Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots
Path planning in dynamic environments is still a challenging issue with autonomous mobile robots. Current methods lack adaptability to various passing scenarios, a variety of passing trajectories including an acceleration path, or immediacy in planning time, which require human-aware navigation. In...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9841573/ |
| _version_ | 1828152367964487680 |
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| author | Mitsuhiro Kamezaki Ayano Kobayashi Ryosuke Kono Michiaki Hirayama Shigeki Sugano |
| author_facet | Mitsuhiro Kamezaki Ayano Kobayashi Ryosuke Kono Michiaki Hirayama Shigeki Sugano |
| author_sort | Mitsuhiro Kamezaki |
| collection | DOAJ |
| description | Path planning in dynamic environments is still a challenging issue with autonomous mobile robots. Current methods lack adaptability to various passing scenarios, a variety of passing trajectories including an acceleration path, or immediacy in planning time, which require human-aware navigation. In this study, we propose Dynamic Waypoint Navigation (DWN), which is a model-based adaptive real-time trajectory planning method. DWN first predicts human-robot path interference and the time and position of the interference on the basis of the measured velocity of humans. It then dynamically designates several waypoints considering the time delay of both calculation time and robot travel time. Then, DWN generates several trajectories by combining different speeds (default, acceleration, and deceleration) and paths (default, right, and left) and selects the best trajectory in terms of an interference-avoidance energy cost based on the degree of velocity-vector change. DWN can also output a trajectory within 0.5 s to immediately adapt to changes in human behavior and adopt a simple mathematical model and algorithm to enable easy expansion. Simulation and experimental results reveal that the DWN can adequately select a time-efficient trajectory in real-time and adaptively change a trajectory depending on human movement. |
| first_indexed | 2024-04-11T22:09:30Z |
| format | Article |
| id | doaj.art-0b3486b7a3db4a6192bef9718cb71c98 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:09:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0b3486b7a3db4a6192bef9718cb71c982022-12-22T04:00:35ZengIEEEIEEE Access2169-35362022-01-0110815468155510.1109/ACCESS.2022.31941469841573Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile RobotsMitsuhiro Kamezaki0https://orcid.org/0000-0002-4377-8993Ayano Kobayashi1Ryosuke Kono2Michiaki Hirayama3Shigeki Sugano4https://orcid.org/0000-0002-9331-2446Waseda Research Institute for Science and Engineering, Waseda University, Shinjuku-ku, Tokyo, JapanDepartment of Modern Mechanical Engineering, Waseda University, Shinjuku-ku, Tokyo, JapanDepartment of Modern Mechanical Engineering, Waseda University, Shinjuku-ku, Tokyo, JapanDepartment of Modern Mechanical Engineering, Waseda University, Shinjuku-ku, Tokyo, JapanDepartment of Modern Mechanical Engineering, Waseda University, Shinjuku-ku, Tokyo, JapanPath planning in dynamic environments is still a challenging issue with autonomous mobile robots. Current methods lack adaptability to various passing scenarios, a variety of passing trajectories including an acceleration path, or immediacy in planning time, which require human-aware navigation. In this study, we propose Dynamic Waypoint Navigation (DWN), which is a model-based adaptive real-time trajectory planning method. DWN first predicts human-robot path interference and the time and position of the interference on the basis of the measured velocity of humans. It then dynamically designates several waypoints considering the time delay of both calculation time and robot travel time. Then, DWN generates several trajectories by combining different speeds (default, acceleration, and deceleration) and paths (default, right, and left) and selects the best trajectory in terms of an interference-avoidance energy cost based on the degree of velocity-vector change. DWN can also output a trajectory within 0.5 s to immediately adapt to changes in human behavior and adopt a simple mathematical model and algorithm to enable easy expansion. Simulation and experimental results reveal that the DWN can adequately select a time-efficient trajectory in real-time and adaptively change a trajectory depending on human movement.https://ieeexplore.ieee.org/document/9841573/Autonomous mobile robotdynamic waypoint navigationpath planningreal-time adaptive trajectory planning |
| spellingShingle | Mitsuhiro Kamezaki Ayano Kobayashi Ryosuke Kono Michiaki Hirayama Shigeki Sugano Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots IEEE Access Autonomous mobile robot dynamic waypoint navigation path planning real-time adaptive trajectory planning |
| title | Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots |
| title_full | Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots |
| title_fullStr | Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots |
| title_full_unstemmed | Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots |
| title_short | Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots |
| title_sort | dynamic waypoint navigation model based adaptive trajectory planner for human symbiotic mobile robots |
| topic | Autonomous mobile robot dynamic waypoint navigation path planning real-time adaptive trajectory planning |
| url | https://ieeexplore.ieee.org/document/9841573/ |
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