论文标题
非全面机器人动态限制的运动计划网络
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots
论文作者
论文摘要
在当今快速扩展的自动化生态系统中，对机器人的可靠实时规划至关重要。在这样的环境中，对于运动限制的机器人，通过放松约束来计划的传统方法变得不可靠或减速。本文介绍了算法动态运动计划网络（动态MPNET），即运动计划网络的扩展，针对使用神经计划方法来应对实时运动计划的挑战。我们建议对培训和规划网络进行修改，使实时计划成为可能，同时提高培训和受过培训模型的可推广性的数据效率。我们在模拟中评估了非全面机器人计划任务的模型。我们还使用Dubins Car展示了室内导航任务的实验结果。
Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car.
