Implementing Ant Colony Optimization for Multi-Robot Coordination in Unmanned Ground Robots

Single robots are not enough for multi-objective tasks especially where the task variate and we need complex computations for desired results. Therefore, multiple ground robot coordination is widely used in various domains such as warehouse operations, precision agriculture, surveillance, mapping, and healthcare, etc. The coordination of multiple robots in a complex environment can be made easy with the use of suitable optimization algorithms such as Ant Colony Optimization. However, in this research, multiple ground robot formations using Master-Slave coordination have been presented for shortest path selection. Three identical toy car robots (one master and two slave robots) equipped with sensors are designed and allowed to operate in three different paths to find the optimal (shortest) path. The selection of paths and coordination for master robots with slave robots is made through the GUI Android app. Master robots initially follow all the paths one by one instructed by the operator through the app and find the optimal one by calculating the fitness cost. Ant colony optimization algorithm also verifies the optimal path found by the master robot. The path planning simulations are also performed in MATLAB. A comparative analysis of the proposed algorithm with the Greedy algorithm is also presented for validation of the proposed algorithm in this scenario. In the future, the number of robots can be extended, or the combinatorial optimization algorithms can be implemented to precise optimal solutions.