Motion planning for humanoid robots / Kensuke Harada, Eiichi Yoshida, Kazuhito Yokoi, editors.
"Research on humanoid robots has been mostly with the aim of developing robots that can replace humans in the performance of certain tasks. Motion planning for these robots can be quite difficult, due to their complex kinematics, dynamics and environment. It is consequently one of the key resea...
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| Other Authors: | , , |
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| Format: | Book |
| Language: | English |
| Published: |
London :
Springer,
2010.
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| Subjects: |
MARC
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| 245 | 0 | 0 | |a Motion planning for humanoid robots / |c Kensuke Harada, Eiichi Yoshida, Kazuhito Yokoi, editors. |
| 264 | 1 | |a London : |b Springer, |c 2010. | |
| 300 | |a xv, 306 pages : |b illustrations (some colour) ; |c 24 cm | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a 1. Navigation and Gait Planning / Joel Chestnutt -- 2. Compliant Control of Whole-body Multi-contact Behaviors in Humanoid Robots / Luis Sentis -- 3. Whole-body Motion Planning - Building Blocks for Intelligent Systems / Michael Gienger, Marc Toussaint and Christian Goerick -- 4. Planning Whole-body Humanoid Locomotion, Reaching, and Manipulation / Eiichi Yoshida, Claudia Esteves, Oussama Kanoun, Mathieu Poirier, Anthony Mallet, Jean-Paul Laumond and Kazuhito Yokoi -- 5. Efficient Motion and Grasp Planning for Humanoid Robots / Nikolaus Vahrenkamp, Tamim Asfour and R¨udiger Dillmann -- 6. Multi-contact Acyclic Motion Planning and Experiments on HRP-2 Humanoid / Adrien Escande and Abderrahmane Kheddar -- 7. Motion Planning for a Humanoid Robot Based on a Biped Walking Pattern Generator / Kensuke Harada -- 8. Autonomous Manipulation of Movable Obstacles / Mike Stilman -- 9. Multi-modal Motion Planning for Precision Pushing on a Humanoid Robot / Kris Hauser and Victor Ng-Thow-Hing -- 10. A Motion Planning Framework for Skill Coordination and Learning / Marcelo Kallmann and Xiaoxi Jiang -- -- | |
| 505 | 0 | 0 | |g 1. |t Navigation and Gait Planning / |r Joel Chestnutt -- |g 1.1. |t Introduction -- |g 1.1.1. |t Navigation Planning -- |g 1.1.2. |t Navigation and Legs -- |g 1.2. |t Dimensionality Reductions -- |g 1.3. |t Contact Forces and Hybrid Dynamics -- |g 1.4. |t Stance Connectivity -- |g 1.5. |t Terrain Evaluation -- |g 1.6. |t A Simple Example -- |g 1.6.1. |t Environment Representation -- |g 1.6.2. |t The State Space -- |g 1.6.3. |t The Action Model -- |g 1.6.4. |t The State-Action Evaluation Function -- |g 1.6.5. |t Using the Simple Planner -- |g 1.7. |t Estimated Cost Heuristic -- |g 1.8. |t Limited-time and Tiered Planning -- |g 1.9. |t Adaptive Actions -- |g 1.9.1. |t Adaptation Algorithm -- |g 1.10. |t Robot and Environment Dynamics -- |g 1.11. |t Summary -- -- |g 2. |t Compliant Control of Whole-body Multi-contact Behaviors in Humanoid Robots / |r Luis Sentis -- |g 2.1. |t Introduction -- |g 2.2. |t Modeling Humanoids Under Multi-contact Constraints -- |g 2.2.1. |t Kinematic and Dynamic Models -- |g 2.2.2. |t Task Kinematics and Dynamics Under Supporting Constraints -- |g 2.2.3. |t