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 Angleencoded 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 (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.

Reconfigurable Multi-UAV Formation Using Angle-Encoded PSO

“…This path will be further adjusted to include the reconfiguration for the formation as presenting in next sections. As we aim to create an optimal path with quick time convergence, the angle-coded PSO (θ-PSO) is used [26]. In θ-PSO, the location of particles is encoded by the angle of the formation centroid and their movements are described as:…”

“…The task designated in our experiments is to inspect different surfaces of a light rail bridge employing three identical UAVs. The UAVs used are the 3DR Solo drones retrofitted with inspection cameras and communication boards [26]. The onboard low-level controllers for these drones have been addressed in [27].…”

“…24 Generate the individual path for UAV n . / * Path generation: * / 25 foreach UAV n do 26 foreach j = 1 to M j do 27 Select a relevant formation shape;…”

“…The main contribution of this work is an augmentation of the proposed optimisation algorithm for path planning [26] with additional constraints based on intermediate waypoints to satisfy the requirements for safety and task efficiency given a number of formation types that can be reconfigurable during operations. The new formation path planning technique can therefore (i) generate safer flights of the UAV group, and (ii) improve the formation flexibility as well as its capacity for inspection tasks.…”

“…i=1 P n ,(6)and the radius is given by r F = max(d n ), while the rotation matrix, R IF , relating the formation and inertial frames is given in[26].…”