Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot

Abstract

This research presents a control structure for an omni-wheel mobile robot (OWMR). The control structure includes the path planning module and the motion control module. In order to secure the robustness and fast control performance required in the operating environment of OWMR, a bio-inspired control method, brain limbic system (BLS)-based control, was applied. Based on the derived OWMR kinematic model, a motion controller was designed. Additionally, an optimal path planning module is suggested by combining the advantages of A* algorithm and the fuzzy analytic hierarchy process (FAHP). In order to verify the performance of the proposed motion control strategy and path planning algorithm, numerical simulations were conducted. Through a point-to-point movement task, circular path tracking task, and randomly moving target tracking task, it was confirmed that the suggesting motion controller is superior to the existing controllers, such as PID. In addition, A*–FAHP was applied to the OWMR to verify the performance of the proposed path planning algorithm, and it was simulated based on the static warehouse environment, dynamic warehouse environment, and autonomous ballet parking scenarios. The simulation results demonstrated that the proposed algorithm generates the optimal path in a short time without collision with stop and moving obstacles.

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

“…A comparison with other algorithms was conducted to verify our suggested hybrid algorithm. RRT, RRT*, PSO, and our proposed algorithm were compared with the fuzzy analytic hierarchy process and A* algorithm (A*-FAHP) hybrid path planning algorithm [31]. The A*-FAHP used a stationary warehouse setting with no change in the working space, where the environment workspace is [60×40] m, the starting point is at (5,36), and the goal point is at (50,14).…”

Modeling and Trajectory Planning Optimization for the Symmetrical Multiwheeled Omnidirectional Mobile Robot

“…In [61], vision-based navigation for an industrial OWMR with four Mecanum wheels was proposed, allowing the robot to navigate autonomously and perform anticollision path planning. The research presented in [62] proposed a hybridized approach based on the use of A* algorithm and the fuzzy analytic hierarchy process (FAHP) together with bio-inspired control method in order to achieve optimal path planning and fast control performance in both static and dynamic working environments. In [63], the velocity obstacles (VO) approach was integrated with the nonlinear model predictive control (NMPC) in order to accomplish collision avoidance online in the dynamic environment.…”

An Emotional Discrete Controller PSO Tuned and PLC Implemented for a Real Industrial Pumping System

“…Some studies point that the BELBIC controller has been successfully used to make decisions and to control simple linear systems as in 8 , and also in nonlinear systems such as the control of a command system of a magnetic synchronous motor and automatic voltage regulator (AVR) 9,10 , level control of quadruple tank system 11 , Multi-variable Adaptive Stimuli for an Emotional Learning Based Controller for a MIMO Process 12 , Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot 13 , micro heat exchanger 14 , flight control 15 and crane positioning and displacement control 16 , control method applied in an industrial fan system compared to the conventional PI controller 17 , control industrial induction heating systems with a serie of resonant inverters and compare performance with the conventional PID controller 18 , features an application for accurate tracking of the speed of the hybrid stepper motor, 19 . On the other hand, new BELBIC models have been developed in 20 and 21 .…”