With the growing adoption of electric vehicles (EVs) and increasing demand for efficient parking infrastructure, intelligent pallet-based parking systems with automated guided vehicles (AGVs) offer a promising solution for high-density EV storage and automated valet parking (AVP) operations. However, in such systems, orthogonal-motion AGVs often experience unstable transport conditions due to abrupt speed variations during operation, which can lead to vibrations and safety risks. Trigonometric acceleration and deceleration algorithms are known for their smooth transitions and low impact, but their high computational complexity makes them difficult to implement in embedded AGV systems that require real-time responsiveness. To address this challenge, this paper proposes an approach that approximates the sine function using a third-order Chebyshev polynomial, thereby constructing a complete acceleration and deceleration algorithm. The algorithm includes speed profile planning under conditions with and without constant-speed phases. Simulation analyses and scaled prototype experiments on an orthogonal-motion AGV were conducted. Compared with the traditional sine-based method, the scaled AGV prototype exhibited a maximum speed tracking error of 5 mm/s and a positioning error of 0.38 mm over an 800 mm travel distance. These results indicate that our approach not only preserves the smooth acceleration/deceleration profile of trigonometric curves but also enhances throughput, positional accuracy, and real-time responsiveness, making it suitable for practical EV parking and automated valet parking systems.
Gu et al. (Wed,) studied this question.