A Random Walk-Based Stochastic Distributed Exploration Algorithm for Low-Cost Swarm Robots

The advancement of swarm robotics has made tremendous strides in recent years, expanding the scope of its deployment from outdoor settings like construction sites and planetary exploration to indoor scenarios such as transportation-related activities. However, due to the large deployment of units in swarm robots, they cannot be engineered to have the costly and high-performance characteristics of conventional large robots. Thus, it is desirable to maintain low performance per unit and to reduce manufacturing costs. This paper presents a stochastic distributed exploration algorithm that accounts for the above features of swarm robots, and its performance is verified through both simulations and experiments. The proposed algorithm is based on a random walk and avoids path planning and high-precision sensing, enabling it to function well even with low-performance robots, relying only on the distance to the center of the search area based on random walks. Simulation outcomes demonstrate that the proposed algorithm can explore the search area with a specified exploration distribution. The experiments affirm that the robot can sequentially explore multiple search areas.