Towards planning for elastic objects
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
The self-reconfiguring robotic molecule: design and control algorithms
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Robot Motion Planning
A Motion Planning Approach to Flexible Ligand Binding
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Motion Planning for a Class of Planar Closed-chain Manipulators
International Journal of Robotics Research
Complexity of the mover's problem and generalizations
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Path planning for deformable linear objects
IEEE Transactions on Robotics
Architectural space planning using evolutionary computing approaches: a review
Artificial Intelligence Review
Randomized path planning on manifolds based on higher-dimensional continuation
International Journal of Robotics Research
Hi-index | 0.00 |
Motion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end-effector placement for articulated linkages. It is usually computationally too expensive to apply sampling-based planners to these problems since it is difficult to generate valid configurations. We overcome this challenge by redefining the robotâ聙聶s degrees of freedom and constraints into a new set of parameters, called reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the number of the robotâ聙聶s degrees of freedom. In addition to supporting efficient sampling of configurations, we show that the RD-space formulation naturally supports planning and, in particular, we design a local planner suitable for use by sampling-based planners. We demonstrate the effectiveness and efficiency of our approach for several systems including closed chain planning with multiple loops, restricted end-effector sampling, and on-line planning for drawing/sculpting. We can sample single-loop closed chain systems with 1,000 links in time comparable to open chain sampling, and we can generate samples for 1,000-link multi-loop systems of varying topologies in less than a second.