Distributed Communication-aware Motion Planning for Multi-agent Systems from STL and SpaTeL Specifications

TL;DR

This paper presents a unified control framework for multi-agent systems that ensures safe, coordinated motion while maintaining communication quality, using logic-based specifications encoded as MILP constraints within a distributed MPC approach.

Contribution

It introduces a novel co-design method integrating motion planning and communication quality constraints using STL and SpaTeL specifications in a distributed control framework.

Findings

Successfully encodes motion and communication specifications as MILP constraints.

Demonstrates effective distributed motion planning satisfying complex specifications.

Validates the approach through simulation of multi-agent systems with communication considerations.

Abstract

In future intelligent transportation systems, networked vehicles coordinate with each other to achieve safe operations based on an assumption that communications among vehicles and infrastructure are reliable. Traditional methods usually deal with the design of control systems and communication networks in a separated manner. However, control and communication systems are tightly coupled as the motions of vehicles will affect the overall communication quality. Hence, we are motivated to study the co-design of both control and communication systems. In particular, we propose a control theoretical framework for distributed motion planning for multi-agent systems which satisfies complex and high-level spatial and temporal specifications while accounting for communication quality at the same time. Towards this end, desired motion specifications and communication performances are formulated as signal temporal logic (STL) and spatial-temporal logic (SpaTeL) formulas, respectively. The specifications are encoded as constraints on system and environment state variables of mixed integer linear programs (MILP), and upon which control strategies satisfying both STL and SpaTeL specifications are generated for each agent by employing a distributed model predictive control (MPC) framework. Effectiveness of the proposed framework is validated by a simulation of distributed communication-aware motion planning for multi-agent systems.