论文部分内容阅读
传统路径规划算法针对多目标情况,主要依据多单一信息融合结果选择避障路径,在中规模的污泥纠缠区域中容易陷入盲区,无法对污泥纠缠环境下的机器人路径进行准确的规划。为此提出一种改进的机器人视觉纠缠摆脱路径规划方法,借助机器人视觉仪器采集污泥纠缠特征,用归一化方法把视觉信息融入到规划模型中进行最佳路径的选择,将机器人摆脱污泥纠缠以及最短路径的要求融合成一个适应度函数,通过遗传算法搜索获取最佳机器人摆脱路径。实验结果说明,该方法对于污泥纠缠环境下机器人摆脱路径规划长度以及效率都优于传统模型,具有较高的鲁棒性。
For the multi-objective case, the traditional path planning algorithm chooses the obstacle avoidance path mainly based on the results of multiple single information fusion. It is easy to fall into the blind area in the medium-sized sludge entanglement area and can not accurately plan the robot path in the entangled environment of sludge. Therefore, an improved robot vision entanglement free path planning method is proposed. With the aid of robotic visual instruments, the entanglement characteristics of sludge are collected, and the visual information is integrated into the planning model by using the normalized method to select the best path. The robot is rid of the sludge The entanglement and the shortest path requirements are merged into a fitness function, and the best robot gets rid of the path through genetic algorithm search. The experimental results show that this method is superior to the traditional model in getting rid of path planning length and efficiency in sludge entangled environment, and has high robustness.