Risk Vector-based Near miss Obstacle Avoidance for Autonomous Surface Vehicles
Risk Vector-based Near miss Obstacle Avoidance for Autonomous Surface Vehicles
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DOI：
10.1109/iros45743.2020.9341105
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Mingi Jeong;Alberto Quattrini Li
中科院分区：
文献类型：
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作者：
Mingi Jeong;Alberto Quattrini Li
提出了一种新的基于风险向量的近脱靶预测和避障方法。所提出的方法使用传感器读取其他障碍物的姿态来推断它们的运动模型（速度和方向），并相应地调整风险评估并在必要时采取纠正措施。相对矢量计算允许该方法实时执行。该算法的计算速度比其他方法快1.68倍，运动变化少，使机器人能够在拥挤区域避开25个障碍物。还提出了传感器故障或情况变化时的回退行为。利用定制的机器人船进行的海洋实验参数模拟实验表明，该方法对环境中存在的许多障碍物具有灵活性和适应性。研究结果强调了更有效的轨迹和与其他先进方法相当的安全性，以及对故障的鲁棒性。
This paper presents a novel risk vector-based near miss prediction and obstacle avoidance method. The proposed method uses the sensor readings about the pose of the other obstacles to infer their motion model (velocity and heading) and, accordingly, adapt the risk assessment and take corrective actions if necessary. Relative vector calculations allow the method to perform in real-time. The algorithm has 1.68 times faster computation performance with less change of motion than other methods and it enables a robot to avoid 25 obstacles in a congested area. Fallback behaviors are also proposed in case of faulty sensors or situation changes. Simulation experiments with parameters inferred from experiments in the ocean with our custom-made robotic boat show the flexibility and adaptability of the proposed method to many obstacles present in the environment. Results highlight more efficient trajectories and comparable safety as other state-of-the-art methods, as well as robustness to failures.