Multi-robot exploration is a complex problem that involves multiple robots working in a shared unknown environment. In such scenarios, the safety of the robots is of paramount importance alongside the completion of the exploration task. In this paper, we propose a modular exploration framework that (i) identifies safe frontier targets for multiple robots while taking into account the system dynamics of each robot to ensure collision avoidance with previously unknown obstacles and (ii) ensures that the robots reach their exploration targets while avoiding any obstacles discovered and each other. We employ a scalable approach to generate symbolic controllers for the multi-robot system, utilizing distance functions. We also provide formal guarantees on the safety of the exploration targets and the completion of each exploration run, with the robots avoiding collisions with each other and the obstacles. We test our approach on simulation experiments and a real-world implementation to validate it.

Autonomous exploration in an unknown environment is widely studied, and many exploration strategies exist. However, in most of the works, safety is not usually given top priority. The reason behind the violation of safety by most of the exploration algorithms in real-world applications is the ignorance of some or all of the following factors (i) the mathematical model of the robot, (ii) practical constraints on states (like constrained steering angle, speed, etc.) and inputs (like actuator saturation), and (iii) hardware constraints such as sampling time, sensor noise, modelling uncertainties, etc. In this work, we propose an autonomous exploration framework for the 2-D exploration problem that considers the factors above to provide safety guarantees for the robot and the environment. The effectiveness of this method is shown in a high-fidelity simulation of different robots with onboard sensors in different simulation environments and real-world implementation.