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Trajectory optimization is a widely used technique in robot motion planningfor letting the dynamics and constraints on the system shape and synthesizecomplex behaviors. Several previous works have shown its benefits inhigh-dimensional continuous state spaces and under differential constraints.However, long time horizons and planning around obstacles in non-convex spacespose challenges in guaranteeing convergence or finding optimal solutions. As aresult, discrete graph search planners and sampling-based planers are preferredwhen facing obstacle-cluttered environments. A recently developed algorithmcalled INSAT effectively combines graph search in the low-dimensional subspaceand trajectory optimization in the full-dimensional space for globalkinodynamic planning over long horizons. Although INSAT successfully reasonedabout and solved complex planning problems, the numerous expensive calls to anoptimizer resulted in large planning times, thereby limiting its practical use.Inspired by the recent work on edge-based parallel graph search, we presentPINSAT, which introduces systematic parallelization in INSAT to achieve lowerplanning times and higher success rates, while maintaining significantly lowercosts over relevant baselines. We demonstrate PINSAT by evaluating it on 6 DoFkinodynamic manipulation planning with obstacles.

PINSAT: Parallelized Interleaving of Graph Search and Trajectory Optimization for Kinodynamic Motion Planning