The virtual springs method: path planning and collision avoidance for redundant manipulators
International Journal of Robotics Research
Robot Motion Planning
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Path planning with general end-effector constraints
Robotics and Autonomous Systems
Randomized path planning on manifolds based on higher-dimensional continuation
International Journal of Robotics Research
Robotics and Computer-Integrated Manufacturing
Task-driven posture optimization for virtual characters
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Task-driven posture optimization for virtual characters
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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We explore global randomized joint-space path planning for articulated robots that are subjected to task-space constraints. This paper describes a representation of constrained motion for joint-space planners and develops two simple and efficient methods for constrained sampling of joint configurations: tangent-space sampling (TS) and first-order retraction (FR). FR is formally proven to provide global sampling for linear task-space transformations. Constrained joint-space planning is important for many real-world problems, which involves redundant manipulators. On the one hand, tasks are designated in workspace coordinates: to rotate doors about fixed axes, to slide drawers along fixed trajectories, or to hold objects level during transport. On the other hand, joint-space planning gives alternative paths that use redundant degrees of freedom (DOFs) to avoid obstacles or satisfy additional goals while performing a task. We demonstrate that our methods are faster and more invariant to parameter choices than the techniques that exist.