiple moving obstacles. Three papers from Yoshioka et al., Ota et al., and Fujimura discuss problems on motion planning for cooperation of multiple mobile robots. One paper from Tsubouchi et al. discussed the motion planning of a single robot among multiple moving obstacles. Motion planning to select an appropriate corner cube as a landmark is addressed in the paper from Hashimoto et al. The guest editors hope that this special issue will provide the readers with a lock at some current issues and new perspectives on robot motion planning.
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Panchu, K. Padmanabhan, M. Rajmohan, M. R. Sumalatha, and R. Baskaran. "Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm." Journal of Computational and Theoretical Nanoscience 15, no. 2 (February 1, 2018): 627–36. http://dx.doi.org/10.1166/jctn.2018.7137.

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This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.
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Sun, Zhang, and Chen. "RTPO: A Domain Knowledge Base for Robot Task Planning." Electronics 8, no. 10 (October 1, 2019): 1105. http://dx.doi.org/10.3390/electronics8101105.

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Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.
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Shin, Youshik, and Zeungnam Bien. "Collision–Free Trajectory Planning for Two Robot Arms." Robotica 7, no. 3 (July 1989): 205–12. http://dx.doi.org/10.1017/s0263574700006068.

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SUMMARYAn approach for collision–free trajectory planning along designated paths of two robots in a common workspace is presented. Specifically, in order to describe potential collision between the links of two robots along the designated paths, explicit forms of virtual obstacle are adopted, according to which links of one robot are made to grow while the other robot is forced to shrink as a point on the path. Then, a notion of virtual coordination space is introduced to visualize all the collision–free coordinations of two trajectories. Assuming that a collision–free coordination curve between the two robots is given via a virtual coordination space, the minimum time collision–free trajectory pair for the two robots is sought considering dynamic constraints of torque and velocity bounds of actuators of the two robots.
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Chiu, Min Chie, Long Jyi Yeh, Tian Syung Lan, and Shao Chun Yen. "Positioning and Path Planning for a Swarm Robotic Cleaner." Advanced Materials Research 740 (August 2013): 112–19. http://dx.doi.org/10.4028/www.scientific.net/amr.740.112.

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The main purpose of this paper is to create an efficient ground-sweeping robot equipped with map-establishing and path-planning functions. Two ground-sweeping robots are connected with a master pc via a Blue-tooth protocol. The position of the ground-sweeping robot will be sent back to the master pc allowing the master pc to control the robots during the ground-sweeping process. An environmental map of the sweeping area will be established by emitting an ultrasonic wave from a rotating ultrasonic sensor within the robot. The geometry data will be sent back to the master pc via the Bluetooth module. The map of sweeping area will be made by the master pc using a wall-searching method. A single-chip Microcontroller PIC18F4520 is used as a control core to control the motor speed via the PWM in the robot. The clockwise and counter clockwise rotation of the motor will then be manipulated by a TA7279 IC. The robot is equipped with two ultrasonic modules used to detect the distance between the robot and the obstacle. This information will be sent back to the master pc via the Blue-tooth module. Consequently, results reveal that a prototype of the swarm robot system using two ground-sweeping robots and a master pc has positioning and mapping abilities.
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Alterovitz, Ron, Sven Koenig, and Maxim Likhachev. "Robot Planning in the Real World: Research Challenges and Opportunities." AI Magazine 37, no. 2 (July 4, 2016): 76–84. http://dx.doi.org/10.1609/aimag.v37i2.2651.

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Recent years have seen significant technical progress on robot planning, enabling robots to compute actions and motions to accomplish challenging tasks involving driving, flying, walking, or manipulating objects. However, robots that have been commercially deployed in the real world typically have no or minimal planning capability. These robots are often manually programmed, teleoperated, or programmed to follow simple rules. Although these robots are highly successful in their respective niches, a lack of planning capabilities limits the range of tasks for which currently deployed robots can be used. In this article, we highlight key conclusions from a workshop sponsored by the National Science Foundation in October 2013 that summarize opportunities and key challenges in robot planning and include challenge problems identified in the workshop that can help guide future research towards making robot planning more deployable in the real world.
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Dissertations / Theses on the topic "Robot planning"

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Kruse, Thibault. "Planning for human robot interaction." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30059/document.

