Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

Abstract

Applying computer vision to mobile robot navigation has been studied for over two decades. One of the most challenging problems for a vision-based mobile robot involves accurately and stably tracking a guide path in the robot limited field of view under high-speed manoeuvres. Pure pursuit controllers are a prevalent class of path tracking algorithms for mobile robots, while their performance is rather limited to relatively low speeds. In order to cope with the demands of high-speed manoeuvres, a multi-loop receding-horizon control framework, including path tracking, robot control, and drive control, is proposed in this paper. This is done within the vision guidance of differential-driving wheeled mobile robots (DWMRs). Lamé curves are used to synthesize a trajectory with G 2 -continuity in the field of view of the mobile robot for path tracking, from its current posture towards the guide path. The platform twist—point velocity and angular velocity—is calculated according to the curvature of the Lamé-curve trajectory, then transformed into actuated joint rates by means of the inverse-kinematics model; finally, the motor torques needed by the driving wheels are obtained based on the inverse-dynamics model. The whole multi-loop control process, initiated from Lamé-curve blending to computational torque control, is conducted iteratively by means of receding-horizon guidance to robustly drive the mobile robot manoeuvring close to the guide path. The results of numerical simulation show the effectiveness of our approach.

Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints

“…The equations highlight that the PP algorithm essentially acts as a P controller for lateral cornering, with the preview distance l d being crucial for effective tracking. In general, a linear relationship is established between the preview distance and the current vehicle speed, as defined in [32]:…”

Trajectory Planning With Lamé-Curve Blending for Motor-Saturation Avoidance Upon Mobile-Robot Turning

“…In order to simplify the calculation, steps 3 and 4 are combined to obtain the homogeneous coordinates of the transition trajectory from Lamé curve C 3 directly [30], i.e.,…”

“…Based on Eqs. (30), the actuated joint rates and their time rates of change have all been derived for the DWMR to track the transition trajectory at a given speed v c . In order to calculate the motor torques, the inverse-dynamics model is formulated in section IV.…”

“…Moreover, from Eqs. (30) and (39), we obtain the inertia-force term of the inverse-dynamics model, namely,…”