  The main aim of this paper is to solve a path planning problem for an
autonomous mobile robot in static and dynamic environments. The problem is
solved by determining the collision-free path that satisfies the chosen
criteria for shortest distance and path smoothness. The proposed path planning
algorithm mimics the real world by adding the actual size of the mobile robot
to that of the obstacles and formulating the problem as a moving point in the
free-space. The proposed algorithm consists of three modules. The first module
forms an optimized path by conducting a hybridized Particle Swarm
Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance
and follows path smoothness criteria. The second module detects any infeasible
points generated by the proposed hybrid PSO-MFB Algorithm by a novel Local
Search (LS) algorithm integrated with the hybrid PSO-MFB algorithm to be
converted into feasible solutions. The third module features obstacle detection
and avoidance (ODA), which is triggered when the mobile robot detects obstacles
within its sensing region, allowing it to avoid collision with obstacles. The
simulation results indicate that this method generates an optimal feasible path
even in complex dynamic environments and thus overcomes the shortcomings of
conventional approaches such as grid methods. Moreover, compared to recent path
planning techniques, simulation results show that the proposed hybrid PSO-MFB
algorithm is highly competitive in terms of path optimality.