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Les avancées récentes en robotique inspirent des visions de robots domestiques et de service rendant nos vies plus faciles et plus confortables. De tels robots pourront exécuter différentes tâches de manipulation d'objets nécessaires pour des travaux de ménage, de façon autonome ou en coopération avec des humains. Dans ce rôle de compagnon humain, le robot doit répondre à de nombreuses exigences additionnelles comparées aux domaines bien établis de la robotique industrielle. Le but de la planification pour les robots est de parvenir à élaborer un comportement visant à satisfaire un but et qui obtient des résultats désirés et dans de bonnes conditions d'efficacité. Mais dans l'interaction homme-robot (HRI), le comportement robot ne peut pas simplement être jugé en termes de résultats corrects, mais il doit être agréable aux acteurs humains. Cela signifie que le comportement du robot doit obéir à des critères de qualité supplémentaire. Il doit être sûr, confortable pour l'homme, et être intuitivement compris. Il existe des pratiques pour assurer la sécurité et offrir un confort en gardant des distances suffisantes entre le robot et des personnes à proximité. Toutefois fournir un comportement qui est intuitivement compris reste un défi. Ce défi augmente considérablement dans les situations d'interaction homme-robot dynamique, où les actions de la personne sont imprévisibles, le robot devant adapter en permanence ses plans aux changements. Cette thèse propose une approche nouvelle et des méthodes pour améliorer la lisibilité du comportement du robot dans des situations dynamiques. Cette approche ne considère pas seulement la qualité d'un seul plan, mais le comportement du robot qui est parfois le résultat de replanifications répétées au cours d'une interaction. Pour ce qui concerne les tâches de navigation, cette thèse présente des fonctions de coûts directionnels qui évitent les problèmes dans des situations de conflit. Pour la planification d'action en général, cette thèse propose une approche de replanification locale des actions de transport basé sur les coûts de navigation, pour élaborer un comportement opportuniste adaptatif. Les deux approches, complémentaires, facilitent la compréhension, par les acteurs et observateurs humains, des intentions du robot et permettent de réduire leur confusion
The recent advances in robotics inspire visions of household and service robots making our lives easier and more comfortable. Such robots will be able to perform several object manipulation tasks required for household chores, autonomously or in cooperation with humans. In that role of human companion, the robot has to satisfy many additional requirements compared to well established fields of industrial robotics. The purpose of planning for robots is to achieve robot behavior that is goal-directed and establishes correct results. But in human-robot-interaction, robot behavior cannot merely be judged in terms of correct results, but must be agree-able to human stakeholders. This means that the robot behavior must suffice additional quality criteria. It must be safe, comfortable to human, and intuitively be understood. There are established practices to ensure safety and provide comfort by keeping sufficient distances between the robot and nearby persons. However providing behavior that is intuitively understood remains a challenge. This challenge greatly increases in cases of dynamic human-robot interactions, where the actions of the human in the future are unpredictable, and the robot needs to constantly adapt its plans to changes. This thesis provides novel approaches to improve the legibility of robot behavior in such dynamic situations. Key to that approach is not to merely consider the quality of a single plan, but the behavior of the robot as a result of replanning multiple times during an interaction. For navigation planning, this thesis introduces directional cost functions that avoid problems in conflict situations. For action planning, this thesis provides the approach of local replanning of transport actions based on navigational costs, to provide opportunistic behavior. Both measures help human observers understand the robot's beliefs and intentions during interactions and reduce confusion
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Switzer, Barbara T. "Robotic path planning with obstacle avoidance /." Online version of thesis, 1993. http://hdl.handle.net/1850/11712.

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Crous, C. B. "Autonomous robot path planning." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2519.

