Designing resource-collection algorithms for relatively simple robots that are effective given the noise and uncertainty of the real world is a challenge in swarm robotics. This paper describes the performance of two algorithms for collective robot foraging: the stochastic central-place foraging algorithm (CPFA) and the distributed deterministic spiral algorithm (DDSA). With the CPFA, robots mimic the foraging behaviors of ants; they stochastically search for targets and share information to recruit other robots to locations where they detect multiple targets. With the DDSA, robots travel along pre-planned spiral paths; robots detect the nearest targets first and, in theory, guarantee eventual complete coverage of the arena with minimal overlap. We implemented both algorithms and compared their performance in a Gazebo simulation and in physical robots in a large outdoor arena. In a realistic Gazebo simulation, the DDSA outperforms the CPFA. However, in real-world experiments with obstacles, collisions, and errors, the movement patterns of robots implementing the DDSA become visually indistinguishable from the CPFA. The CPFA is less affected by noise and error, and it performs as well as, or better than, the DDSA. Physical experiments change our conclusion about which algorithm has the best performance, emphasizing the importance of systematically comparing the performance of swarm robotic algorithms in the real world.

As robot swarms become more viable, efficient solutions to fundamental tasks such as swarm search and collection are required. We propose the distributed deterministic spiral algorithm (DDSA) which generalises a spiral search pattern to robot swarms. While being an effective search strategy in its own right, the DDSA is also a useful point of comparison for other swarm search strategies. Such a benchmark for robot swarm search is currently needed but missing. As a case study, we compare the DDSA to a biologically-inspired central-place foraging algorithm that uses stochastic search, memory, and communication to efficiently collect resources in a variety of different resource distributions.