Collision avoidance ability is very important for autonomous agricultural vehicles, but the influence of different obstacles in agricultural environment is rarely taken into account. In this paper, a velocity control strategy for collision avoidance was proposed to adjust the velocity of autonomous agricultural vehicles according to the movement state and dangerous degree of the obstacles and the distance between the obstacles and the vehicles, thus to improve intelligence and safety of the vehicles. The control strategy involved two steps: collision prediction in dynamic environments with an improved obstacle space–time grid map, and velocity generator for collision avoidance with a cloud model. Simulations were conducted on the obstacle collision prediction and the designed cloud generator for velocity control respectively. Simulation results show that the proposed strategy can effectively predict collision with anti-disturbance ability for threat-free obstacles and rapid and accurate velocity output. And it realizes the real-time operation in dynamic environments with an average time of 0.2 s to predict collision. Additionally, field experiments including five trial schemes were performed to test the proposed velocity control strategy on an agricultural robot, where a haystack, a tractor and walking persons were regarded as static or dynamic obstacles. The results of the field experiments show that the proposed velocity control strategy has strong feasibility and effectiveness.

中文翻译：

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一种自动农用车辆防撞速度控制策略

防撞能力对于自动驾驶农业车辆非常重要，但是很少考虑各种障碍物对农业环境的影响。提出了一种避免碰撞的速度控制策略，可以根据障碍物的运动状态和危险程度以及障碍物与车辆之间的距离来调节自动驾驶农用车的速度，从而提高车辆的智能性和安全性。车辆。控制策略包括两个步骤：在动态环境中通过改进的障碍物时空网格图进行碰撞预测，以及使用云模型避免碰撞的速度生成器。分别对障碍物碰撞预测和设计的速度控制云发生器进行了仿真。仿真结果表明，所提出的策略可以有效地预测具有无干扰障碍物的抗干扰能力，并能快速准确地输出速度。而且它可以在动态环境中实现实时操作，平均时间为0.2 s，以预测碰撞。此外，还进行了包括五个试验计划在内的现场试验，以在农业机器人上测试拟议的速度控制策略，其中将干草堆，拖拉机和步行者视为静态或动态障碍。现场实验结果表明，提出的速度控制策略具有较强的可行性和有效性。而且它可以在动态环境中实现实时操作，平均时间为0.2 s，以预测碰撞。此外，还进行了包括五个试验计划在内的现场试验，以在农业机器人上测试拟议的速度控制策略，其中将干草堆，拖拉机和步行者视为静态或动态障碍。现场实验结果表明，提出的速度控制策略具有较强的可行性和有效性。而且它可以在动态环境中实现实时操作，平均时间为0.2 s，以预测碰撞。此外，还进行了包括五个试验计划在内的现场试验，以在农业机器人上测试拟议的速度控制策略，其中将干草堆，拖拉机和步行者视为静态或动态障碍。现场实验结果表明，提出的速度控制策略具有较强的可行性和有效性。