| 许 松,轩 亮,孙剑韬,周怀东.融合行人运动信息的室内移动机器人动态避障方法[J].电子测量与仪器学报,2022,36(12):144-152 |
| 融合行人运动信息的室内移动机器人动态避障方法 |
| Dynamic obstacle avoidance method for indoor mobile robotintegrating pedestrian motion information |
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| DOI： |
| 中文关键词: 移动机器人 行人轨迹预测 动态避障 |
| 英文关键词:mobile robot pedestrian trajectory prediction dynamic obstacle avoidance |
| 基金项目:国家重点研发计划(2019YFB1310802)、江汉大学校级科研项目(2022XKZX33)资助 |
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| 中文摘要: |
| 为了提高移动机器人在室内人机共融环境下的运动安全和交互性,提出了一种融合行人运动信息的室内移动机器人动
态避障方法,同时考虑任务约束和社会规则。 首先,利用 YOLO v3 算法和 Deep Sort 算法分别对室内环境中的行人进行实时检
测与目标跟踪,计算行人在过去时刻的历史轨迹。 然后,利用 Social-GAN 算法构建行人交互模型,实现轨迹预测。 在此基础上,
将行人的运动状态融合进机器人避障算法之中,根据社会规则设计评价函数,对机器人采样速度样本进行评估,使移动机器人
能够以安全和舒适的方式绕过行人,确保室内人机共融环境下移动机器人的社会接受性。 通过实验对比分析,与传统 DWA 方
法相比,本文方法不仅可以提高机器人导航避障效率,在相同室内场景下导航避障时间由 23. 56 s 提高到 19. 38 s,而且可以有
效降低与行人发生碰撞的风险,保证机器人导航的安全和社交性。 |
| 英文摘要: |
| In order to improve the motion safety and interactivity of mobile robots in an indoor human-robot integration environment, a
dynamic obstacle avoidance method for indoor mobile robots integrating pedestrian motion information is proposed, considering both task
constraints and social rules. First, the YOLO v3 algorithm and the Deep Sort algorithm are used to detect and track pedestrians in indoor
environments in real time, respectively, and calculate the historical trajectories of pedestrians in the past. Then, the Social-GAN
algorithm is used to build a pedestrian interaction model to achieve trajectory prediction. On this basis, the motion state of pedestrians is
integrated into the robot obstacle avoidance algorithm, the evaluation function is designed according to social rules, and the sampling
speed samples of the robot are evaluated, so that the robot can bypass pedestrians in a safe and comfortable way and ensure social
acceptability of mobile robots in a human-robot integration environment. Through experimental comparison and analysis, compared with
the traditional DWA method, the method in this paper not only improves the efficiency of robot navigation and obstacle avoidance, but
also increases the navigation obstacle avoidance time from 23. 56 s to 19. 38 s in the same indoor scene, and can effectively reduce the
collision with pedestrians risk, and ensure the safety and sociality of robot navigation. |
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