This paper considers a trajectory planning problem for a robot navigating complex terrains, which arises in applications ranging from autonomous mining vehicles to planetary rovers. The problem seeks to find a low-cost dynamically feasible trajectory for the robot. The problem is challenging as it requires solving a non-linear optimization problem that often has many local minima due to the complex terrain. To address the challenge, we propose a method called Pareto-optimal Warm-started Trajectory Optimization (PWTO) that attempts to combine the benefits of graph search and trajectory optimization, two very different approaches to planning. PWTO first creates a state lattice using simplified dynamics of the robot and leverages a multi-objective graph search method to obtain a set of paths. Each of the paths is then used to warm-start a local trajectory optimization process, so that different local minima are explored to find a globally low-cost solution. In our tests, the solution cost computed by PWTO is often less than half of the costs computed by the baselines. In addition, we verify the trajectories generated by PWTO in Gazebo simulation in complex terrains with both wheeled and quadruped robots. The code of this paper is open sourced and can be found at https://github.com/rap-lab-org/public_pwto.

翻译：本文研究机器人导航复杂地形时的轨迹规划问题，该问题广泛存在于从自主采矿车辆到行星探测车的各类应用中。该问题旨在为机器人寻找一条低成本的动态可行轨迹。由于复杂地形导致非线性优化问题常存在大量局部极小值，该问题极具挑战性。为应对这一挑战，我们提出一种名为帕累托最优热启动轨迹优化（PWTO）的方法，该方法尝试融合图搜索与轨迹优化这两种差异显著的规划方法的优势。PWTO首先利用简化机器人动力学构建状态网格，并采用多目标图搜索方法获取路径集合。每条路径随后被用于热启动局部轨迹优化过程，从而通过探索不同的局部极小值来寻找全局低成本解。在测试中，PWTO计算得到的解成本通常低于基线方法成本的一半。此外，我们在Gazebo仿真环境中使用轮式与四足机器人在复杂地形中验证了PWTO生成的轨迹。本文代码已开源，可通过 https://github.com/rap-lab-org/public_pwto 获取。