This paper presents multimodal function optimization, using a nature-inspired glowworm swarm optimization (GSO) algorithm, with applications to collective robotics. GSO is similar to ACO and PSO but with important differences. A key feature of the al

In this article, several basic swarming laws for Unmanned Aerial Vehicles (UAVs) are developed for both two-dimensional (2D) plane and three-dimensional (3D) space. Effects of these basic laws on the group behaviour of swarms of UAVs are studied. It is shown that when cohesion rule is applied an equilibrium condition is reached in which all the UAVs settle at the same altitude on a circle of constant radius. It is also proved analytically that this equilibrium condition is stable for all values of velocity and acceleration. A decentralised autonomous decision-making approach that achieves collision avoidance without any central authority is also proposed in this article. Algorithms are developed with the help of these swarming laws for two types of collision avoidance, Group-wise and Individual, in 2D plane and 3D space. Effect of various parameters are studied on both types of collision avoidance schemes through extensive simulations.

Control barrier functions have been widely studied and applied to safety-critical systems, including multi-agent obstacle avoidance problems. In this work, we apply control barrier functions to a collaborative transportation problem involving two unmanned aerial vehicles (UAVs) moving a payload around obstacles as they deliver it to a target location. We develop a target-tracking controller for the UAVs, which is constrained to meet the requirements of payload dynamics and obstacle avoidance. We also present simulation results to demonstrate the benefits of the proposed problem formulation for a multi-obstacle environment.