| 基于时间栅格法和免疫算法的高密度仓储机器人跟随与编队技术 |
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| 引用本文：顾三林.基于时间栅格法和免疫算法的高密度仓储机器人跟随与编队技术[J].计算技术与自动化,2026,(2):70-74 |
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| 中文摘要:在求解机器人跟随与编队决策模型时，由于编队形状复杂，决策策略无法保证跟随路线与预设路线的一致性，跟随编队精度偏低。为此，提出了基于时间栅格法和免疫算法的高密度仓储机器人跟随与编队技术。利用栅格法将仓储机器人的二维工作空间转换为栅格地图，并引入时间维度计算各个栅格的栅格值，根据栅格值的取值和环境测量信息标定障碍物的位置，由此确定机器人的可行区域，结合领航机器人的运动学模型和误差量线性函数，利用轮式里程计估计领航机器人的运动速度，基于此，以最小化跟随误差和自动化编队稳定性为目标函数，综合跟随误差和编队形状等约束条件建立机器人跟随编队模型，并采用免疫算法求解模型，以此得到机器人跟随编队策略。实验结果表明，利用所提方法对仓储机器人进行跟随编队决策，跟随机器人的偏航角均保持在0.2°以下，跟随一致性较好，跟随编队精度较高。 |
| 中文关键词:时间栅格法 高密度仓储机器人 跟随编队 免疫算法 动态环境 |
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| High Density Warehousing Robot Following and Formation Technology Based on Time Grid Method and Immune Algorithm |
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| Abstract:When solving the robot following and formation decision-making model, due to the complex shape of the formation, the decision-making strategy cannot guarantee the consistency between the following route and the preset route, resulting in low tracking accuracy of the formation. Therefore, a high-density storage robot following and formation technology based on time grid method and immune algorithm is proposed. Using the grid method to convert the two-dimensional workspace of the storage robot into a grid map, and introducing a time dimension to calculate the grid values of each grid, the position of obstacles is calibrated based on the grid values and environmental measurement information, and the feasible area of the robot is determined. Combining the kinematic model of the navigation robot and the linear function of the error amount, the wheel odometer is used to estimate the motion speed of the navigation robot. Based on this, the objective function is to minimize the following error and the stability of the automated formation. A robot following formation model is established by integrating constraints such as the following error and formation shape, and an immune algorithm is used to solve the model to obtain the robot following formation strategy. The experimental results show that using the proposed method for following formation decision-making of warehousing robots, the yaw angle of the following robots is kept below 0.2 °, with good following consistency and high following formation accuracy. |
| keywords:time grid method high density warehousing robots following formation immune algorithm dynamic environment |
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