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Thesis (MSc (Mathematical Sciences. Computer SCience))--University of Stellenbosch, 2009.
In this thesis we consider the dynamic path planning problem for robotics. The dynamic path planning problem, in short, is the task of determining an optimal path, in terms of minimising a given cost function, from one location to another within a known environment of moving obstacles. Our goal is to investigate a number of well-known path planning algorithms, to determine for which circumstances a particular algorithm is best suited, and to propose changes to existing algorithms to make them perform better in dynamic environments. At this stage no thorough comparison of theoretical and actual running times of path planning algorithms exist. Our main goal is to address this shortcoming by comparing some of the wellknown path planning algorithms and our own improvements to these path planning algorithms in a simulation environment. We show that the visibility graph representation of the environment combined with the A* algorithm provides very good results for both path length and computational cost, for a relatively small number of obstacles. As for a grid representation of the environment, we show that the A* algorithm produces good paths in terms of length and the amount of rotation and it requires less computation than dynamic algorithms such as D* and D* Lite.
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Dragan, Anca D. "Legible Robot Motion Planning." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/629.

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The goal of this thesis is to enable robots to produce motion that is suitable for human-robot collaboration and co-existence. Most motion in robotics is purely functional: industrial robots move to package parts, vacuuming robots move to suck dust, and personal robots move to clean up a dirty table. This type of motion is ideal when the robot is performing a task in isolation. Collaboration, however, does not happen in isolation. In collaboration, the robot’s motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences that the observer makes, drawing on action interpretation theory in psychology. Predictable motion stems from a goal-to-action inference and matches the observer’s expectation, given the robot’s goal. Legible motion stems from an action-to-goal inference: the robot is clearly conveying its goal with its ongoing motion. We propose models for these inferences based on the principle of rational action, Bayesian inference, and the principle of maximum entropy. We then use a combination of constrained trajectory optimization and machine learning techniques to enable robots to plan motion that is predictable or legible. Finally, we verify that the generated motions are more predictable and legible, and evaluate the impact of such motion on a physical human-robot collaboration task. Our results suggest that predictability and legibility do not only increase task performance, but also make the collaboration process more fluent, increasing subjective metrics such as trust or comfort. We also show generalizations of the legibility formalism to deception, gestures, and assistive teleoperation.
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Wooden, David T. "Graph-based Path Planning for Mobile Robots." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11092006-180958/.

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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007.
Magnus Egerstedt, Committee Chair ; Patricio Vela, Committee Member ; Ayanna Howard, Committee Member ; Tucker Balch, Committee Member ; Wayne Book, Committee Member.
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Akan, Batu. "Planning and Sequencing Through Multimodal Interaction for Robot Programming." Doctoral thesis, Mälardalens högskola, Inbyggda system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26474.

