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Adaptation Strategy for a Distributed Autonomous UAV Formation in Case of Aircraft Loss

Tagir Muslimov

TL;DR

Problem: fully decentralized UAV formations experience altered dynamics and potential speed deviations when a UAV is lost. Approach: the authors propose an adaptive reconfiguration that tunes the formation pattern $E$ by minimizing an interaction energy $E = 1/2 sum_i (p_i - p_{di})^2$ and applying a sigmoid-based update to $p_{di}$, operating on a slower time scale than the formation dynamics. Findings: simulation on full nonlinear UAV models of circular target tracking demonstrates that the previously observed cruising-speed increase is eliminated and the system reaches a new equilibrium; in tests with parameter $\tau_p = 0.1$, the fleet regains the preconfigured speed. Significance: this fault-tolerant, decentralized adaptation enables robust target-tracking formations and can be extended to broader decentralized control scenarios, with future work including diagnostics modules.

Abstract

Controlling a distributed autonomous unmanned aerial vehicle (UAV) formation is usually considered in the context of recovering the connectivity graph should a single UAV agent be lost. At the same time, little focus is made on how such loss affects the dynamics of the formation as a system. To compensate for the negative effects, we propose an adaptation algorithm that reduces the increasing interaction between the UAV agents that remain in the formation. This algorithm enables the autonomous system to adjust to the new equilibrium state. The algorithm has been tested by computer simulation on full nonlinear UAV models. Simulation results prove the negative effect (the increased final cruising speed of the formation) to be completely eliminated.

Adaptation Strategy for a Distributed Autonomous UAV Formation in Case of Aircraft Loss

TL;DR

Problem: fully decentralized UAV formations experience altered dynamics and potential speed deviations when a UAV is lost. Approach: the authors propose an adaptive reconfiguration that tunes the formation pattern by minimizing an interaction energy and applying a sigmoid-based update to , operating on a slower time scale than the formation dynamics. Findings: simulation on full nonlinear UAV models of circular target tracking demonstrates that the previously observed cruising-speed increase is eliminated and the system reaches a new equilibrium; in tests with parameter , the fleet regains the preconfigured speed. Significance: this fault-tolerant, decentralized adaptation enables robust target-tracking formations and can be extended to broader decentralized control scenarios, with future work including diagnostics modules.

Abstract

Controlling a distributed autonomous unmanned aerial vehicle (UAV) formation is usually considered in the context of recovering the connectivity graph should a single UAV agent be lost. At the same time, little focus is made on how such loss affects the dynamics of the formation as a system. To compensate for the negative effects, we propose an adaptation algorithm that reduces the increasing interaction between the UAV agents that remain in the formation. This algorithm enables the autonomous system to adjust to the new equilibrium state. The algorithm has been tested by computer simulation on full nonlinear UAV models. Simulation results prove the negative effect (the increased final cruising speed of the formation) to be completely eliminated.
Paper Structure (6 sections, 3 theorems, 11 equations, 5 figures)

This paper contains 6 sections, 3 theorems, 11 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Adaptation Algorithm for a Decentralized UAV Formation
  3. Distributed Autonomous System Model
  4. Adaptation Strategy for a Distributed Autonomous System
  5. Simulation Results and Discussion
  6. Conclusions

Key Result

theorem 1

(Yuasa and Ito [19]). Dynamics of ${\bf{P}}$ in the equation ${\dot{\bf P}} = {\bf{L}} \dot {\bf Q}$=${\bf{LF}}\left( {\bf{Q}} \right)$ can be described as an autonomous system if and only if ${\bf{F}}$ in the equation $\dot {\bf Q} = {\bf{F}}\left( {\bf{Q}} \right)$ satisfies the following conditi

Figures (5)

  • Figure 1: Losing one UAV in a formation due to failure
  • Figure 2: Reconfiguring a decentralized system by means of an adaptation algorithm. Figure partly adapted from [18]
  • Figure 3: (a) 4-UAV formation trajectories; (b) relative phase errors in a 4-UAV formation
  • Figure 4: (a) Phase shift errors in the resulting 3-UAV formation after losing 3rd UAV; (b) UAV speeds in the resulting 3-UAV formation after losing 3rd UAV
  • Figure 5: (a) Adaptation algorithm performance in case of adaptively reconfiguring a UAV formation after losing a single UAV; (b) changes in phase shift during post-loss adaptive reconfiguration

Theorems & Definitions (3)

  • theorem 1
  • theorem 2
  • theorem 3