Multi-Agent Motion Planning From Signal Temporal Logic Specifications

Dawei Sun, Jingkai Chen, Sayan Mitra, Chuchu Fan

Abstract:

We tackle the challenging problem of multi-agent
cooperative motion planning for complex tasks described using
signal temporal logic (STL), where robots can have nonlinear and
nonholonomic dynamics. Existing methods in multi-agent motion
planning, especially those based on discrete abstractions and model
predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the
planning horizon. We present a method based on timed waypoints
to address this issue. We show that timed waypoints can help abstract nonlinear behaviors of the system as safety envelopes around
the reference path defined by those waypoints. Then the search
for waypoints satisfying the STL specifications can be inductively
encoded as a mixed-integer linear program. The agents following
the synthesized timed waypoints have their tasks automatically
allocated, and are guaranteed to satisfy the STL specifications
while avoiding collisions. We evaluate the algorithm on a wide
variety of benchmarks. Results show that it supports multi-agent
planning from complex specification over long planning horizons,
and significantly outperforms state-of-the-art abstraction-based
and MPC-based motion planning methods. The implementation
is available at https://github.com/sundw2014/STLPlanning.