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Over the past few decades the use of industrial robots has increased the efficiency as well as the competitiveness of several sectors. Despite this fact, in many cases robot automation investments are considered to be technically challenging. In addition, for most small and medium-sized enterprises (SMEs) this process is associated with high costs. Due to their continuously changing product lines, reprogramming costs are likely to exceed installation costs by a large margin. Furthermore, traditional programming methods of industrial robots are too complex for most technicians or manufacturing engineers, and thus assistance from a robot programming expert is often needed. The hypothesis is that in order to make the use of industrial robots more common within the SME sector, the robots should be reprogrammable by technicians or manufacturing engineers rather than robot programming experts. In this thesis, a novel system for task-level programming is proposed. The user interacts with an industrial robot by giving instructions in a structured natural language and by selecting objects through an augmented reality interface. The proposed system consists of two parts: (i) a multimodal framework that provides a natural language interface for the user to interact in which the framework performs modality fusion and semantic analysis, (ii) a symbolic planner, POPStar, to create a time-efficient plan based on the user's instructions. The ultimate goal of this work in this thesis is to bring robot programming to a stage where it is as easy as working together with a colleague.This thesis mainly addresses two issues. The first issue is a general framework for designing and developing multimodal interfaces. The general framework proposed in this thesis is designed to perform natural language understanding, multimodal integration and semantic analysis with an incremental pipeline. The framework also includes a novel multimodal grammar language, which is used for multimodal presentation and semantic meaning generation. Such a framework helps us to make interaction with a robot easier and more natural. The proposed language architecture makes it possible to manipulate, pick or place objects in a scene through high-level commands. Interaction with simple voice commands and gestures enables the manufacturing engineer to focus on the task itself, rather than the programming issues of the robot. The second issue addressed is due to inherent characteristics of communication with the use of natural language; instructions given by a user are often vague and may require other actions to be taken before the conditions for applying the user's instructions are met. In order to solve this problem a symbolic planner, POPStar, based on a partial order planner (POP) is proposed. The system takes landmarks extracted from user instructions as input, and creates a sequence of actions to operate the robotic cell with minimal makespan. The proposed planner takes advantage of the partial order capabilities of POP to execute actions in parallel and employs a best-first search algorithm to seek the series of actions that lead to a minimal makespan. The proposed planner can also handle robots with multiple grippers, parallel machines as well as scheduling for multiple product types.
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Cakmak, Maya. "Robot Planning Based On Learned Affordances." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608551/index.pdf.

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This thesis studies how an autonomous robot can learn affordances from its interactions with the environment and use these affordances in planning. It is based on a new formalization of the concept which proposes that affordances are relations that pertain to the interactions of an agent with its environment. The robot interacts with environments containing different objects by executing its atomic actions and learns the different effects it can create, as well as the invariants of the environments that afford creating that effect with a certain action. This provides the robot with the ability to predict the consequences of its future interactions and to deliberatively plan action sequences to achieve a goal. The study shows that the concept of affordances provides a common framework for studying reactive control, deliberation and adaptation in autonomous robots. It also provides solutions to the major problems in robot planning, by grounding the planning operators in the low-level interactions of the robot.
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Marshall, Gillian Fiona. "Resistive grids for robot path-planning." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317916.

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Kent, Simon. "Evolutionary Approaches to Robot Path Planning." Thesis, Brunel University, 1999. http://bura.brunel.ac.uk/handle/2438/1276.

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The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which may be present in such a robot, one of which is path planning — the ability to determine a sequence of positions or configurations between an initial and goal position within a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques.
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Sanders, David Adrian. "Automatic robot path planning with constraints." Thesis, University of Portsmouth, 1990. https://researchportal.port.ac.uk/portal/en/theses/automatic-robot-path-planning-with-constraints(8b5bedfa-68c2-40ac-afad-c318a5037305).html.

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In a complex and flexible manufacturing environment tasks maybe dynamically reconfigured. In this situation a robot needs to plan paths automatically to avoid obstacles and rendezvous with changing target points. A novel path planning system is presented which takes into account both kinematic and dynamic constraints. The main part of the system comprises a robot "Path Planner" and "Path Adapter", both using a dynamic "World Model" updated by a vision system. The Path Planner contains a geometric model of the static environment and the robot. Given a task, the Path Planner calculates an efficient collision free path. This is passed to the control computer where a trajectory is generated. Pre-determination of optimum paths using established techniques frequently involve unacceptably high time penalties. To overcome this problem the automatic path refinement techniques employed avoid the necessity for optimality before beginning a movement. Repeated improvements to the sub optimal paths initially generated by the Path Planner are made until the robot is ready to begin the new path. Algorithms are presented which give a rapid solution for simplified obstacle models. The algorithms are robust and are especially suitable for repetitive robot tasks. With the Path Planner, the robot structure is modelled as connected cylinders and spheres and the range of robot motion is quantised. The robot path, calculated initially only takes account of geometric, kinematic and obstacle constraints. Although this path is sub optimal, the calculation time is short. The path avoids obstacle and seeks the "shortest" path in terms of total actuator movement. Several of the new path planning methods presented employ a local method, taking a "best guess" at a path through a 2-D space for two joints and then calculating a path for the third joint such that obstacles are avoided. A different approach is global and depends on searching a 3-D graph of quantised joint space. The Path Planner works in real time. If there is enough time available a "Path Adapter" modifies the planned path in an effort to improve the path subject to selected criteria. The Path Adapter considers dynamic constraints. The first robot path improvement method depends on detecting the joint motor currents in order to minimise changes in joint direction, the other is based on a set of adaptive rules based on simplified dynamic software models of the robot stored within the planning computer. The adapted path is passed to the control computer. The static model of the robot work-cell is held in computer memory as several solid polyhedral. With the aid of a vision system, this model is updated as new obstacles enter or leave the work-place. Overlapping spheres and 2-D slices in joint space are used to model obstacles. In this form the vision system can be updated quickly and the obstacle data can de accessed efficiently by the path planning and path improvement algorithms.
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Books on the topic "Robot planning"

