The Cost of Reality: Effects of Real-World Factors on Multi-Robot Search

Abstract

Designing algorithms for multi-robot systems can be a complex and difficult process: the cost of such systems can be very high, collecting experimental data can be time-consuming, and individual robots may malfunction, invalidating experiments. These constraints make it very tempting to work using high-level abstractions of the robots and their environment. While these high-level models can be useful for initial design, it is important to verify techniques in more realistic scenarios that include real-world effects that may have been ignored in the abstractions. In this paper, we take a simple, coordinated, multi-robot search algorithm and illustrate problems that it encounters in environments which incorporate real-world factors, such as probabilistic target detection and positional noise. We compare the performance to that of several simple randomized approaches, which are better able to deal with these constraints.