Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics and constraints on the system shape and synthesize complex behaviors. Several previous works have shown its benefits in high-dimensional continuous state spaces and under differential constraints. However, long time horizons and planning around obstacles in non-convex spaces pose challenges in guaranteeing convergence or finding optimal solutions. As a result, discrete graph search planners and sampling-based planers are preferred when facing obstacle-cluttered environments. A recently developed algorithm called INSAT effectively combines graph search in the low-dimensional subspace and trajectory optimization in the full-dimensional space for global kinodynamic planning over long horizons. Although INSAT successfully reasoned about and solved complex planning problems, the numerous expensive calls to an optimizer resulted in large planning times, thereby limiting its practical use. Inspired by the recent work on edge-based parallel graph search, we present PINSAT, which introduces systematic parallelization in INSAT to achieve lower planning times and higher success rates, while maintaining significantly lower costs over relevant baselines. We demonstrate PINSAT by evaluating it on 6 DoF kinodynamic manipulation planning with obstacles.

翻译：摘要：轨迹优化是机器人运动规划中广泛使用的技术，通过系统动力学与约束来塑造并合成复杂行为。先前多项研究已证明其在高维连续状态空间及微分约束下的优势。然而，长时间规划以及非凸空间中的避障问题对收敛性保证与最优解求解构成了挑战。因此，在障碍物密集环境中，离散图搜索规划器和基于采样的规划器更受青睐。最新提出的INSAT算法有效结合了低维子空间的图搜索与全维空间的轨迹优化，实现了长时域全局动力学运动规划。尽管INSAT成功求解了复杂规划问题，但大量昂贵的优化器调用导致规划时间过长，限制了其实用性。受近期基于边的并行图搜索研究的启发，我们提出PINSAT，通过引入INSAT的系统化并行化机制，在保持显著低于相关基线的路径成本的同时，实现了更低的规划时间和更高的成功率。我们通过六自由度动力学操作规划（含障碍物）的评估实验验证了PINSAT的性能。