Multi-target trajectory planning and control technique for autonomous navigation of multiple robots.

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• Autonomous robots are increasingly vital in defense and intelligent industries due to automation demands.
• Efficient multi-target trajectory optimization and obstacle negotiation are critical challenges in robotic navigation.

Purpose of the Study:

• To develop and implement a novel hybrid algorithm for enhanced autonomous robot navigation.
• To address multi-target trajectory optimization and smooth navigation challenges in complex environments.

Main Methods:

• Hybridization of a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) for controller design.
• Integration of a Petri-Net controller to manage navigational conflicts.
• Validation through WEBOTS and MATLAB simulations, and real-time experiments using the Khepera-II robot.

Main Results:

• The hybrid controller successfully tackled single-robot multi-target, multiple-robot single-target, and multiple-robot multi-target scenarios.
• Significant improvements observed: an average of 34.2% in trajectory optimization and 70.6% in time consumption compared to existing techniques.
• The algorithm demonstrated suitability, precision, and stability in various navigation tasks.

Conclusions:

• The proposed hybrid MFDA-FA controller offers a robust solution for complex autonomous robot navigation tasks.
• The integration with Petri-Net enhances navigational conflict resolution, ensuring reliable operation.
• The validated improvements highlight the technique's potential for advancing automation in defense and intelligent industries.