We present a novel receding-horizon multi-contact motion planner for legged robots in challenging scenarios, able to plan motions such as chimney climbing, navigating very narrow passages or crossing large gaps. Our approach adds new capabilities to the state of the art, including the ability to reactively re-plan in response to new information, and planning contact locations and whole-body trajectories simultaneously, simplifying the implementation and removing the need for post-processing or complex multi-stage approaches. Our method is more resistant to local minima problems than other potential field based approaches, and our quadratic-program-based posture generator returns nodes more quickly than those of existing algorithms. Rigorous statistical analysis shows that, with short planning horizons (e.g., one step ahead), our planner is faster than the state-of-the-art across all scenarios tested (between 45% and 98% faster on average, depending on the scenario), while planning less efficient motions (requiring 5% fewer to 700% more stance changes on average). In all but one scenario (Chimney Walking), longer planning horizons (e.g., four steps ahead) extended the average planning times (between 73% faster and 400% slower than the state-of-the-art) but resulted in higher quality motion plans (between 8% more and 47% fewer stance changes than the state-of-the-art).

翻译：本文提出了一种新颖的滚动时域多接触运动规划器，用于足式机器人在挑战性场景（如烟囱攀爬、穿越狭窄通道或跨越大型沟壑）中的运动规划。本方法在现有技术基础上新增了多项能力：包括能够根据新信息进行反应式重规划，以及同步规划接触位置与全身轨迹，从而简化了实现流程并消除了后处理或复杂多阶段方法的需求。相较于其他基于势场的方法，本方法对局部极小值问题具有更强的鲁棒性；同时，基于二次规划的姿势生成器能比现有算法更快地返回节点。严格的统计分析表明：在短规划时域（例如前瞻一步）条件下，本规划器在所有测试场景中均快于现有最优方法（平均快45%至98%，具体取决于场景），但规划出的运动效率较低（平均所需姿态切换次数比最优方法少5%至多700%）。除烟囱行走场景外，较长规划时域（例如前瞻四步）会延长平均规划时间（比最优方法快73%至慢400%），但能生成更高质量的运动方案（姿态切换次数比最优方法多8%至少47%）。