Efficient Motion Planning for Manipulators with Control Barrier
  Function-Induced Neural Controller
Mingxin Yu Chenning Yu M-Mahdi Naddaf-Sh Devesh Upadhyay Sicun Gao Chuchu Fan
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/.