Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Planning Algorithms
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
HERB: a home exploring robotic butler
Autonomous Robots
Generality and legibility in mobile manipulation
Autonomous Robots
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Humanoid motion planning for dual-arm manipulation and re-grasping tasks
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Addressing pose uncertainty in manipulation planning using task space regions
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Motion planning with dynamics by a synergistic combination of layers of planning
IEEE Transactions on Robotics
Task Space Regions: A framework for pose-constrained manipulation planning
International Journal of Robotics Research
Coupled dynamical system based arm-hand grasping model for learning fast adaptation strategies
Robotics and Autonomous Systems
Learning and reasoning with action-related places for robust mobile manipulation
Journal of Artificial Intelligence Research
Guiding sampling-based motion planning by forward and backward discrete search
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
International Journal of Robotics Research
CHOMP: Covariant Hamiltonian optimization for motion planning
International Journal of Robotics Research
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We present an approach to path planning for manipulators that uses Workspace Goal Regions (WGRs) to specify goal end-effector poses. Instead of specifying a discrete set of goals in the manipulator's configuration space, we specify goals more intuitively as volumes in the manipulator's workspace. We show that WGRs provide a common framework for describing goal regions that are useful for grasping and manipulation. We also describe two randomized planning algorithms capable of planning with WGRs. The first is an extension of RRT-JT that interleaves exploration using a Rapidly-exploring Random Tree (RRT) with exploitation using Jacobian-based gradient descent toward WGR samples. The second is the IKBiRRT algorithm, which uses a forward-searching tree rooted at the start and a backward-searching tree that is seeded by WGR samples. We demonstrate both simulation and experimental results for a 7DOF WAM arm with a mobile base performing reaching and pick-and-place tasks. Our results show that planning with WGRs provides an intuitive and powerful method of specifying goals for a variety of tasks without sacrificing efficiency or desirable completeness properties.