Path planning of mobile robots in dynamic environment based on analytic geometry and cubic Bézier curve with three shape parameters

Bulut, Vahide

doi:10.1016/j.eswa.2023.120942

Cited by 5 publications

(2 citation statements)

References 67 publications

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“…Moreover, most curve-based path planning techniques rely on the interpolation of reference points, with the resulting curve passing through all reference points. This approach often results in longer paths compared to the Bezier curve, since these curves only pass through the first and last control points [34]. The Bezier curve is used in various fields, such as face recognition [35], medical imaging [36], computer graphics and animation [37], path planning for robots [38][39][40], etc.…”

Section: Path Smoothing Methodsmentioning

confidence: 99%

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Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Liu,

Wang,

Wang

et al. 2024

Electronics

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In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently, based on fuzzy control theory and sliding mode variable structure control theory, we established a kinematic model for tracked agricultural machinery as the control object, designed a fuzzy sliding mode approaching law, and preprocessed it to reduce the time required for sliding mode control to reach the chosen stage. The simulation experiment of path planning shows that compared with A*, the average reduction rate of the path length for IA* is 5.51%, and the average reduction rate of the number of turning points is 39.01%. The path tracking simulation experiment shows that when the driving speed is set to 0.2 m/s, the adjustment time of the F-SMC controller is reduced by 0.99 s and 1.42 s compared to the FUZZY controller and PID controller, respectively. The variance analysis of the adjustment angle shows that the minimum variance of the F-SMC controller is 0.086, and the error converges to 0, proving that the vehicle trajectory is smoother and ultimately achieves path tracking. The field test results indicate that the path generated by the IA* algorithm can be tracked by the F-SMC controller in the actual environment. Compared to the A* algorithm and FUZZY controller, the path tracking time reduction rate of IA* and F-SMC is 29.34%, and the fuel consumption rate is reduced by 2.75%. This study is aimed at providing a feasible approach for improving the efficiency of tracked agricultural machinery operations, reducing emissions and operating costs.

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Section: Path Smoothing Methodsmentioning

confidence: 99%

“…We design the sliding mode function s 3 = ϕ e = ϕ − ϕ d , from which it can be obtained that . 33) Based on this, the attitude control law is (34) where k 3 > 0, η 3 > 0 (both k 3 and η 3 are constant coefficients).…”

Section: Design Of Posture Control Lawmentioning

confidence: 99%