Swarm cohesion is a prime contributor to the success of swarm guidance. However, a limited sensing range of the agents could lead to fragmentation of the swarm, which cascades to mission failure. Shepherding is a swarm guidance approach that relies on a finite number of agents to control a proportionally larger number of swarm members. We propose a connectivity-aware shepherding mechanism for improving the cohesion of the swarm in the presence of sensing constraints. A graph-based model of the flock is integrated with a particle swarm optimisation algorithm and a data clustering algorithm to identify the most appropriate herding point for influencing the swarm to move and to guide them towards the goal. The path to this herding point is optimised by particle swarm optimisation. The proposed methodology is evaluated with multiple swarm configurations. A comparison between the proposed methodology and classic shepherding demonstrates the superior performance of the former, which is demonstrated by a reduction of task completion time to half and a doubling in mission success rate.

This paper investigates the usage of machine learning algorithms, applied to the task of position estimation in visible light positioning systems. Traditional approaches relying in trilateration usually resort to the application of the least squares method to find the position estimate. The least squares method is very prone to outlier information present in the data set, which reduces the estimation accuracy. This paper presents a strategy based on clustering and outlier removal able to improve the estimation accuracy. Clustering is based on DBSCAN, an algorithm used to find structure in unstructured data. The tuning parameters for DBSCAN are optimized following a linear regression supervised learning step, where a set of training examples with known real position is used. Simulation results show a 35% gain improvement in accuracy achieved with a moderate complexity increase. The minimum estimation error for the case scenario under study was 0.2 mm, with an r.m.s. error of 35 mm.

Within applications of swarm control, failing to maintain cohesion amongst the agents may lead to mission failure. We study the effect of limited sensing range of swarming agents when guided by a shepherd. A connectivity-aware approach is proposed to enhance the cohesion of the swarm. We combine a graph-based model of the flock with particle swarm optimization (assisted by DBSCAN) to improve the shepherd's performance in herding sheep (swarm members). The approach is evaluated using multiple initial swarm configurations. Simulation results on swarm sizes of 50 and 100 agents show up to 50% reduction in the task completion time and an average improvement of 25% in the success rate for low density sheep initialization scenarios and competitive results for the higher density scenarios.