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 smooth, stable, and efficient traversal motions. To achieve this, we develop a planar robot-terrain interaction model and partition the robot's motion into hybrid modes of driving and traversing. By using a generalized coordinate description, the configuration space dimension is reduced, which provides real-time planning capability. The hybrid trajectory optimization is transcribed into a nonlinear programming problem and 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 experiments with the Searcher robotic platform, effectively achieving smooth and stable motion with high time and energy efficiency compared to expert operator control.

翻译：铰接式履带机器人的自主地形穿越可降低操作员认知负荷，从而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，旨在生成平滑、稳定且高效的穿越运动。为此，我们建立了平面机器人-地形交互模型，并将机器人运动划分为驱动与穿越的混合模式。通过采用广义坐标描述，构型空间维度得以降低，从而具备实时规划能力。混合轨迹优化被转化为非线性规划问题，并以滚动时域规划方式求解。通过将优化运动时长与预定义穿越序列相关联，实现了模式切换。进一步构建了多目标代价函数以提升穿越性能。此外，地图采样、地形简化及跟踪控制模块被集成至自主地形穿越系统中。该方法在Searcher机器人平台的仿真与真实实验中得到了验证，与专家操作员控制相比，有效实现了平滑稳定的运动，并具有较高的时间与能量效率。