Angle-Encoded Swarm Optimization for UAV Formation Path Planning

Abstract

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.

In Proceedings of The 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), pp. 5239-5244

References in corpus (3)

• Survey of Important Issues in UAV Communication Networks
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• Adaptive twisting sliding mode control for quadrotor unmanned aerial vehicles

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• Radial Basis Function Neural Networks for Formation Control of Unmanned Aerial Vehicles
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• CoDe: A Cooperative and Decentralized Collision Avoidance Algorithm for Small-Scale UAV Swarms Considering Energy Efficiency