Abstract
Freight Urban Robotic Vehicle (FURBOT) is an autonomous vehicle designed to transport last mile freight to designated urban stations. It is a slow vehicle designed to tackle urban environment with complete autonomy. A slow vehicle may have slightly different strategies for avoiding obstacles. Unlike on a highway, it has to deal with pedestrians, traffic lights and slower vehicles while maintaining smoothness in its drive. To tackle obstacle avoidance for this vehicle, sensor feedback based strategies have been formulated for smooth drive and obstacle avoidance. A full mathematical model for the vehicle is formulated and simulated in MATLAB environment. The mathematical model uses velocity control for obstacle avoidance without steering control. The obstacle avoidance is attained through velocity control and strategies are formulated with velocity profiling. Innovative techniques are formulated in creating the simulated sensory feed-backs of the environment. Using these feed-backs, correct velocity profiling is autonomously created for giving velocity profile input to the velocity controller. Proximity measurements are assumed to be available for the vehicle in its given range of drive. Novelty is attained by manipulating velocity profile without prior knowledge of the environment. Four different type of obstacles are modeled for simulated environment of the vehicle. These obstacles are randomly placed in the path of the vehicle and autonomous velocity profiling is verified in simulated environment. The simulated results obtained show satisfactory velocity profiling for controller input. The current technique helps to tune the existing controller and in designing of a better velocity controller for the autonomous vehicle and bridges the gap between sensor feed-back and controller input. Moreover, accurate input profiling creates less strain on the system and brings smoothness in drive for an overall safer environment.
Highlights
Work on autonomous vehicle and specially for obstacle avoidance is being studied in depth in recent times due to high demand of autonomous vehicles
Obstacle avoidance for autonomous vehicles mainly fall in the category of path planning and steering control [1,2,3,4,5,6,7,8]
Velocity profiling was formulated in this research and implemented in the simulated environment for the project
Summary
Work on autonomous vehicle and specially for obstacle avoidance is being studied in depth in recent times due to high demand of autonomous vehicles. Obstacle avoidance is usually limited to autonomous parking problem [18,19] This is the usual setting where vehicle speeds are slow and speed control is necessary in addition to steering control. The best option to do so is not through steering control due to lane limitation, but through velocity control It has to tackle other type of hurdles within its path except moving vehicles e.g., traffic lights. Keeping lane restriction in mind and goal of avoiding obstacles, the best option is to cater with autonomous velocity profiling This need sets the tone for technical requirement for our work that is carried out in this research
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