Sophie Taboada

Multi-Agent Motion Planning with Signal Temporal Logic Constraints

Abstract

Motion planning algorithms allow us to define a sequence of configurations to guide robots from a starting point to an ending goal while considering the environment's and the robot's constraints. As all robots and circumstances are different, motion planning can be adapted to fit the system's specifications and the user's preferences. Temporal Logic (TL) has been used to enable the implementation of more complex missions. In this work, we are interested in using TL to establish the relation between robots in a multi-robot system, as well as their affiliation with features of the workspace. Signal Temporal Logic (STL) is used to guide motion planning into respecting certain preferences linked to the robot's motion behaviour. To achieve this, RRT* sampling-based algorithm is used to study the free space and to identify the best paths through the cost analysis of all available paths. RRT* is adapted to fit for multi-robot systems and to allow the simultaneous planning of trajectories for several robots. The robustness metric of STL is used to quantify the respect of paths for STL formulas and to influence the cost function of RRT*. The impact on the cost function results in the selection of trajectories with better respect for the STL formulas.

The proposed multi-agent motion planning is tested in simulations with environments containing multiple obstacles and robots. To demonstrate the impact STL has on motion planning, a comparison is made between the trajectories and performances extracted with and without the use of STL in simulations with specific scenarios. Finally, we conduct some hardware experiments up until four robots and present different ways the developed motion planning can be implemented in real life.