NFOMP: Neural Field for Optimal Motion Planner of Differential Drive Robots With Nonholonomic Constraints

• Published in: IEEE robotics and automation letters 2022-10, Vol.7 (4), p.10991-10998
• Main Authors: Kurenkov, Mikhail, Potapov, Andrei, Savinykh, Alena, Yudin, Evgeny, Kruzhkov, Evgeny, Karpyshev, Pavel, Tsetserukou, Dzmitry
• Format: Article
• Language: English
• Subjects: Asymptotic methods; Barriers; Collision avoidance; Deep learning methods; Lagrange multiplier; Mobile robots; motion and path planning; Motion planning; nonholonomic motion planning; Optimization; Planning; Robot dynamics; Robotics; Robots; Service robots; Smoothing methods; Trajectory optimization
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2022.3196886

Optimal motion planning is one of the most critical problems in mobile robotics. On the one hand, classical sampling-based methods propose asymptotically optimal solutions to this problem. However, these planners cannot achieve smooth and short trajectories in reasonable calculation time. On the other hand, optimization-based methods are able to generate smooth and plain trajectories in a variety of scenarios, including a dense human crowd. However, modern optimization-based methods use the precomputed signed distance function for collision loss estimation, and it limits the application of these methods for general configuration spaces, including a differential drive non-circular robot with non-holonomic constraints. Moreover, optimization-based methods lack the ability to handle U-shaped or thin obstacles accurately. We propose to improve the optimization methods in two aspects. Firstly, we developed an obstacle neural field model to estimate collision loss; training this model together with trajectory optimization allows improving collision loss continuously, while achieving more feasible and smoother trajectories. Secondly, we forced the trajectory to consider non-holonomic constraints by adding Lagrange multipliers to the trajectory loss function. We applied our method for solving the optimal motion planning problem for differential drive robots with non-holonomic constraints, benchmarked our solution, and proved that the novel planner generates smooth, short, and plain trajectories perfectly suitable for a robot to follow, and outperforms the state-of-the-art approaches by 25% on normalized curvature and by 75% on the number of cusps in the MovingAI environment.