A Hybrid Controller for Vision-Based Navigation of Autonomous Vehicles in Urban Environments

Abstract

This paper presents a new hybrid control approach for vision-based navigation applied to autonomous robotic automobiles in urban environments. It is composed by a visual servoing (VS) for road lane following (as deliberative control) and a dynamic window approach (DWA) for obstacle avoidance (as reactive control). Typically, VS applications do not change the velocities to stop the robot in dangerous situations or avoid obstacles while performing the navigation task. However, in several urban conditions, these are elements that must be dealt with to guarantee the safe movement of the car. As a solution for this problem, in this paper, a line-following VS controller will be used to perform road lane following tasks with obstacle avoidance, validating its control outputs in a new image-based dynamic window approach (IDWA). The final solution combines the benefits of both controllers (VS+IDWA) for optimal lane following and fast obstacle avoidance, taking into account the car kinematics and some dynamics constraints. Experiments in a challenging scenario with both simulated and real experimental car show the viability of the proposed methodology.