Optimization-based Motion Planning in Dynamic Environments
Chonhyon Park, Jia Pan and Dinesh
Manocha Abstract
We
present a novel optimization-based algorithm for motion planning in dynamic
environments. Our approach uses a stochastic trajectory optimization
framework to avoid collisions and satisfy smoothness and dynamics
constraints. Our algorithm does not require a priori knowledge about global
motion or trajectories of dynamic obstacles. Rather, we compute a
conservative local bound on the position or trajectory of each obstacle over
a short time and use the bound to compute a collision-free trajectory for the
robot in an incremental manner. Moreover, we interleave planning and
execution of the robot in an adaptive manner to balance between the planning
horizon and responsiveness to obstacle. We highlight the performance of our
planner in a simulated dynamic environment with the 7-DOF PR2 robot arm and
dynamic obstacles.
Papers
Dynamically Balanced and Plausible Trajectory Planning for Human-Like Characters (PDF) [Video] Simulating High-DOF Human-like Agents using Hierarchical Feedback Planner (PDF) [Video] Smooth and Dynamically Stable Navigation of Multiple Human-Like Robots (PDF)
Fast and Dynamically Stable Optimization-based Planning for High-DOF Human-like Robots (PDF)
[Video 1][Video 2][Video 3]
High-DOF Robots in Dynamic Environments using Incremental Trajectory Optimization (PDF) Real-time Optimization-based Planning in Dynamic Environments using GPUs (PDF) [Video] ITOMP: Incremental Trajectory Optimization for Real-time Replanning in Dynamic Environments (PDF) ResultsDynamically Balanced and Plausible Trajectory Planning for Human-Like CharactersPerformance Comparison of ITOMP and OMPL for Amazon Picking Challenge [MP4]Multi-Robot PlanningStable Optimizaton-based Planning for High-DOF Human-like RobotsHierarchical Optimization-based PlanningOptimization-based Planning in Dynamic Environments(a) Success rates
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