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.
Highlights
Mobile robots have become an essential element in production sites, medical sites, and various robot-based service environments
A hybrid path generation module is designed to utilize the characteristics of A* and fuzzy analytic hierarchy process (FAHP) to generate the optimal path in a short time and secure robustness to a dynamic environment
The motion control module was developed via brain limbic system control due the advantages of BLS control, such as the fast response and robustness to uncertainty, and error elimination was achieved
Summary
Mobile robots have become an essential element in production sites, medical sites, and various robot-based service environments. Most of the work environment is not a stationary environment wherein an unchanging map-based work is possible, but it is a dynamic environment where the work environment is partially or significantly changed. Since the omnidirectional mobile robot is operated in a dynamic working environment, the functions to cover the following issues are essential for the mobile robot to respond to such environment. The first issue is to control the robot to the desired position in a dynamic environment wherein uncertainty exists. Mobile robots are required to have robustness against to the model uncertainty and sensor noise, fast responses to rapidly changing references, and error elimination performance to achieve high accuracy position control.
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