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Optimisation based control framework for autonomous vehicles: Algorithm and experiment

机译:基于优化的自动驾驶汽车控制框架:算法与实验

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This paper addresses both path tracking and local trajectory generation for autonomous ground vehicles. An optimisation based two-level control framework is proposed for this task. The high-level control operates in a receding horizon fashion by taking into account real-time sensory information. It generates a feasible trajectory satisfying the nonlinear vehicle model and various constraints, and resolves possible short term conflicts through on-line optimisation. The low-level controller drives the vehicle tracking the local trajectory in the presence of uncertainty and disturbance. It is shown that the time varying controller proposed in this paper guarantees stability under all possible trajectories. The two-level control structure significantly facilitates the real-time implementation of optimisation based control techniques on systems with fast dynamics such as autonomous vehicle systems. The proposed technique is implemented on a small-scale autonomous vehicle in the lab. Both simulation and experimental results demonstrate the efficiency of the proposed technique.
机译:本文介绍了自动地面车辆的路径跟踪和局部轨迹生成。为此,提出了一种基于优化的两级控制框架。高级别控制通过考虑实时感官信息以渐进的方式进行操作。它生成了满足非线性车辆模型和各种约束条件的可行轨迹,并通过在线优化解决了可能的短期冲突。在存在不确定性和干扰的情况下,低级控制器驱动车辆跟踪局部轨迹。结果表明,本文提出的时变控制器保证了在所有可能轨迹下的稳定性。两级控制结构极大地促进了在具有快速动态性的系统(例如自动驾驶汽车系统)上实时实施基于优化的控制技术。所提出的技术在实验室中的小型自动驾驶汽车上实现。仿真和实验结果均证明了该技术的有效性。

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