TY - JOUR
T1 - Dynamic Waypoint Navigation
T2 - Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots
AU - Kamezaki, Mitsuhiro
AU - Kobayashi, Ayano
AU - Kono, Ryosuke
AU - Hirayama, Michiaki
AU - Sugano, Shigeki
N1 - Funding Information:
This work was supported in part by the Japan Science and Technology Agency (JST) PRESTO under Grant JPMJPR1754; in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant 19H01130; and in part by the Research Institute Science and Engineering, Waseda University.
Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Autonomous mobile robot
KW - dynamic waypoint navigation
KW - path planning
KW - real-time adaptive trajectory planning
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U2 - 10.1109/ACCESS.2022.3194146
DO - 10.1109/ACCESS.2022.3194146
M3 - Article
AN - SCOPUS:85135762135
SN - 2169-3536
VL - 10
SP - 81546
EP - 81555
JO - IEEE Access
JF - IEEE Access
ER -