The proposed research aims to equip mobile robots with advanced logical skills, enabling them to perform locomotion and navigation in unfamiliar environments using sensory information. This study focuses on the design and analysis of robot cognition, sensor fusion, and trajectory scheduling, which must be later integrated with the robot’s locomotion system to achieve different pilot behaviors such as obstacle negotiation, edge following, dead-end avoidance, and target search. The development of a set of fuzzy rules enables pilot behavior. In a variety of environmental situations, the robot uses fuzzy rules during intelligent locomotion. The proposed technique uses the Mamdani fuzzy model to develop an online control algorithm during locomotion within an environment. The cohesive framework enables the mobile robot to create realistic trajectories towards the target. The implementation of the Petri Net Model tackles dynamic obstacles effectively. Using the Petri Net Model, multiple robots communicate with each other and decide on locomotion. The authenticity and efficiency of the proposed control algorithm have been confirmed by performing multiple simulation experiments. To check the effectiveness of the controller, two cases are studied. In the first case, each robot starts from the same position, but they attain the target in a different position. In the second case, the start position is different, but they have the same target position. Finally, both robots are capable of negotiating static and dynamic obstacles, negotiating trajectory traps, and achieving the given target efficiently.