A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots

Abstract

This paper presents a generic trajectory planning method for wheeled robots with fixed steering axes while the steering angle of each wheel is constrained. In the existing literatures, All-Wheel-Steering (AWS) robots, incorporating modes such as rotation-free translation maneuvers, in-situ rotational maneuvers, and proportional steering, exhibit inefficient performance due to time-consuming mode switches. This inefficiency arises from wheel rotation constraints and inter-wheel cooperation requirements. The direct application of a holonomic moving strategy can lead to significant slip angles or even structural failure. Additionally, the limited steering range of AWS wheeled robots exacerbates non-linearity characteristics, thereby complicating control processes. To address these challenges, we developed a novel planning method termed Constrained AWS (C-AWS), which integrates second-order discrete search with predictive control techniques. Experimental results demonstrate that our method adeptly generates feasible and smooth trajectories for C-AWS while adhering to steering angle constraints.

Figures (9)

• Figure 1: (a) A typical autonomous airport baggage carrier, equipped with LiDAR, cameras, computing units, and AWS chassis. (b) Design of the vehicle's steering wheel. The image reveals that despite the wheel's ability to rotate via the top-mounted steering motor, its free rotation is constrained by essential circuits and hydraulic systems. (c) and (d) The operation of the AWS vehicle through mode switching and the movement optimization achieved by the proposed C-AWS algorithm, respectively.
• Figure 2: The schematic representation of an AWS robot kinematic model. Note that the $\omega$ direction only indicates the positive direction, instead of the rotating direction at the instance.
• Figure 3: Feasible ICM regions near control center are marked green, here we show feasible regions when $\delta_{lim}=$ 90°, 75°, 60°.
• Figure 4: The sampled maneuvers with $\delta_{lim}=$ 90°, 75°, 60° and front-75°-rear-0° for (a), (b), (c) and (d) respectively. The red points are the end of the velocity vector, the blue points are the end of trajectories, and the black points are instantaneous centers. In all the forward samples above, $N$ of $\mathcal{E}$ is 8, $N$ of $\Psi$ is 8, and $\omega$ is uniformly sampled 8 times in range $[-\pi/2, \pi/2]$.
• Figure 5: In upper figures, the initial and refined trajectory sequences are represented in cyan and blue, the arrow indicating the direction of wheel speeds. In lower figures, plus$(\textbf{+})$ indicate the ICM relative locations before optimization in the spherical coordinate system Spherical_ICM, while bullets$(\bullet)$ represent post-optimization positions, and the red zone indicates the feasible solution of ICMs. The color of the dot gradually transitions from blue to green as time progresses. In this figure: front and rear steering limits are set to $\pm90$° and $\pm75$° in the left and right columns, respectively.