Modeling of Contact Centers of Pressure, Internal Forces, and Co MBehavior -- |g 2.2.4. |t Friction Boundaries for Planning Co Mand Internal Force Behaviors -- |g 2.3. |t Prioritized Whole-body Torque Control -- |g 2.3.1. |t Representation of Whole-body Skills -- |g 2.3.2. |t Prioritized Torque Control -- |g 2.3.3. |t Real-time Handling of Dynamic Constraints -- |g 2.3.4. |t Task Feasibility -- |g 2.3.5. |t Control of Contact Centers of Pressure and Internal Tensions/Moments -- |g 2.4. |t Simulation Results -- |g 2.4.1. |t Multi-contact Behavior -- |g 2.4.2. |t Real-time Response to Dynamic Constraints -- |g 2.4.3. |t Dual Arm Manipulation -- |g 2.5. |t Conclusion and Discussion -- -- |g 3. |t Whole-body Motion Planning - Building Blocks for Intelligent Systems / |r Michael Gienger, Marc Toussaint and Christian Goerick -- |g 3.1. |t Introduction -- |g 3.2. |t Models for Movement Control and Planning -- |g 3.2.1. |t Control System -- |g 3.2.2. |t Trajectory Generation -- |g 3.2.3. |t Task Relaxation: Displacement Intervals -- |g 3.3. |t Stance Point Planning -- |g 3.4. |t Prediction and Action Selection -- |g 3.4.1. |t Visual Perception -- |g 3.4.2. |t Behavior System -- |g 3.4.3. |t Experiments -- |g 3.5. |t Trajectory Optimization -- |g 3.6. |t Planning Reaching and Grasping -- |g 3.6.1. |t Acquisition of Task Maps for Grasping -- |g 3.6.2. |t Integration into Optimization Procedure -- |g 3.6.3. |t Experiments -- |g 3.7. |t Conclusion -- -- |g 4. |t Planning Whole-body Humanoid Locomotion, Reaching, and Manipulation / |r Eiichi Yoshida, Claudia Esteves, Oussama Kanoun, Mathieu Poirier, Anthony Mallet, Jean-Paul Laumond and Kazuhito Yokoi -- |g 4.1. |t Introduction -- |g 4.1.1. |t Basic Motion Planning Methods -- |g 4.1.2. |t Hardware and Software Platform -- |g 4.2. |t Collision-free Locomotion: Iterative Two-stage Approach -- |g 4.2.1. |t Two-stage Planning Framework -- |g 4.2.2. |t Second Stage: Smooth Path Reshaping -- |g 4.3. |t Reaching: Generalized Inverse Kinematic Approach -- |g 4.3.1. |t Method Overview -- |g 4.3.2. |t Generalized Inverse Kinematics for Whole-body Motion -- |g 4.3.3. |t Results -- |g 4.4. |t Manipulation: Pivoting a Large Object -- |g 4.4.1. |t Pivoting and Small-time Controllability -- |g 4.4.2. |t Collision-free pivoting sequence planning -- |g 4.4.3. |t Whole-body Motion Generation and Experiments -- |g 4.4.4. |t Regrasp Planning -- |g 4.5. |t Motion in Real World: Integratingwith Perception -- |g 4.5.1. |t Object Recognition and Localization -- |g 4.5.2. |t Coupling the Motion Plannerwith Perception -- |g 4.5.3. |t Experiments -- |g 4.6. |t Conclusion -- -- |g 5. |t Efficient Motion and Grasp Planning for Humanoid Robots / |r Nikolaus Vahrenkamp, Tamim Asfour and R¨udiger Dillmann -- |g 5.1. |t Introduction -- |g 5.1.1. |t RRT-based Planning -- |g 5.1.2. |t The Motion Planning Framework -- |g 5.2. |t Collision Checks and Distance Calculations -- |g 5.3. |t Weighted Sampling -- |g 5.4. |t Planning Grasping Motions -- |g 5.4.1. |t Predefined Grasps -- |g 5.4.2. |t Randomized IK-solver -- |g 5.4.3. |t RRT-based Planning of Grasping Motions with a Set of Grasps -- |g 5.5. |t Dual Arm Motion Planning for Re-grasping -- |g 5.5.1. |t Dual Arm IK-solver -- |g 5.5.2. |t