5 accepted papers
Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and
The scene flow estimation aims at accurately achieving the motion of 3D points, imposing challenges like mis-registration, object occlusions, and non-uniform upsampling. This paper introduces a scene flow estimation framework featuring a unified scene flow estimator, a symmetric cost volume approach
Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this letter, we prese
The major challenges of collision avoidance for robot navigation in crowded scenes lie in accurate environment modeling, fast perceptions, and trustworthy motion planning policies. This paper presents a novel adaptive environment model based collision avoidance reinforcement learning (i.e., AEMCARL)…
The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (R