Project title: NFOMP: Neural Field for Optimal Motion Planner of Differential Drive Robots with Nonholonomic Constraints

Description:
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. 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 apply 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. 

Candidates requirements:
➔ basic programming skills in Python or ROS / Gazebo

Supervisor: Associate Professor Dzmitry Tsetserukou

Internship duration: 1-2 months

The start date of the internship: July 12

End day: August 31

Contact person: Dzmitry Tsetserukou d.tsetserukou@skoltech.ru