Reachability Space -- |g 5.5.3. |t Gradient Descent in Reachability Space -- |g 5.5.4. |t Dual Arm J+-RRT -- |g 5.5.5. |t Dual Arm IK-RRT -- |g 5.5.6. |t Planning Hand-off Motions for Two Robots -- |g 5.5.7. |t Experiment on ARMAR-III -- |g 5.6. |t Adaptive Planning -- |g 5.6.1. |t Adaptively Changing the Complexity for Planning -- |g 5.6.2. |t A 3D Example -- |g 5.6.3. |t Adaptive Planning for ARMAR-III -- |g 5.6.4. |t Extensions to Improve the Planning Performance -- |g 5.6.5. |t Experiments -- |g 5.7. |t Conclusion -- -- |g 6. |t Multi-contact Acyclic Motion Planning and Experiments on HRP-2 Humanoid / |r Adrien Escande and Abderrahmane Kheddar -- |g 6.1. |t Introduction -- |g 6.2. |t Overview of the Planner -- |g 6.3. |t Posture Generator -- |g 6.4. |t Contact Planning -- |g 6.4.1. |t Set of Contacts Generation -- |g 6.4.2. |t Rough Trajectory -- |g 6.4.3. |t Using Global Potential Field as Local Optimization Criterion -- |g 6.5. |t Simulation Scenarios -- |g 6.6. |t Experimentation on HRP-2 -- |g 6.7. |t Conclusion -- -- |g 7. |t Motion Planning for a Humanoid Robot Based on a Biped Walking Pattern Generator / |r Kensuke Harada -- |g 7.1. |t Introduction -- |g 7.2. |t Gait Generation Method -- |g 7.2.1. |t Analytical-solution-based Approach -- |g 7.2.2. |t Online Gait Generation -- |g 7.2.3. |t Experiment -- |g 7.3. |t Whole-body Motion Planning -- |g 7.3.1. |t Definitions -- |g 7.3.2. |t Walking Pattern Generation -- |g 7.3.3. |t Collision-free Motion Planner -- |g 7.3.4. |t Results -- |g 7.4. |t Simultaneous Foot-place/Whole-body Motion Planning -- |g 7.4.1. |t Definitions -- |g 7.4.2. |t Gait Pattern Generation -- |g 7.4.3. |t Overall Algorithm -- |g 7.4.4. |t Experiment -- |g 7.5. |t Whole-body Manipulation -- |g 7.5.1. |t Motion Modification -- |g 7.5.2. |t Force-controlled Pushing Manipulation -- |g 7.6. |t Conclusion -- -- |g 8. |t Autonomous Manipulation of Movable Obstacles / |r Mike Stilman -- |g 8.1. |t Introduction -- |g 8.1.1. |t Planning Challenges -- |g 8.1.2. |t Operators -- |g 8.1.3. |t Action Spaces -- |g 8.1.4. |t Complexity of Search -- |g 8.2. |t NAMO Planning -- |g 8.2.1. |t Overview -- |g 8.2.2. |t Configuration Space -- |g 8.2.3. |t Goals for Navigation -- |g 8.2.4. |t Goals for Manipulation -- |g 8.2.5. |t Planning as Graph Search -- |g 8.2.6. |t Planner Prototype -- |g 8.2.7. |t Summary -- |g 8.3. |t Humanoid Manipulation -- |g 8.3.1. |t Background -- |g 8.3.2. |t Biped Controlwith External Forces -- |g 8.3.3. |t Modeling Object Dynamics -- |g 8.3.4. |t Experiments and Results -- |g 8.3.5. |t Summary -- |g 8.4. |t System Integration -- |g 8.4.1. |t From Planning to Execution -- |g 8.4.2. |t Measurement -- |g 8.4.3. |t Planning -- |g 8.4.4. |t Uncertainty -- |g 8.4.5. |t Results -- -- |g 9. |t Multi-modal Motion Planning for Precision Pushing on a Humanoid Robot / |r Kris Hauser and Victor Ng-Thow-Hing -- |g 9.1. |t Introduction -- |g 9.2. |t Background -- |g 9.2.1. |t Pushing -- |g 9.2.2. |t Multi-modal Planning -- |g 9.2.3. |t Complexity and Completeness -- |g 9.3. |t Problem Definition -- |g 9.3.1. |t Configuration Space -- |g 9.3.2. |t Modes -- |g 9.3.3. |t Transitions -- |g 9.4. |t Single-mode Motion Planning -- |g 9.4.1. |t Collision Checking -- |g 9.4.2. |t