Toward a Generic Framework for Mission Planning and Execution with a Heterogeneous Multi-Robot System.

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Science

Background:

• Coordinating heterogeneous robots (unmanned ground vehicles and unmanned aerial vehicles) in dynamic environments presents significant challenges.
• Existing systems often lack scalability and adaptability for complex, unstructured mission scenarios.

Purpose of the Study:

• To develop a comprehensive framework for mission planning and execution in heterogeneous multi-robot systems.
• To enhance coordination, task allocation, and resource optimization for unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs).

Main Methods:

• A decentralized control strategy for adaptive mission execution in 2D and 3D spaces.
• Implementation of a robust task allocation algorithm and dynamic formation control.
• Utilizing the ROS 2 communication protocol for reliable inter-robot information exchange.

Main Results:

• Demonstrated effective coordinated exploration and data collection through a case study.
• Showcased the framework's ability to manage missions while optimizing robot collaboration.
• Validated the system's scalability and maintainability due to its loosely coupled architecture.

Conclusions:

• The proposed framework provides a scalable and adaptable solution for multi-robot coordination in challenging environments.
• This work advances the field of heterogeneous robotic systems by enabling robust mission planning and execution.
• The framework facilitates efficient collaboration between UGVs and UAVs for complex tasks.