Uncertainty is prevalent in robotics. Due to measurement noise and complex dynamics, we cannot estimate the exact system and environment state. Since conservative motion planners are not guaranteed to find a safe control strategy in a crowded, uncertain environment, we propose a density-based method. Our approach uses a neural network and the Liouville equation to learn the density evolution for a system with an uncertain initial state. We can plan for feasible and probably safe trajectories by applying a gradient-based optimization procedure to minimize the collision risk. We conduct motion planning experiments on simulated environments and environments generated from real-world data and outperform baseline methods such as model predictive control and nonlinear programming. While our method requires offline planning, the online run time is 100 times smaller compared to model predictive control.
翻译:由于测量噪音和复杂动态,我们无法估计准确的系统和环境状况。由于保守运动规划者不能保证在拥挤、不确定的环境中找到安全控制战略,我们建议采用基于密度的方法。我们的方法使用神经网络和Liouville等式来学习初始状态不确定的系统的密度演变。我们可以通过采用基于梯度的优化程序来尽量减少碰撞风险,来规划可行和可能安全的轨道。我们用模拟环境以及来自现实世界的数据和超效基线方法(如模型预测控制和非线性程序)进行运动规划实验。虽然我们的方法需要离线规划,但在线运行的时间比模型预测控制要小100倍。</s>