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A robot collision avoidance scheme based on the moving obstacle motion prediction

机译:基于移动障碍物运动预测的机器人碰撞避免方案

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摘要

This paper proposes an integrated scheme for the robot to avoid collision based on obstacle's motion prediction. The robot obtains the target bearing by using object detection technique and fuses the bearing information with the external sensors data to locate the current position of the moving target. The motion state of the moving obstacle is described using the Constant Velocity (CV) model, the Constant Accelerate (CA) model and the Current Statistical (CS) model. Then, a Kalman-based Interacting Multiple Model (IMM) filter is adopted to estimate the obstacle motion trend. This scheme mainly focuses on the obstacle detection and the motion prediction. Based on the motion prediction of the obstacle, an abbreviated strategy is proposed for collision avoidance. To validate the proposed scheme, we have implemented an obstacle avoidance experiment with a Pioneer 3-AT robot and a moving obstacle. The results verify that the proposed scheme is valid and viable.
机译:本文提出了一种机器人的综合方案,以避免基于障碍物的运动预测碰撞。机器人通过使用对象检测技术获得目标轴承,并用外部传感器数据融合轴承信息以定位移动目标的当前位置。使用恒定速度(CV)模型,恒定加速(CA)模型和当前统计(CS)模型来描述移动障碍物的运动状态。然后,采用基于卡尔曼的交互式多模型(IMM)滤波器来估计障碍物运动趋势。该方案主要侧重于障碍物检测和运动预测。基于障碍物的运动预测,提出了一种缩写策略来避免碰撞。为了验证拟议的计划,我们已经实施了一个障碍避免实验,并在机器人和移动障碍物上进行了先驱3.结果验证了拟议方案是否有效和可行。

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