In this paper, the mobile robot motion planning approach (PSO-CF2-mt: PSO-CF2 for moving targets) is improved to track a moving target following a smooth path. PSO-CF2-mt was tested previously in various static and dynamic environments and proved its capacity to track moving targets whatever the form of the target’s trajectory. The problem with PSO-CF2-mt that we want to face in this paper is the stability of the robot’s motion. It is tackled by limiting the angular velocity of the robot. The angular speed limit is PSO selected and the variance of the angular speed is added to the fitness function as a third objective. This new version of PSO-CF2-mt is called OS-CF2-mt (Optimized Stable CF2-mt). Simulation results prove the capacity of OS-CF2-mt in ensuring stable travels for mobile robots following short, secure and smooth paths when tracking moving targets whether the environment is static or dynamic and whatever the form of the target’s trajectory.

Autonomous$\text{PSO-DVSF}^{\text{2}}$is a PSO optimized$\mathrm{F}^{2}$based mobile robot motion planning approach that we have previously proposed to guide a robot in unknown environments. We proved the efficiency of this approach by means of simulation tests [15]. Hence in this paper, we are dealing with the experimental setup of our proposed simulations. As a first step, the approach is tested in the MobileSim virtual environment then in a second step the tests are done in experimental environments using the differential Pioneer P3-DX wheeled robot. The results of these tests proved the real efficiency of autonomous PSO-$\text{DVSF}^{\text{2}}$algorithm to reach its destination selecting the shortest and the securest trajectory whatever the environment is static or dynamic and whatever its complexity.

This paper presents the PSO-CF2-mt motion planning approach that we propose for two wheeled mobile robots for tracking moving targets in known dynamic environments. The Particle Swarm Optimization Canonical Force Field (PSO-CF2) is a mobile robot motion planning approach that we have previously proposed for static and dynamic environments[9]. PSO-CF2-mt is an improved version of PSO-CF2to become capable of tracking a moving target. The basic concept of PSO-CF2-mt is to generate a continually changing parameterized Force Field for the robot based on the characteristics of all objects presents in the environment. The simulation results prove clearly the ability of PSO-CF2-mt to follow and reach a moving target by choosing the shortest and the most secure paths whatever the complexity of the environment and the form of the target's trajectory. A comparative study with APF (Artificial Potential Field) proves the quality of our proposed approach.