Many-RRT*: Robust Joint-Space Trajectory Planning for Serial Manipulators

TL;DR

Many-RRT* is a novel sampling-based motion planner that efficiently finds high-quality, robust trajectories for high-DoF serial manipulators by exploring multiple inverse kinematic solutions simultaneously, outperforming existing methods.

Contribution

It introduces Many-RRT*, a parallel goal sampling extension of RRT* that improves success rates and trajectory quality in complex environments for serial manipulators.

Findings

Achieves 44.5% lower trajectory cost compared to baseline methods.

Attains 100% success rate in diverse environments, outperforming previous RRT variants.

Maintains computational efficiency comparable to existing sampling-based planners.

Abstract

The rapid advancement of high degree-of-freedom (DoF) serial manipulators necessitates the use of swift, sampling-based motion planners for high-dimensional spaces. While sampling-based planners like the Rapidly-Exploring Random Tree (RRT) are widely used, planning in the manipulator's joint space presents significant challenges due to non-invertible forward kinematics. A single task-space end-effector pose can correspond to multiple configuration-space states, creating a multi-arm bandit problem for the planner. In complex environments, simply choosing the wrong joint space goal can result in suboptimal trajectories or even failure to find a viable plan. To address this planning problem, we propose Many-RRT*: an extension of RRT*-Connect that plans to multiple goals in parallel. By generating multiple IK solutions and growing independent trees from these goal configurations simultaneously alongside a single start tree, Many-RRT* ensures that computational effort is not wasted on suboptimal IK solutions. This approach maintains robust convergence and asymptotic optimality. Experimental evaluations across robot morphologies and diverse obstacle environments demonstrate that Many-RRT* provides higher quality trajectories (44.5% lower cost in the same runtime) with a significantly higher success rate (100% vs. the next best of 1.6%) than previous RRT iterations without compromising on runtime performance.