Learning of an autonomous mobile robot for path generation includes the use of previous experience to obtain the better path within its work space. When the robot is moving in its search space for target seeking, each task requires different form of learning. Therefore, the modeling of an efficient learning mechanism is the hardest problem for an autonomous mobile robot. To solve this problem, the present research work introduced an adaptive learning-based motion planner using artificial immune system, called adaptive immune-based path planner. Later the developed adaptive mechanism has been integrated to the innate immune-based path planner in order to obtain the better results. To verify the effectiveness of the proposed adaptive immune-based motion planner, simulation results as well as experimental results are presented in various unknown environments.
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Deepak, B.B.V.L., Parhi, D. Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment. Intel Serv Robotics 6, 155–162 (2013). https://doi.org/10.1007/s11370-013-0131-9