Biologically Inspired Swarm Dynamic Target Tracking and Obstacle Avoidance

TL;DR

This paper introduces a novel AI-driven drone swarm control system with a new predictive model, enabling accurate dynamic target tracking and obstacle avoidance, outperforming traditional methods in robustness and speed.

Contribution

It presents a robust adaptive bidirectional fuzzy brain emotional learning prediction model integrated into a swarm control system for improved dynamic target tracking.

Findings

Enhanced short-term prediction accuracy

Improved long-term tracking stability

Superior robustness compared to traditional methods

Abstract

This study proposes a novel artificial intelligence (AI) driven flight computer, integrating an online free-retraining-prediction model, a swarm control, and an obstacle avoidance strategy, to track dynamic targets using a distributed drone swarm for military applications. To enable dynamic target tracking the swarm requires a trajectory prediction capability to achieve intercept allowing for the tracking of rapid maneuvers and movements while maintaining efficient path planning. Traditional predicative methods such as curve fitting or Long ShortTerm Memory (LSTM) have low robustness and struggle with dynamic target tracking in the short term due to slow convergence of single agent-based trajectory prediction and often require extensive offline training or tuning to be effective. Consequently, this paper introduces a novel robust adaptive bidirectional fuzzy brain emotional learning prediction (BFBEL-P) methodology to address these challenges. The controller integrates a fuzzy interface, a neural network enabling rapid adaption, predictive capability and multi-agent solving enabling multiple solutions to be aggregated to achieve rapid convergence times and high accuracy in both the short and long term. This was verified through the use of numerical simulations seeing complex trajectory being predicted and tracked by a swarm of drones. These simulations show improved adaptability and accuracy to state of the art methods in the short term and strong results over long time domains, enabling accurate swarm target tracking and predictive capability.