We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 common application scenarios with 5000 test cases each, we show that optimization-based or sampling-based planners alone are not effective for realistic problems where fast planning times are required. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the combination of our sparse roadmap and trajectory optimization provides superior performance over other standard sampling-based planners' combinations. By using a multi-query roadmap instead of generating completely new trajectories for each planning problem, our approach allows for extensions such as persistent control policy information associated with a trajectory across planning problems. Also, the sub-optimality resulting from the sparsity of roadmap, as well as the unexpected disturbances from the environment, can both be overcome by the real-time trajectory optimization process.
Despite the advantages of optimization-based planners, they are not stand-alone planners and their performance is very sensitive to the quality of initializations. Also, numerical trajectory optimization often suffers from the problem of getting stuck in high-cost local optima. Therefore, a natural thought to improve the performance of optimization-based planners is to combine them with global planners. Some existing work, for example and , has proposed online path shortening methods for sampling-based planners. The effect of optimization in those approaches is mostly limited to trajectory smoothing and shortening, and can't account for real-time obstacle avoidance and dynamics constraints. Therefore, those modified sampling-based planners still share the typical slow planning times with other common sampling-based planners. Other researches @cite_14 have presented a combined roadmap and trajectory optimization planning algorithm. However, they additionally focused on avoiding singularities in redundant manipulators and meeting Cartesian constraints resulting in relatively long planning times. In comparison, our approach aims at fast reactive real-time planning in practical planning scenarios, and extensive experiment results in Section show that our approach reaches this goal.
@cite_14
2217544563
We present a parallel Cartesian planning algorithm for redundant robot arms and manipulators. We pre-compute a roadmap, that takes into account static obstacles in the environment as well as singular configurations. At runtime, multiple paths in this roadmap are computed as initial trajectories for an optimization-based planner that tends to satisfy various constraints corresponding to demands on the trajectory, including end-effector constraints, collision-free, and non-singular. We highlight and compare the performance of our parallel planner using 7-DOF arms with other planning algorithms. To the best of our knowledge, this is the first approach that can compute smooth and collision-free trajectories in complex environments with dynamic obstacles.