Shortest Path Planning and Efficient Fuzzy Logic Control of Mobile Robots in Indoor Static and Dynamic Environments

Efficient navigation in dynamic environments is a critical skill for of mobile robots, where obstacles can stochastically appear. This paper presents a complete navigation and control system that integrates effective path optimization and motion control capabilities for mobile robots evolving in indoor static and dynamic environments. This system consists primarily of two layers. In the Optimization Layer (Global planner), a Deterministic Constructive Algorithm (DCA) quickly generates the shortest path, as a sequence of nodes, to get to the goal position while avoiding the static obstacles. The Control layer (Local planner) employs an Efficient Fuzzy Logic Controller (EFLC) to continuously guide the robot around the detected dynamic obstacles and drive safely the robot along the intended path. Simulations conducted on various maps with different complexities demonstrate the effectiveness of the DCA planner. Finally, validations using V-REP software show the strength of the proposed EFLC that mimics human reasoning for mobile robots navigating in dynamic environments.