SPACE-CONSTRAINED AUTONOMOUS REVERSING OF ARTICULATED VEHICLES
This dissertation presents several modern control methods for autonomous reversing of long combination vehicles (LCVs). These approaches not only significantly improve the performance of previous autonomous reversing systems, but also address a major gap in the reversing control literature for LCVs. The methods were validated by implementing them at full-scale on experimental articulated vehicles owned by the ‘Cambridge Vehicle Dynamics Consortium’ (CVDC). Experimental results were in very good agreement with simulation results. Previous path following control methods for autonomous reversing of LCVs have focussed on minimising the tracking error between the rear-end of the combination and a desired path, irrespective of the motion of the rest of the vehicle. A significant disadvantage of these strategies is that the other parts of the vehicle, particularly the tractor unit, can experience large excursions from the reference path, thus making their implementation infeasible for manoeuvres in spatially-limited areas, such as warehouse yards and normal roads. The ‘Minimum Swept Path Control’ (MSPC) method was devised to reduce this problem by relaxing the requirement for very accurate path following, while minimising the maximum excursion of the vehicle. This strategy weights both the path-following error at the rear end of the vehicle and the swept path of the front end of the vehicle. MSPC enables the swept path to be reduced by about 50%, compared with path following control, which gives this method more realistic applications. The ‘Lane-bounded Reversing Control’ (LBRC) method requires a vehicle to satisfy the reversing objectives while constraining the motion to be within a specified ‘lane’. The LBRC controller is ‘intelligent’, which means it can pursue an optimum route without tracking a desired path generated by a path planner and can proactively avoid future potential clashes. Hence, this controller enables the autonomous reversing system to perform a precise, minimum-cost and collision-free manoeuvre to a specified terminal position by planning ahead and making optimal decisions. The controller performance was evaluated in numerous realistic scenarios, both simulated and in field tests at full-scale. The analysis of LBRC provides a solid foundation for the development of more advanced control methods. Adaptive Lane-bounded Reversing Control (ALBRC) and Adaptive Bi-directional Control (ABC) systems were designed to improve the LBRC method. The tuning of the LBRC controller was based on empirical experience and there are many weights to be tuned in the controller configuration. To offset this drawback, the ALBRC algorithm was developed by attaching ‘virtual bumpers’ to the vehicle system states, and allowing the controller weights to adapt to lane boundaries and obstacles. The ALBRC method simplifies the original tuning process significantly. The ALBRC controller performs well in most cases. However, a solution is not guaranteed if the preview and control horizons are not tuned properly or a vehicle reverses from an arbitrary position. Hence, the ABC algorithm incorporates a so-called ‘cusp technology’, which allows a vehicle to move forward and backward to realign its position and orientation between attempts at the reversing manoeuvre. In this case, the preview and control horizons do not need to change in different scenarios. The ABC method can significantly reduce computational time compared with using a long preview horizon.
Autonomous Driving, Articulated Vehicles, Model Predictive Control, Sequential Decision Making, Reinforcement Learning
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