  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.
