To address the issue of obstacle avoidance during formation flight by Unmanned Aerial Vehicles (UAVs), a novel method that utilizes enhanced artificial potentials combined with virtual leaders has been introduced. Initially, the method generates dynamic target points for formation based on the predetermined flight formation and virtual leader positions. Subsequently, it devises suitable gravitational and repulsive functions, which include the gravitational pull towards the desired target points, the repulsive forces among formation UAVs, and the repulsive forces from obstacles onto the UAVs. These functions are designed to enable the UAVs to swiftly maneuver away from obstacle-affected regions while concurrently overcoming the limitations of the traditional artificial potential field (APF) approach, such as target inaccessibility and local minima issues. Experimental outcomes corroborate that the proposed formation obstacle avoidance approach successfully guides UAVs to navigate around pre-positioned obstacles while preserving formation integrity. Furthermore, the UAVs are capable of reconstituting their formation and executing formation shifts once the obstacles have been passed, thereby affirming the practicality and effectiveness of the method.

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UAVs Formation Obstacle Avoidance Method Based on Improved Artificial Potentials and Virtual Leaders

  • Jiayun Wen,
  • Qiang Tang,
  • Xianglun Zhang,
  • Hao Li,
  • Yukun Yang,
  • Shichao Ma,
  • Zhiyu Li

摘要

To address the issue of obstacle avoidance during formation flight by Unmanned Aerial Vehicles (UAVs), a novel method that utilizes enhanced artificial potentials combined with virtual leaders has been introduced. Initially, the method generates dynamic target points for formation based on the predetermined flight formation and virtual leader positions. Subsequently, it devises suitable gravitational and repulsive functions, which include the gravitational pull towards the desired target points, the repulsive forces among formation UAVs, and the repulsive forces from obstacles onto the UAVs. These functions are designed to enable the UAVs to swiftly maneuver away from obstacle-affected regions while concurrently overcoming the limitations of the traditional artificial potential field (APF) approach, such as target inaccessibility and local minima issues. Experimental outcomes corroborate that the proposed formation obstacle avoidance approach successfully guides UAVs to navigate around pre-positioned obstacles while preserving formation integrity. Furthermore, the UAVs are capable of reconstituting their formation and executing formation shifts once the obstacles have been passed, thereby affirming the practicality and effectiveness of the method.