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 track?ing 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：自主越野能力可降低铰接式履带机器人操作人员的认知负荷，从而提升任务效率并促进其大规模部署。本文提出一种新型混合轨迹优化方法，旨在生成高效、稳定且平滑的越野运动。为此，我们构建了平面机器人-地形接触模型，并将机器人的运动划分为驱动与越障两种混合模态。通过采用广义坐标描述，降低了构型空间维度，便于实现实时规划。将混合轨迹优化转化为非线性规划问题，并分解为多个子问题，以滚动时域规划方式求解。通过将优化后的运动时长与预定义越障序列相关联，实现模态切换。为提升越障性能，构建了多目标代价函数。此外，将地图采样、地形简化及跟踪控制器模块集成至自主越障系统中。基于Searcher机器人平台，在仿真与真实场景中验证了本方法的有效性。与专家操作控制及现有先进方法的对比实验表明，本方法在时间效率、能量效率、运动稳定性及平滑性方面均具有优势。