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Robot motion planning. Boston: Kluwer Academic Publishers, 1991.

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Latombe, Jean-Claude. Robot Motion Planning. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9.

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Koubaa, Anis, Hachemi Bennaceur, Imen Chaari, Sahar Trigui, Adel Ammar, Mohamed-Foued Sriti, Maram Alajlan, Omar Cheikhrouhou, and Yasir Javed. Robot Path Planning and Cooperation. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77042-0.

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Laumond, J. P., ed. Robot Motion Planning and Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0036069.

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Canny, John. The complexity of robot motion planning. Cambridge, Mass: MIT Press, 1988.

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Bonert, Martin. Motion planning for multi-robot assembly systems. Ottawa: National Library of Canada, 1999.

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Lam, Tin Lun. Tree Climbing Robot: Design, Kinematics and Motion Planning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Haight, Timothy A. Layered path planning for an autonomous mobile robot. Monterey, Calif: Naval Postgraduate School, 1994.

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Brown, Brad B. Robot orienteering: Path planning and navigation with uncertain vision. Toronto: University of Toronto, Dept. of Computer Science, 1991.

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Zhang, Yunong. Repetitive Motion Planning and Control of Redundant Robot Manipulators. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Robot planning"

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de Berg, Mark, Marc van Kreveld, Mark Overmars, and Otfried Schwarzkopf. "Robot Motion Planning." In Computational Geometry, 265–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03427-9_13.

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Morecki, Adam, and Józef Knapczyk. "Robot Task Planning." In Basics of Robotics, 319–77. Vienna: Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-2532-8_12.

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de Berg, Mark, Marc van Kreveld, Mark Overmars, and Otfried Cheong Schwarzkopf. "Robot Motion Planning." In Computational Geometry, 267–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04245-8_13.

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Latombe, Jean-Claude. "Dealing with Uncertainty." In Robot Motion Planning, 452–532. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9_10.

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Latombe, Jean-Claude. "Introduction and Overview." In Robot Motion Planning, 1–57. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9_1.

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Latombe, Jean-Claude. "Movable Objects." In Robot Motion Planning, 533–86. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9_11.

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Latombe, Jean-Claude. "Configuration Space of a Rigid Object." In Robot Motion Planning, 58–104. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9_2.

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Latombe, Jean-Claude. "Obstacles in Configuration Space." In Robot Motion Planning, 105–52. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9_3.

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Latombe, Jean-Claude. "Roadmap Methods." In Robot Motion Planning, 153–99. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9_4.

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Latombe, Jean-Claude. "Exact Cell Decomposition." In Robot Motion Planning, 200–247. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4022-9_5.

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Conference papers on the topic "Robot planning"

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Schima, Francis, and Stephen Derby. "Two Robot Arm Cooperative Path Planning Using String Stretching." In ASME 1991 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/cie1991-0159.

