Angle-Encoded Swarm Optimization for UAV Formation Path Planning
This addresses formation path planning for UAVs in infrastructure inspection, but it is incremental as it builds on existing PSO methods with angle encoding.
The paper tackled path planning for UAV formations during infrastructure inspection by proposing a theta-PSO algorithm to minimize travel distance while avoiding obstacles and maintaining formation shape, with experiments on 3DR Solo drones confirming feasibility and effectiveness.
This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (UAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of UAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angle-encoded Particle Swarm Optimization (theta-PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of-Things (IoT) for multi-directional transmission and reception of data between the UAVs, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach.