Decentralized decision making and navigation strategy for tracking intruders in a cluttered area by a group of mobile robots

In the current era of the industrial revolution, mobile robots are playing a pivotal role in helping out mankind in many complex and hazardous environments for performing tasks like search and rescue, obstacle avoidance, mining and security surveillance, etc. A lot of navigation algorithms have been developed in recent years but novel challenges still exist in autonomous path planning of multiple robots to track and follow multiple intruders. This report demonstrates a decentralized strategy of arithmetic mean based navigation algorithm for a group of mobile robots to navigate through an unknown environment filled with obstacles to detect and follow multiple invading intruders. The suggested navigation strategy ensures that mobile robots safely move right in the middle of surrounding obstacles to maintain a safe distance and to avoid collision with obstacles and each other. The conventional method of color recognition is used to detect dynamic intruders and calculate pixel values using the Microsoft Kinect sensor camera. A probability of danger algorithm is introduced to ensure that all the intruders present in the environment are being followed by friendly robots on the bases of the minimum distance between an intruder and its follower. The mobile robots follow intruders movement on the bases of their pixel values. The low pixel value means that intruder is far away and high pixel value represents that intruder is closer to the friendly robots. All the algorithms and image processing techniques are implemented and tested in WEBOTS simulation environment using C programming language and the results show the success of proposed arithmetic mean based navigation and probability of danger based intruders following algorithms.