Motion control of multiple humanoids using a hybridized prim’s algorithm-fuzzy controller

Prim’s algorithm has demonstrated a very effective and selective method of solving the minimum spanning tree optimization problems. It is a greedy algorithm that starts from an empty spanning tree and reaches its goal by picking the minimum weight edges which alternately optimizes the path in less possible time. In this paper, the capability of prim’s algorithm in designing the behavioural controller of a humanoid robot has been shown. Here, a new hybrid PA–Fuzzy motion planning approach has been proposed that uses the concept of minimizing the distance between the robot and obstacles as well as robot and target. An optimal turning angle is generated by the hybrid controller that helps to avoid the obstacles present in the arena to create a collision-free path. The results obtained from hybrid PA–Fuzzy motion planning procedure show the capability of the controller in achieving the optimal paths in different environments with both static and dynamic obstacles. The results observed from simulation and experimental arenas are found to be in satisfactory agreement with each other producing minimal error limits. The developed hybrid technique is compared with some existing methodologies, and significant improvement is found in relation to path length and computational time.