Autonomous terrain traversal of articulated tracked robots can reduce operator cognitive load to enhance task efficiency and facilitate extensive deployment. We present a novel hybrid trajectory optimization method aimed at generating efficient, stable, and smooth traversal motions. To achieve this, we develop a planar robot-terrain contact model and divide the robot's motion into hybrid modes of driving and traversing. By using a generalized coordinate description, the configuration space dimension is reduced, which facilitates real-time planning. The hybrid trajectory optimization is transcribed into a nonlinear programming problem and divided into subproblems to be solved in a receding-horizon planning fashion. Mode switching is facilitated by associating optimized motion durations with a predefined traversal sequence. A multi-objective cost function is formulated to further improve the traversal performance. Additionally, map sampling, terrain simplification, and tracking controller modules are integrated into the autonomous terrain traversal system. Our approach is validated in simulation and real-world scenarios with the Searcher robotic platform. Comparative experiments with expert operator control and state-of-the-art methods show advantages in terms of time and energy efficiency, stability, and smoothness of motion.

翻译：铰接式履带机器人的自主越障能力可降低操作员认知负荷，提升任务效率并促进大规模部署。本文提出一种新型混合轨迹优化方法，旨在生成高效、稳定且平滑的越障运动。为此，我们建立了平面机器人-地形接触模型，并将机器人运动划分为驱动与越障的混合模式。通过广义坐标描述降低构型空间维度，从而支持实时规划。将混合轨迹优化转化为非线性规划问题，并采用滚动时域规划方式分解为子问题求解。通过将优化运动时长与预定义越障序列关联实现模式切换，同时构建多目标代价函数进一步优化越障性能。此外，自主越障系统集成了地图采样、地形简化与跟踪控制模块。基于Searcher机器人平台的仿真与真实场景实验验证了该方法有效性。与专家操作控制及现有方法的对比实验表明，本方法在时间/能量效率、运动稳定性与平滑性方面具有显著优势。