Clustered Multi-Target Search in Unknown Large Environments Using Modified Bee Swarm Optimization

Abstract

Swarm robotics (SR) is an emerging field of research that utilizes a swarm of robots that work together as a team to solve complex problems. One such challenge in SR is the exploration of unknown environments to find multiple targets. This problem finds applications in various domains, such as archaeology, underground exploration, signal source localization, and more. In line with this, the objective of this thesis is to develop a scalable SR algorithm for efficiently finding clustered targets in large unknown environments. Similar to how artefacts in archaeology are often found clustered around old settlements in large areas.

Inspired by Bee Swarm Optimization (BSO), the proposed algorithm leverages the strengths of BSO in balancing exploration and exploitation. However, modifications are made to adapt the proposed algorithm to incorporate limited communication range scenarios, common in large-scale environments. Also, the exploration-exploitation properties of BSO are redesigned. The proposed algorithm is named Modified Bee Swarm Optimization (MBSO). Here, robots assume different roles (scout, onlooker, experienced forager), similar to BSO, to optimize search and exploitation tasks. To address the issue of limited communication range, the robots establish an ad hoc network, truncating target information throughout the swarm. Additionally, an Artificial Potential Field (APF) is introduced to guide robots towards targets, and away from readily travelled clusters. To further aid the balancing of exploration and exploitation, a swarming architecture is introduced. This architecture is called the Architecture Multi-robot systems heterogeneous robots with Emergent Behaviour (AMEB) and aids with the decision-making of individual robots. The AMEB architecture is used to determine whether scouts should become onlookers, and the speed at which experienced foragers change back to scouts while considering real-robot physical limitations and individual performance levels. This architecture facilitates the continued advancement of the algorithm by allowing for the integration of additional sensory inputs, which in turn influences individual decision-making and, consequently, the emergence of the swarm. Lastly, cluster recognition is added to the algorithm, resulting in robots not transferring to readily travelled areas.

The characteristics of MBSO are evaluated. The target finding performance is benchmarked against a generic random walk method that stems from Lévy walking. Furthermore, this study investigates the effects of scaling the algorithm with an increasing number of robots and varying specific control parameters of MBSO on various aspects such as performance, redundancy, scalability, stability, and robustness. The results of this research have implications beyond archaeology, as the algorithm can be applied to various multi-target search problems in large unknown environments, such as minefield detection. By adjusting the proposed input variables, the algorithm can be optimized for these different scenarios. The developed SR algorithm shows promise in efficiently finding and truncating target information, leveraging the short communication range of the individual robots.

Overall, this thesis presents a novel approach to autonomous multi-target searching, in cases where targets are spread out in clusters, using scalable robotic swarming algorithms. In a field of 0.36 ha with 34 targets spread out over 5 clusters, robots that transfer with a speed of 6.4 km h−1 and optimal parameters for MBSO, scaling from 10 to 15 robots leads to 7.74 % more targets being found. Scaling from 15 to 20 robots resulted in 13.22 % more found targets...