Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance
For autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan avoida...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
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| Online Access: | http://dx.doi.org/10.1080/18824889.2024.2307684 |
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| _version_ | 1850061562726842368 |
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| author | Satoshi Hoshino Yu Kubota Yusuke Yoshida |
| author_facet | Satoshi Hoshino Yu Kubota Yusuke Yoshida |
| author_sort | Satoshi Hoshino |
| collection | DOAJ |
| description | For autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan avoidance motions in consideration of the time series variation in the images. For this purpose, an LSTM block is applied to the CNN. The policy of the motion planner represented by CNN with LSTM is trained through imitation learning. In this regard, however, it is difficult for the robot to recognize unknown objects as obstacles. For obstacle recognition, a perception process is further provided between the image inputs and CNN with LSTM in the motion planner. Moreover, the robot plans different avoidance motions depending on the velocity of the dynamic obstacle. For this purpose, an obstacle state classifier based on CNN is used ahead of the motion planner. A depth-difference image generated from two depth images is fed as the input to the classifier. A classified state that indicates the velocity of the obstacle is fed as the input to the following motion planner. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward a destination autonomously while avoiding standing and walking persons, respectively. Furthermore, we show that the robot based on the motion planner with the obstacle state input is able to plan different avoidance motions for a person walking slowly or fast using the obstacle state classifier. |
| format | Article |
| id | doaj-art-768f5fd4658240dcabcbf8694d58cd13 |
| institution | DOAJ |
| issn | 1884-9970 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj-art-768f5fd4658240dcabcbf8694d58cd132025-08-20T02:50:12ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702024-12-01171193010.1080/18824889.2024.23076842307684Motion planner based on CNN with LSTM through mediated perception for obstacle avoidanceSatoshi Hoshino0Yu Kubota1Yusuke Yoshida2Utsunomiya UniversityUtsunomiya UniversityHitachi Construction MachineryFor autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan avoidance motions in consideration of the time series variation in the images. For this purpose, an LSTM block is applied to the CNN. The policy of the motion planner represented by CNN with LSTM is trained through imitation learning. In this regard, however, it is difficult for the robot to recognize unknown objects as obstacles. For obstacle recognition, a perception process is further provided between the image inputs and CNN with LSTM in the motion planner. Moreover, the robot plans different avoidance motions depending on the velocity of the dynamic obstacle. For this purpose, an obstacle state classifier based on CNN is used ahead of the motion planner. A depth-difference image generated from two depth images is fed as the input to the classifier. A classified state that indicates the velocity of the obstacle is fed as the input to the following motion planner. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward a destination autonomously while avoiding standing and walking persons, respectively. Furthermore, we show that the robot based on the motion planner with the obstacle state input is able to plan different avoidance motions for a person walking slowly or fast using the obstacle state classifier.http://dx.doi.org/10.1080/18824889.2024.2307684mobile robotsmotion planningobstacle avoidancecnnlstm |
| spellingShingle | Satoshi Hoshino Yu Kubota Yusuke Yoshida Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance SICE Journal of Control, Measurement, and System Integration mobile robots motion planning obstacle avoidance cnn lstm |
| title | Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance |
| title_full | Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance |
| title_fullStr | Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance |
| title_full_unstemmed | Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance |
| title_short | Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance |
| title_sort | motion planner based on cnn with lstm through mediated perception for obstacle avoidance |
| topic | mobile robots motion planning obstacle avoidance cnn lstm |
| url | http://dx.doi.org/10.1080/18824889.2024.2307684 |
| work_keys_str_mv | AT satoshihoshino motionplannerbasedoncnnwithlstmthroughmediatedperceptionforobstacleavoidance AT yukubota motionplannerbasedoncnnwithlstmthroughmediatedperceptionforobstacleavoidance AT yusukeyoshida motionplannerbasedoncnnwithlstmthroughmediatedperceptionforobstacleavoidance |