A novel safe and flexible control strategy based on target reaching for the navigation of urban vehicles

Vilca, José; Adouane, Lounis; Mezouar, Youcef · 2015 · OpenAlex-citations

DOI: 10.1016/j.robot.2015.01.008

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Summary

This paper addresses the challenge of achieving safe and flexible autonomous navigation for urban vehicles, specifically unmanned ground vehicles operating in structured environments. The authors aim to replicate the reactive behavior of human drivers, who guide vehicles smoothly to destinations while respecting road boundaries, without relying on pre-defined reference trajectories. Traditional methods, such as trajectory tracking and path following, often require complex planning, suffer from replanning difficulties, and depend on computing the closest point on a reference path, which can introduce errors. To overcome these limitations, the study proposes a novel control strategy based on target reaching, allowing the vehicle to navigate through successive waypoints defined by position, orientation, and velocity. The methodology employs a kinematic model of the vehicle, assuming pure rolling and non-slipping conditions, and defines a dynamic or static target with similar non-holonomic constraints. The core of the approach is a control law synthesized using Lyapunov stability analysis. This law utilizes a novel set of control variables, including a specific error function $e_{RT}$ that accounts for the vehicle's position relative to the target's orientation, ensuring the vehicle remains in the "wake" of the target. The control inputs—linear velocity and front wheel orientation—are derived to ensure the asymptotic stability of the error system. Additionally, the paper presents a target assignment strategy for navigating through sequential waypoints and provides a method for estimating the maximum error boundary based on controller parameters, ensuring that navigation remains within safe physical limits. The results demonstrate that the proposed control law is asymptotically stable, as proven through Lyapunov analysis, provided initial orientation and distance errors satisfy specific conditions. The study establishes a relationship between controller parameters and the upper bounds of distance and orientation errors, allowing for precise tuning to respect vehicle kinematic constraints such as maximum velocity and steering angle. Simulations and experiments validate the strategy's flexibility, reliability, and efficiency. The vehicle successfully reaches static and dynamic targets and performs smooth trajectories between waypoints. When waypoints are placed closely together, the control behavior approximates traditional trajectory tracking, demonstrating the method's versatility. The approach avoids the need for complex trajectory generation and replanning, adapting easily to environmental changes. The significance of this work lies in providing a robust, reactive navigation framework that simplifies autonomous vehicle control in urban settings. By eliminating the dependency on pre-generated reference trajectories, the strategy offers greater flexibility and reduces computational complexity associated with path planning. The proven stability and the ability to guarantee safe navigation within defined error boundaries make this approach suitable for various robotic applications, including multi-robot formations and urban transportation. The findings suggest that a small number of well-placed waypoints are sufficient for safe navigation, offering a practical alternative to rigid trajectory-following methods.

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