Neural Trajectory Model: Implicit Neural Trajectory Representation for Trajectories Generation

Yuqing Tang

Abstract:

Trajectory planning is a fundamental problem in
robotics. It facilitates a wide range of applications in navigation
and motion planning, control, and multi-agent coordination.
Trajectory planning is a difficult problem due to its computational complexity and real-world environment complexity
with uncertainty, non-linearity, and real-time requirements.
The multi-agent trajectory planning problem adds another
dimension of difficulty due to inter-agent interaction. Existing
solutions are either search-based or optimization-based approaches with simplified assumptions of environment, limited
planning speed, and limited scalability in the number of agents.
In this work, we make the first attempt to reformulate single
agent and multi-agent trajectory planning problem as query
problems over an implicit neural representation of trajectories.
We formulate such implicit representation as Neural Trajectory
Models (NTM) which can be queried to generate nearly optimal
trajectory in complex environments. We conduct experiments in
simulation environments and demonstrate that NTM can solve
single-agent and multi-agent trajectory planning problems. In
the experiments, NTMs achieve (1) sub-millisecond panning
time using GPUs, (2) almost avoiding all environment collision,
(3) almost avoiding all inter-agent collision, and (4) generating
almost shortest paths. We also demonstrate that the same NTM
framework can also be used for trajectories correction and
multi-trajectory conflict resolution refining low quality and
conflicting multi-agent trajectories into nearly optimal solutions efficiently. (Open source code is available at https://
github.com/laser2099/neural-trajectory-model)