Multi-Robot Exploration and Control System

Multi-robot collaborative exploration represents a cutting-edge research direction in the field of intelligent control. For exploration missions in complex environments, we have designed a MATLAB-based control system framework that incorporates several key technical components:

The system first implements environmental modeling and map construction using a grid-based approach. The environment undergoes discretization processing, with real-time sensor data continuously updating map information to support subsequent path planning operations. This methodology offers high computational efficiency, making it particularly suitable for real-time requirements in multi-robot systems. In MATLAB implementation, this typically involves creating occupancy grid maps using functions like robotics.OccupancyGrid and updating them with sensor readings through binary Bayes filters.

For path planning, we integrated an enhanced A* algorithm with dynamic obstacle avoidance strategies. Each robot autonomously computes optimal exploration paths based on global maps and real-time perception data, while an information-sharing mechanism prevents redundant exploration of covered areas. The distributed decision-making architecture significantly enhances system scalability and robustness. Code implementation involves creating custom heuristic functions for the A* algorithm and implementing neighbor communication protocols for data sharing between robots.

Multi-robot coordination stands as the system's core advantage. Through a dynamic task allocation algorithm based on auction mechanisms, the robot swarm can autonomously adjust exploration strategies according to environmental changes and mission requirements. The system also implements gradient field-based area coverage algorithms, ensuring comprehensive and efficient exploration without omissions. The auction mechanism can be coded using bid calculation functions and winner determination protocols, while gradient fields are typically generated using potential field methods with MATLAB's gradient function.

Finally, the system includes comprehensive monitoring and exception handling modules. A real-time visualization interface enables intuitive observation of each robot's status and exploration progress. When communication interruptions or hardware failures occur, the system automatically triggers contingency plans and reallocates tasks. This solution has demonstrated excellent stability and adaptability in practical testing, providing reliable technical support for complex environment exploration. The monitoring module can be implemented using MATLAB's App Designer for GUI creation and event-driven exception handling functions.