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Abstract Two arm robot path planning has many applications. Potential uses of 2 arm robot path planning include terrestrial and space based construction, and general movement of objects. The U.S. space station will most likely be built using robots so that humans do not have to be put into space regularly at great expense and risk. Control of robots from Earth via telerobotics is not practical because the robots will be so far away that there is a delay in the signals due to the great distance between the robots and the controllers and the fact that the signals are limited by the speed of light. The robot arms could also be controlled remotely from space, but only one at a time could be controlled and thus many people would need to be sent up to control all of the robots. One pair of robot arms can replace one human to manually build a structure. However, there would not be any savings in the number of humans sent into space because each pair of robot arms would require a human operator in space. Thus the robot arms should be autonomous or at least semi-autonomous to reduce the number of humans required in space for construction of the space station.
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Wu, Bin, and C. Steve Suh. "Decentralized Multi-Robot Motion Planning Applicable to Dynamic Environment." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10788.

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Abstract Multi-robots navigation in dynamic environment is a promising topic in intelligent robotics with motion planning being one of the fundamental problems. However, in practicel, multi-robots motion planning is challenging with traditional centralized approach since computational demand makes it less practical and robust for the motion planning of a large number of robots. In this paper, a decentralized distribute robots motion planning framework (DDRMPF) is discussed which addresses the specific issue. DDRMPF directly maps raw sensor data to steering command to generate optimal paths for each constituent robot. Unlike centralized method which needs a complete observation along with a center agent which processes heavy data collected from all the robots, DDRMPF allows each agent to generate an optimal local path needing only partial observation, thus rendering motion planning involving large numbers of robots more practical and robust. DDRMPF trains the policy for each robot in the complex and dynamic environment simultaneously based on the reinforcement algorithm.
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Jarvis, Ray. "Robot path planning." In the 2006 international symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1232425.1232430.

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Sarkar, Saurabh, Ernest L. Hall, and Manish Kumar. "Mobile Robot Path Planning Using Support Vector Machines." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2200.

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This paper describes an approach that uses support vector machines (SVM) for path planning of mobile robots. The algorithm generates a collision free path for mobile robots running between two tracks or moving towards a known way point. This approach can negotiate tracks and avoid obstacles which may be initially unknown but are later perceived by the robot, and hence is suitable for use with onboard sensors which provides local information. The approach involves dividing the whole terrain into two different classes, classifying any new point obtained from sensors into either of the classes, and generating a track between both the classes as a path of the robot. SVM generates a non-linear class boundary on the principle of maximizing the margin. The boundary generated by this method is smooth, free of obstacles, and safe for a robot to navigate. The paper presents various case studies and simulation results. Future possibility to integrate this technique with other path planning techniques is also discussed.
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Bonert, M., L. H. Shu, and B. Benhabib. "Motion Planning for Multi-Robot Assembly Systems." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8649.

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Abstract This paper addresses a multi-robot optimal assembly planning problem which in essence is an augmented Travelling Salesperson Problem (TSP+). In our TSP+, both the “salesperson” (a robot with a tool) as well as the “cities” (another robot with a workpiece) move. Namely, in addition to the sequencing of tasks, further planning is required to choose where the “salesperson” should rendezvous with each “city”. The use of a genetic algorithm (GA) is chosen as the search engine for the solution of the multi-robot TSP+ optimization. As an example industrial application the optimization of the electronic-component placement process is addressed. In the most generalized component-placement system configuration, the placement robots meet the component delivery systems (CDSs) at optimal rendezvous locations for the pick-up of components and subsequently meet the printed circuit board (on the mobile XY-table) at optimal rendezvous locations for their placement. In addition to the solution of the component-placement sequencing problem and the rendezvous-point planning problem, the collision-avoidance issue is also addressed.
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Zheng, Huanfei, Zhanrui Liao, and Yue Wang. "Human-Robot Trust Integrated Task Allocation and Symbolic Motion Planning for Heterogeneous Multi-Robot Systems." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9161.

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This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of parallel subtasks. Parallel subtask specifications are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. A dynamic Bayesian network (DBN) based human-robot trust model is constructed considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded human-robot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata.
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Choi, J., and E. Amir. "Combining planning and motion planning." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152872.

