Collaborative gas source localization strategy with networked nano-drones in unknown cluttered environments

• Published in: Swarm and evolutionary computation 2024-08, Vol.89, p.101615, Article 101615
• Main Authors: Tran, Vu Phi, Garratt, Matthew A., Anavatti, Sreenatha G., Ravi, Sridhar
• Format: Article
• Language: English
• Subjects: Adaptive control; Evolutionary algorithms; Flight control system; Fuzzy neural network; Gas source localization; Particle swarm optimization; Quadrotor UAV; Robotic swarms; Self-organizing systems
• ISSN: 2210-6502
• DOI: 10.1016/j.swevo.2024.101615

This paper introduces a novel approach for improving gas source localization in dynamic urban environments, employing a swarm of nano-Crazyflie drones through a hybrid strategy that integrates Adaptive Robotic Particle Swarm Optimization (ARPSO) with Bidirectional Brain Emotional Learning (BBEL). The proposed method refines the ARPSO algorithm into two phases: an initial exploration phase and a subsequent seeking phase. The exploration phase activates when no gas is detected, seamlessly transitioning to the seeking phase upon gas detection. This facilitates efficient information exchange and desired velocity generation within the particle swarm. During the seeking phase, particles conduct measurements, share the global best position, intelligently navigate obstacles, and avoid collisions while directly identifying the global concentration field maximum. Unlike conventional methods, the ARPSO algorithm autonomously adapts its parameters online, mitigating algorithmic failures and local optima challenges. It also considers the limited communication of robots in complex environments, a factor often overlooked in recent methods. To enhance plume tracking robustness, facilitate the controller gain tuning process, and ensure system resilience against environmental disturbances and uncertainties, the BBEL method incorporates a sophisticated fuzzy neural network structure with reinforcement learning-based adaptation mechanisms. The stability of the closed-loop control system is rigorously proven using Lyapunov theory. Numerical simulations in complex urban scenarios validate the algorithm’s effectiveness, showcasing a significant 20% improvement in turnaround time and a flawless 100% success rate with no collisions, in addition to enhanced capabilities in handling uncertainties and disturbances compared to benchmarks like ARPSO-BBEL with unlimited communication range and no delayed communications (ARPSO-BBELU), Sniffy Bug (SB)-BBELU, and ARPSO-PIDU. •Present an adaptive robotic particle swarm method for localizing the gas source.•Propose a flocking model to keep robots closer and collect gas information more precisely.•Add a robust, adaptive BBEL algorithm for tuning controllers and handling swarm uncertainties.•Evaluate our algorithm with multiple obstacles and limited communication resources.•Benchmark our algorithm in a comparative study against other strategies.