论文标题
部分可观测时空混沌系统的无模型预测
Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability
论文作者
论文摘要
不确定性在机器人技术中很普遍。由于测量噪声和复杂的动态，我们无法估计确切的系统和环境状态。由于不能保证在拥挤的，不确定的环境中找到安全的控制策略，因此我们提出了一种基于密度的方法。我们的方法使用神经网络和liouville方程来学习具有不确定初始状态的系统的密度演变。我们可以通过采用基于梯度的优化程序来最大程度地降低碰撞风险来计划可行的轨迹。我们对由现实世界数据和胜过基线方法（例如模型预测性控制和非线性编程）产生的模拟环境和环境进行运动计划实验。尽管我们的方法需要离线计划，但与模型预测控制相比，在线运行时间小100倍。
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.