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McPherson, Finlay N., Jonathan R. Corney, and Raymond C. W. Sung. "Path Planning for Automated Robot Painting." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35301.

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This paper describes the analysis work underlying the path-planning algorithm for a robotic painting system. The system requires no bespoke production tooling and fills an automation gap in rapid prototyping and manufacturing technology that is currently occupied by hand painting. The system creates images by exposing individual pixels of a photographic coating with a robot-mounted laser. The painting process requires no physical contact so potentially images could be developed on any shape regardless of its complexity: As objects can only be “painted” when their surface can be “hit” (i.e. exposed) by the light beam the system requires six degrees of freedom to ensure all overhanging or reentrant areas can be exposed. The accuracy of serial robots degrades with the length of the kinematic chain (in other words six axis robots cannot position themselves with the same accuracy as four axis ones). Consequently to ensure high precision in the location and orientation of the light source, the object being exposed is mounted on a rotary tilt table within the workspace of a four-axis robot. This gives a six-degree of freedom positioning system composed of two separate kinematic chains. Although the resulting system is accurate the problems of constructing a coordinated path that allows the light beam to efficiently sweep (i.e. cover) the surface regardless of its geometry are challenging. This paper describes the difficulties and, after reviewing existing path planning algorithms, a new algorithm is introduced firstly by describing the nature of the system’s configuration space and then further developing this concept as an alternative to a previously described planning algorithm. Having outlined the approach the paper presents a kinematic model for the system and compares the configuration space approach to a purely Cartesian planning approach.
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Paxton, Chris, Yotam Barnoy, Kapil Katyal, Raman Arora, and Gregory D. Hager. "Visual Robot Task Planning." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8793736.

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Le, Duong, and Erion Plaku. "Multi-Robot Motion Planning with Dynamics Guided by Multi-Agent Search." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/744.

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This paper presents an effective multi-robot motion planner that enables each robot to reach its desired location while avoiding collisions with the other robots and the obstacles. The approach takes into account the differential constraints imposed by the underlying dynamics of each robot and generates dynamically-feasible motions that can be executed in the physical world. The crux of the approach is the sampling-based expansion of a motion tree in the continuous state space of all the robots guided by multi-agent search over a discrete abstraction. Experiments using vehicle models with nonlinear dynamics operating in complex environments show significant speedups over related work.
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Reports on the topic "Robot planning"

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Barraquand, Jerome, and Jean-Claude Latombe. Robot Motion Planning: A Distributed Representation Approach. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada209890.

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Kavraki, Lydia, Jean-Claude Latombe, Rajeew Motwani, and P. Raghavan. Randomized Query Processing in Robot Motion Planning. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada326821.

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Ranganathan, Ananth, and Sven Koenig. A Reactive Robot Architecture With Planning on Demand. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada442524.

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Barraquand, Jerome, Bruno Langlois, and Jean-Claude Latombe. Numerical Potential Field Techniques for Robot Path Planning. Fort Belvoir, VA: Defense Technical Information Center, October 1989. http://dx.doi.org/10.21236/ada326999.

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Crabbe, Frderick L., and Rebecca Hwa. Robot Imitation Learning of High-Level Planning Information. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada460420.

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Latombe, J. C., A. Lazanas, and S. Shekhar. Robot Motion Planning with Uncertainty in Control and Sensing. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada323613.

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Kaelbling, Leslie P., and Tomas Lozano-Perez. Integrated Robot Task and Motion Planning in the Now. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada564092.

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Pin, Francois G. Multi-optimization Criteria-based Robot Behavioral Adaptability and Motion Planning. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/835388.

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Ruiz, Javier Matias. Predictive Sampling-Based Robot Motion Planning in Unmodeled Dynamic Environments. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1573326.

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Pin, Francois G. Multi-optimization Criteria-based Robot Behavioral Adaptability and Motion Planning. Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/835385.

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