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Autonomous car decision making and trajectory tracking based on genetic algorithms and fractional potential fields
Intelligent Service Robotics ( IF 2.3 ) Pub Date : 2020-02-28 , DOI: 10.1007/s11370-020-00314-x
Jean-Baptiste Receveur , Stéphane Victor , Pierre Melchior

This article deals with the issue of trajectory optimization of autonomous terrestrial vehicles on a specific range handled by the human driver. The main contributions of this paper are a genetic algorithm-potential field combined method for optimized trajectory planning, the definition of the multi-criteria optimization problem by including a time variable, dynamical vehicle constraints, obstacle motion for collision avoidance, and improvements on the attractive and repulsive potential field definitions. The main interests of the proposed method are its efficiency even in only arc-connected spaces with holes, trajectory optimality thanks to the genetic algorithm that minimizes multi-criteria optimization, reactivity thanks to the potential field through the consideration for nature and motion of obstacles, its orientation toward situations a human driver would consider, and finally the inclusion of constraints to avoid danger and to take into account vehicle dynamics. The global trajectory, optimized through genetic algorithm, is used as a reference in a fractional potential field, which is a reactive local path planning method. The repulsive potential field is made safer by adding fractional orders to the obstacles, and the attractive potential field is improved by creating a dynamical optimal target seen from a robust control point of view. This target replaces the unique attractive potential field point and avoids its drawbacks such as local minima. Autonomous car simulation results are given for a crossroad and an overtaking scenarios.

中文翻译:

基于遗传算法和分数势场的自主汽车决策与轨迹跟踪

本文讨论了由驾驶员操纵的特定范围内的自主地面车辆的轨迹优化问题。本文的主要贡献是用于轨迹规划优化的遗传算法-势场组合方法,包括时间变量,动态车辆约束,避免碰撞的障碍物运动以及对吸引力的改进等方面的多准则优化问题的定义。和排斥势场定义。提出的方法的主要兴趣在于它的效率,即使在只有带孔的弧形连接空间中也是如此,由于遗传算法将多准则优化降至最低,因此轨迹的最优性;通过考虑障碍物的性质和运动,由于势场而具有反应性,它面向人类驾驶员要考虑的情况的方向,最后包括避免危险并考虑车辆动力学的约束。通过遗传算法优化的全局轨迹用作分数势场的参考,分数势场是一种反应性局部路径规划方法。通过将分数阶添加到障碍物,可以使排斥势场更安全,而通过创建从鲁棒控制角度来看的动态最佳目标,可以改善吸引势场。该目标取代了独特的有吸引力的潜在场点,并避免了其缺点,例如局部极小值。给出了十字路口和超车场景下的自动汽车仿真结果。最后包括约束以避免危险并考虑车辆动力学。通过遗传算法优化的全局轨迹用作分数势场的参考,分数势场是一种反应性局部路径规划方法。通过将分数阶添加到障碍物,可以使排斥势场更安全,并且通过创建从鲁棒控制角度来看的动态最佳目标,可以改善吸引力势场。该目标取代了独特的有吸引力的潜在场点,并避免了其缺点,例如局部极小值。给出了十字路口和超车场景下的自动汽车仿真结果。最后包括约束以避免危险并考虑车辆动力学。通过遗传算法优化的全局轨迹用作分数势场的参考,分数势场是一种反应性局部路径规划方法。通过将分数阶添加到障碍物,可以使排斥势场更安全,并且通过创建从鲁棒控制角度来看的动态最佳目标,可以改善吸引力势场。该目标取代了独特的有吸引力的潜在场点,并避免了其缺点,例如局部极小值。给出了十字路口和超车场景下的自动汽车仿真结果。通过将分数阶添加到障碍物,可以使排斥势场更安全,而通过创建从鲁棒控制角度来看的动态最佳目标,可以改善吸引势场。该目标取代了独特的有吸引力的潜在场点,并避免了其缺点,例如局部极小值。给出了十字路口和超车场景下的自动汽车仿真结果。通过将分数阶添加到障碍物,可以使排斥势场更安全,而通过创建从鲁棒控制角度来看的动态最佳目标,可以改善吸引势场。该目标取代了独特的有吸引力的潜在场点,并避免了其缺点,例如局部极小值。给出了十字路口和超车场景下的自动汽车仿真结果。
更新日期:2020-02-28
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