Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficult to use in real time. To address this issue, we propose a new generalizable control barrier function (CBF)-based steering controller to reduce the number of samples needed in a sampling-based motion planner RRT. Our method combines the strength of CBF for real-time collision-avoidance control and RRT for long-horizon motion planning, by using CBF-induced neural controller (CBF-INC) to generate control signals that steer the system towards sampled configurations by RRT. CBF-INC is learned as Neural Networks and has two variants handling different inputs, respectively: state (signed distance) input and point-cloud input from LiDAR. In the latter case, we also study two different settings: fully and partially observed environmental information. Compared to manually crafted CBF which suffers from over-approximating robot geometry, CBF-INC can balance safety and goal-reaching better without being over-conservative. Given state-based input, our neural CBF-induced neural controller-enhanced RRT (CBF-INC-RRT) can increase the success rate by 14% while reducing the number of nodes explored by 30%, compared with vanilla RRT on hard test cases. Given LiDAR input where vanilla RRT is not directly applicable, we demonstrate that our CBF-INC-RRT can improve the success rate by 10%, compared with planning with other steering controllers. Our project page with supplementary material is at https://mit-realm.github.io/CBF-INC-RRT-website/.

翻译：在拥挤环境下，基于采样的机械臂运动规划方法常因昂贵的碰撞检测和高采样复杂度而难以实现实时应用。针对此问题，我们提出了一种新型可泛化的控制势垒函数（CBF）引导控制器，以减少基于采样的运动规划器RRT所需的样本数量。该方法通过将CBF的实时碰撞规避控制能力与RRT的长程运动规划优势相结合，利用CBF诱导的神经控制器（CBF-INC）生成控制信号，引导系统朝向RRT采样的构型运动。CBF-INC以神经网络形式学习，并针对不同输入形式设计了两种变体：状态输入（符号距离）和激光雷达点云输入。针对后者，我们进一步研究了两种场景：环境信息完全观测与部分观测。相较于因过度近似机器人几何形状而导致保守性的人造CBF，CBF-INC能在避免过度保守的同时更优地平衡安全性与目标到达。在基于状态输入的实验中，与原始RRT相比，我们提出的CBF诱导神经控制器增强RRT（CBF-INC-RRT）在困难测试案例中成功率达到14%，同时探索节点数减少30%。对于原始RRT无法直接适用的激光雷达输入场景，我们验证了CBF-INC-RRT相比使用其他引导控制器进行规划，成功率提升10%。项目页面及补充材料请访问 https://mit-realm.github.io/CBF-INC-RRT-website/。