A Multi-UAV Formation Obstacle Avoidance Method Combined Improved Simulated Annealing and Adaptive Artificial Potential Field

2025-04-15

AI Summary

Traditional artificial potential field (APF) methods suffer from attraction mismatch—excessive long-range attraction causing collisions and insufficient short-range attraction hindering convergence—as well as susceptibility to local minima in multi-UAV formation obstacle avoidance. To address these issues, this paper proposes DSA-AAPF: a Directional-Steering Annealing APF algorithm integrating directional-steering simulated annealing (to escape local minima induced by semi-enclosed obstacles) with an adaptive APF featuring dynamic gravitational gain tuning and a smoothed resultant force model, implemented within a leader-follower distributed architecture. The key innovations are the first-ever directional-steering escape mechanism and an adaptive gravitational regulation strategy. Simulation results demonstrate that DSA-AAPF significantly improves obstacle avoidance success rate, trajectory smoothness, and robustness in complex environments, outperforming both conventional APF and standard SA-APF across formation reconfiguration, obstacle penetration, and local-minimum escape tasks.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The traditional Artificial Potential Field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. Furthermore, APF is highly susceptible to local minima, compromising motion reliability in complex environments. To address these challenges, this paper presents a novel hybrid obstacle avoidance algorithm-Deflected Simulated Annealing-Adaptive Artificial Potential Field (DSA-AAPF)-which combines an improved simulated annealing mechanism with an enhanced APF model. The proposed approach integrates a Leader-Follower distributed formation strategy with the APF framework, where the resultant force formulation is redefined to smooth UAV trajectories. An adaptive gravitational gain function is introduced to dynamically adjust UAV velocity based on environmental context, and a fast-converging controller ensures accurate and efficient convergence to the target. Moreover, a directional deflection mechanism is embedded within the simulated annealing process, enabling UAVs to escape local minima caused by semi-enclosed obstacles through continuous rotational motion. The simulation results, covering formation reconfiguration, complex obstacle avoidance, and entrapment escape, demonstrate the feasibility, robustness, and superiority of the proposed DSA-AAPF algorithm.

Problem

• Improves UAV obstacle avoidance in APF force distribution
• Solves local minima issue in complex environments
• Enhances target convergence with adaptive velocity control

Innovation

• Combines improved simulated annealing with adaptive APF
• Uses Leader-Follower formation for smoother trajectories
• Introduces directional deflection to escape local minima