ICRA 2011 Paper Abstract


Paper TuP109.3

Lobaton, Edgar (University of North Carolina at Chapel Hill), Zhang, Jinghe (the University of North Carolina, Chapel Hill), Patil, Sachin (UNC Chapel Hill), Alterovitz, Ron (University of North Carolina at Chapel Hill)

Planning Curvature-Constrained Paths to Multiple Goals Using Circle Sampling

Scheduled for presentation during the Regular Sessions "Motion and Path Planning I" (TuP109), Tuesday, May 10, 2011, 14:10−14:25, Room 5D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 21, 2019

Keywords Motion and Path Planning, Nonholonomic Motion Planning


We present a new sampling-based method for planning optimal, collision-free, curvature-constrained paths for nonholonomic robots to visit multiple goals in any order. Rather than sampling configurations as in standard sampling-based planners, we construct a roadmap by sampling circles of constant curvature and then generating feasible transitions between the sampled circles. We provide a closed-form formula for connecting the sampled circles in 2D and generalize the approach to 3D workspaces. We then formulate the multi-goal planning problem as finding a minimum directed Steiner tree over the roadmap. Since optimally solving the multi-goal planning problem requires exponential time, we propose greedy heuristics to efficiently compute a path that visits multiple goals. We apply the planner in the context of medical needle steering where the needle tip must reach multiple goals in soft tissue, a common requirement for clinical procedures such as biopsies, drug delivery, and brachytherapy cancer treatment. We demonstrate that our multi-goal planner significantly decreases tissue that must be cut when compared to sequential execution of single-goal plans.



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