Angle-Encoded Swarm Optimization for UAV Formation Path Planning

Publication Type:
Conference Proceeding
Citation:
IEEE International Conference on Intelligent Robots and Systems, 2018, pp. 5239 - 5244
Issue Date:
2018-12-27
Filename Description Size
IROS18_2180_FI.pdfAccepted Manuscript version1.91 MB
Adobe PDF
Full metadata record
© 2018 IEEE. This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (DAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of DAVs 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 (θ- 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 (loT) for multi-directional transmission and reception of data between the DAV s, 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.
Please use this identifier to cite or link to this item: