MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• Multi-robot systems (MRS) present complex path planning and tracking challenges.
• Real-world applications require robust solutions that handle dynamic environments and potential robot failures.

Purpose of the Study:

• To propose a novel multi-robot path planning and tracking model (MPPTM) for online operation.
• To address critical issues like collision avoidance and robot failure within MRS.

Main Methods:

• The MPPTM integrates a neural dynamic path planner, a hyperbolic tangent path optimizer, and an error-driven path tracker.
• An Ultra-wideband positioning system assists in achieving accurate tracking in practical settings.

Main Results:

• The proposed path planner demonstrates good time performance.
• The path optimizer enhances tracking accuracy for the multi-robot system.
• Real experiments and simulations validate the model's effectiveness and superior performance.

Conclusions:

• The MPPTM offers a low-cost, high-accuracy solution for online path planning and tracking in multi-robot systems.
• The model's components work synergistically to improve overall MRS navigation capabilities.