# Angle-Encoded Swarm Optimization for UAV Formation Path Planning

## TL;DR

This paper introduces a new angle-encoded particle swarm optimization method for UAV formation path planning, effectively minimizing travel distance while avoiding obstacles and maintaining formation shape during surface inspections.

## Contribution

The paper proposes the theta-PSO algorithm that accelerates convergence using angular velocity and position, and models UAV formation as a virtual rigid body for shape maintenance.

## Key findings

- Successful triangular formation maintenance along a bridge
- Effective obstacle avoidance and path optimization
- Feasibility confirmed through extensive drone experiments

## 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.