Velocity control strategy based on a cloud model for unmanned agricultural vehicle during obstacle crossing
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摘要:目的
提高遥控操作农业车辆的智能性与安全性。
方法提出一种新的无人农业车辆遇障后的速度控制方法。建立动态环境中无人车辆的碰撞预测模型，确定实时碰撞位置，依据专家经验与农业作业环境制定的云推理规则，建立速度控制策略，实现速度控制。
结果算法预测判断平均耗时0.170 1 s，无人车辆速度控制过程没有受到无威胁障碍物影响，且符合速度云推理规则。
结论该算法能够实现实时碰撞预测，具备抗干扰能力，满足实时性要求。
Abstract:ObjectiveTo improve the intelligence and safety of remote-operated agricultural vehicles.
MethodA new method for unmanned agricultural vehicles during obstacle crossing was proposed. The collision prediction model of unmanned vehicles in dynamic environment was established, and the real-time collision location was determined. According to the cloud inference principle based on both experience of experts and agricultural operation environment, velocity control strategy was established to realize velocity control.
ResultThe algorithm took 0.170 1 s on average to make a prediction, the velocity control results of unmanned vehicles excluded the impact of non-threatening obstacles and accorded with the velocity cloud inference principle.
ConclusionThe established algorithm is able to realize real-time collision prediction, possesses anti-disturbance ability, and satisfies real-time requirement.
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图 4 运动障碍物直线预测模型
vr：障碍物速度；ar：障碍物加速度；θ：障碍物运动方向与X轴正向的夹角
Figure 4. Straightline prediction model of moving obstacles
表 1 速度推理规则
Table 1 Velocity inference principle
危险等级(j) 距离等级(i) 近(1) 较近(2) 一般(3) 较远(4) 远(5) 低(1) 速度较慢 速度一般 速度一般 速度较快 速度快 较低(2) 速度较慢 速度较慢 速度一般 速度较快 速度快 一般(3) 速度零 速度较慢 速度一般 速度较快 速度较快 较高(4) 速度零 速度较慢 速度较慢 速度一般 速度较快 高(5) 速度零 速度零 速度较慢 速度一般 速度较快 
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