Walk Planning -- |g 9.4.3. |t Reach Planning -- |g 9.4.4. |t Push Planning -- |g 9.5. |t Multi-modal Planning with Random-MMP -- |g 9.5.1. |t Effects of the Expansion Strategy -- |g 9.5.2. |t Blind Expansion -- |g 9.5.3. |t Utility computation -- |g 9.5.4. |t Utility-centered Expansion -- |g 9.5.5. |t Experimental Comparison of Expansion Strategies -- |g 9.6. |t Postprocessing and System Integration -- |g 9.6.1. |t Visual Sensing -- |g 9.6.2. |t Execution of Walking Trajectories -- |g 9.6.3. |t Smooth Execution of Reach Trajectories -- |g 9.7. |t Experiments -- |g 9.7.1. |t Simulation Experiments -- |g 9.7.2. |t Experiments on ASIMO -- |g 9.8. |t Conclusion -- -- |g 10. |t A Motion Planning Framework for Skill Coordination and Learning / |r Marcelo Kallmann and Xiaoxi Jiang -- |g 10.1. |t Introduction -- |g 10.1.1. |t Related Work -- |g 10.1.2. |t Framework Overview -- |g 10.2. |t Motion Skills -- |g 10.2.1. |t Reaching Skill -- |g 10.2.2. |t Stepping Skill -- |g 10.2.3. |t Balance Skill -- |g 10.2.4. |t Other Skills and Extensions -- |g 10.3. |t Multi-skill Planning -- |g 10.3.1. |t Algorithm Details -- |g 10.3.2. |t Results and Discussion -- |g 10.4. |t Learning -- |g 10.4.1. |t A Similarity Metric for Reaching Tasks -- |g 10.4.2. |t Learning Reaching Strategies -- |g 10.4.3. |t Learning Constraints from Imitation -- |g 10.4.4. |t Results and Discussion -- |g 10.5. |t Conclusion. |
| 520 | |a "Research on humanoid robots has been mostly with the aim of developing robots that can replace humans in the performance of certain tasks. Motion planning for these robots can be quite difficult, due to their complex kinematics, dynamics and environment. It is consequently one of the key research topics in humanoid robotics research and the last few years have witnessed considerable progress in the field. Motion Planning for Humanoid Robots surveys the remarkable recent advancement in both the theoretical and the practical aspects of humanoid motion planning. Various motion planning frameworks are presented in Motion Planning for Humanoid Robots, including one for skill coordination and learning, and one for manipulating and grasping tasks. The problem of planning sequences of contacts that support acyclic motion in a highly constrained environment is addressed and a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table is described. The main areas of interest include: whole body motion planning, task planning, biped gait planning, and sensor feedback for motion planning. Torque-level control of multi-contact behavior, autonomous manipulation of moving obstacles, and movement control and planning architecture are also covered. Motion Planning for Humanoid Robots will help readers to understand the current research on humanoid motion planning. It is written for industrial engineers, advanced undergraduate and postgraduate students."--Publisher's website. | ||
| 588 | |a Machine converted from AACR2 source record. | ||
| 650 | 0 | |a Androids. |9 313735 | |
| 650 | 0 | |a Robots |x Motion |9 339086 | |
| 700 | 1 | |a Harada, Kensuke. |9 1081725 | |
| 700 | 1 | |a Yoshida, Eiichi, |d 1967- |9 1081726 | |
| 700 | 1 | |a Yokoi, Kazuhito. |9 1081727 | |
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