We present a motion planning algorithm with probabilistic guarantees for limbed robots with stochastic gripping forces. Planners based on deterministic models with a worst-case uncertainty can be conservative and inflexible to consider the stochastic behavior of the contact, especially when a gripper is installed. Our proposed planner enables the robot to simultaneously plan its pose and contact force trajectories while considering the risk associated with the gripping forces. Our planner is formulated as a nonlinear programming problem with chance constraints, which allows the robot to generate a variety of motions based on different risk bounds. To model the gripping forces as random variables, we employ Gaussian Process regression. We validate our proposed motion planning algorithm on an 11.5 kg six-limbed robot for two-wall climbing. Our results show that our proposed planner generates various trajectories (e.g., avoiding low friction terrain under the low risk bound, choosing an unstable but faster gait under the high risk bound) by changing the probability of risk based on various specifications.

中文翻译：

---

使用非线性规划的具有随机夹持力的四肢机器人的风险感知运动规划

我们为具有随机夹持力的四肢机器人提出了一种具有概率保证的运动规划算法。基于具有最坏情况不确定性的确定性模型的规划器在考虑接触的随机行为时可能是保守且不灵活的，尤其是在安装了夹具时。我们提出的规划器使机器人能够同时规划其姿势和接触力轨迹，同时考虑与夹持力相关的风险。我们的规划器被表述为一个具有机会约束的非线性规划问题，它允许机器人根据不同的风险界限生成各种运动。为了将夹持力建模为随机变量，我们采用高斯过程回归。我们在一个 11.5 kg 的六肢机器人上验证了我们提出的运动规划算法，用于爬墙。