Neural Potential Field for Obstacle-Aware Local Motion Planning
Muhammad Alhaddad Konstantin Mironov Aleksey Staroverov Aleksandr Panov
Model predictive control (MPC) may provide local motion planning for mobile
robotic platforms. The challenging aspect is the analytic representation of
collision cost for the case when both the obstacle map and robot footprint are
arbitrary. We propose a Neural Potential Field: a neural network model that
returns a differentiable collision cost based on robot pose, obstacle map, and
robot footprint. The differentiability of our model allows its usage within the
MPC solver. It is computationally hard to solve problems with a very high
number of parameters. Therefore, our architecture includes neural image
encoders, which transform obstacle maps and robot footprints into embeddings,
which reduce problem dimensionality by two orders of magnitude. The reference
data for network training are generated based on algorithmic calculation of a
signed distance function. Comparative experiments showed that the proposed
approach is comparable with existing local planners: it provides trajectories
with outperforming smoothness, comparable path length, and safe distance from
obstacles. Experiment on Husky UGV mobile robot showed that our approach allows
real-time and safe local planning. The code for our approach is presented at
https://github.com/cog-isa/NPField together with demo video.