Model Predictive Obstacle Avoidance and Wheel Allocation Control of Mobile Robots Using Embedded CPU

Abstract

In this study, we propose a real-time model predictive control method for leg/wheel mobile robots which simultaneously achieves both obstacle avoidance and wheel allocation at a flexible position. The proposed method generates both obstacle avoidance path and dynamical wheel positions, and controls the heading angle depending on the slope of the predicted path so that the robot can keep a good balance between stability and mobility in narrow and complex spaces like indoor environments. Moreover, we reduce the computational effort of the algorithm by deleting usage of mathematical function in the repetitive numerical computation. Thus the proposed real-time optimization method can be applied to low speed on-board CPUs used in commerciallyproduced vehicles. We conducted experiments to verify efficacy and feasibility of the real-time implementation of the proposed method. We used a leg/wheel mobile robot which is equipped with two laser range finders to detect obstacles and an embedded CPU whose clock speed is only 80 MHz. Experiments indicate that the proposed method achieves improved obstacle avoidance comparing with the previous method in the sense that it generates an avoidance path with balanced allocation of right and left side wheels.

Model predictive obstacle avoidance control for leg/wheel mobile robots with optimized articulated leg configuration

“…2) Comparison of the optimality between the proposed method and our latest method [19]. 3) Further reduction of local minima of the artificial potential field method.…”

“…We expect that the optimality of the entire robot system is improved by simultaneous optimization of wheel positions and joint angles. However, the optimality of the proposed method and our latest method [19] is not compared based on the same index. It should be executed.…”

“…Using the state space equation (19), the shape of the body cannot be maintained and the wheels diverge. To prevent divergence of the wheels, we apply the constrains to maintain the shape of the body represented by…”

“…Recently, the performance of the computers have been improved and fast numerical algorithms for MPC have been proposed [14]- [16] to apply MPC to the practical problems. Thus, the real-time implementation of MPC becomes feasible and is applied to the robot controls [9], [17]- [19].…”

“…In our past studies [17]- [19], we have proposed obstacle avoidance control methods for leg/wheel mobile robots. However, in our latest methods [19], the optimization of the robot position/wheel positions, and the joint angles/wheel speeds are divided and the optimization of the wheel positions does not consider the dynamics of the leg. Thus, the joint angles may not realize due to the actual limitation and the optimality of the entire robot system is low.…”