Collision-avoidance algorithm for human-symbiotic robot

Hosoda, Yuji; Yamamoto, Kenjirou; Ichinose, Ryouko; Egawa, Saku; Tamamoto, Junichi; Tsubouchi, Kouji; Yuta, Shin’ichi

doi:10.1109/iccas.2010.5669933

Cited by 8 publications

(2 citation statements)

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“…We consider a situation in which a human and robot are interfering with each other; when the robot changes its path, the human also avoids it in the same direction as the robot. As a reaction, conventional path-planning methods make the robot move to the left path, e.g., [12], but it would be more efficient to 'accelerate' and pass on the right path. The robot can execute more appropriate and efficient movements if the path-planning method can output an appropriate velocity vector depending on the situation.…”

Section: Importance Of (Model-based) Dwnmentioning

confidence: 99%

“…Many derivatives can handle moving obstacles, e.g., [11], but those studies do not focus on directly planning a robot trajectory and experimental situations are simple. Path-planning methods for human avoidance have been developed, but some of them only change a moving direction, such as avoiding to the left or right without changing the velocity [12] or stopping or decelerating without changing the path [13]. Moreover, passing scenarios in those studies are limited to specific ones, such as only right angle [14] or facing angle.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots

et al. 2022

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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.

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Section: Importance Of (Model-based) Dwnmentioning

confidence: 99%