Multi Target Task Distribution and Path Planning for Multi-Agents

In the field of robotics, there are basic subjects such as control design, machine vision, path planning, performing assigned tasks. It is widely focused on one robot systems in literature. There are also studies on multiple robots and multiple target / task sharing. In this study, a task sharing system for navigating multiple targets with multiple robots and a navigation algorithm for finding the appropriate route has been investigated. This study is similar with respect to the problem of Multi-Traveling Salesman Problem (M-TSP). In task sharing system, task balancing is made according to passive or active states. In load balancing, the goal is to avoid overloading a robot. After assignment of tasks to the relevant robots, the target cluster appears as many as the number of robots. For each set, the robot position and the available targets are considered as one of the graph nodes. The distance matrix is created by making these formed nodes as fully connected. Then, the path plan is made based on the proximity cost to the target nodes from the initial position of the robot (which is the starting node). When the next node to be moved is considered as the new starting position, each node that is visited, it is extracted from the graph connectivity matrix. The target and robots are labeled with colored labels and the positions of the objects are calculated by color-based quantization and thresholding methods. It has been observed that the system can make the task sharing and creates the appropriate path plan successfully with the variable target number and the different target distributions.