Receding Horizon Control Using Graph Search for Multi-Agent Trajectory Planning

It is hard to find the global optimum to general nonlinear, nonconvex
optimization problems in reasonable time.
This paper presents a method to transfer the receding horizon control
approach, where nonlinear, nonconvex optimization problems are
considered, into graph-search problems. Specifically, systems with
symmetries are considered to transfer system dynamics into a finite
state automaton. In contrast to traditional graph-search approaches
where the search continues until the goal vertex is found, the transfer
of a receding horizon control approach to graph-search problems
presented in this paper allows to solve them in real-time. We proof
that the solutions are recursively feasible by restricting the graph
search to end in accepting states of the underlying finite state
automaton.
The approach is applied to trajectory planning for multiple networked
and autonomous vehicles.
We evaluate its effectiveness in simulation as well as in experiments
in the Cyber-Physical Mobility Lab, an open source platform for
networked and autonomous vehicles. We show real-time capable trajectory
planning with collision avoidance in experiments on off-the-shelf
hardware and code in MATLAB for two vehicles.