Formation Flight and Collision Avoidance for Multiple UAVs using Concept of Elastic Weighting Factor

Abstract

In this paper, the guidance law for formation flight and collision avoidance of multiple Unmanned Aerial Vehicle (UAV)s is proposed. To construct the physically comprehensible guidance law for formation flight, the virtual structure approach is used. To develop a guidance law for collision avoidance considering both other UAVs and unknown static obstacles, a geometric approach using information such as a relative position vector is utilized. Through the Lyapunov theorem, the stability of the proposed guidance law is proved. To combine guidance commands, the concept of the elastic weighting factor inspired by the elastic behavior of shape memory polymer, which tends to regain its original shape after deformation, is introduced. By using the concept of elastic weighting factor, multiple UAVs are able to cope actively with the situation of a collision between both UAVs and static obstacles during the formation flight. To verify the performance of the proposed method, numerical simulations are performed.

Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

“…Given the problem of formation rendezvous for N UAVs, suppose the starting pose (position and direction) at initial location of UAV i is given as p si = (x si , y si , z si , χ si , γ si ) and the formation pose p f = (x f , y f , z f , χ f , γ f ) at the rendezvous point is also known beforehand, where (x, y, z) is the location of UAV or formation and (χ, γ) are the horizontal and vertical angles, respectively. The formation configuration at the rendezvous point is defined with the virtual structure approach [25] by a series of relative coordinates (x fi , y fi , z fi ) i = 1, . .…”

“… are the horizontal and vertical angles, respectively. The formation configuration at the rendezvous point is defined with the virtual structure approach [25] by a series of relative coordinates…”

Formation control of aerial robots using virtual structure and new fuzzy-based self-tuning synchronization

“…There are three main information structure approaches for the formation control problem, namely the leader-follower (Alexis et al, 2014;Lin et al, 2013), virtual structure (Alexis et al, 2014;Kahn et al, 2013;Kang et al, 2013;Zhang and Liu, 2013) and behavioural approach (Cai et al, 2012;Kuppan Chetty et al, 2012;Seungkeun and Youdan, 2007).…”