Obstacle Avoidance Learning for Robot Motion Planning in Human-Robot Integration Environments

In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning...

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Bibliographic Details
Published in:IEEE transactions on cognitive and developmental systems Vol. 15; no. 4; p. 1
Main Authors: Hong, Yang, Ding, Zhiyu, Yuan, Yuan, Chi, Wenzheng, Sun, Lining
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Collision avoidance
Conditional Variational Autoencoder
Dynamic Obstacle Avoidance
Human motion
Human-Robot Integration Environment
Learning
Motion planning
Navigation
Obstacle avoidance
Path planning
Pedestrians
Planning
Predictive models
Robot dynamics
Robot motion
Robots
Trajectory
ISSN:2379-8920, 2379-8939
Online Access:Get full text
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Summary:In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning. When encountering fast-moving pedestrians, local path planning often fails, causing the robot to stagnate, spin or shake in place, which in turn reduces the navigation efficiency and results in unnatural navigation trajectories. To address this problem, it is desirable for the robot to find a safe and convenient temporary target to avoid the collision with fast-moving pedestrians. In this paper, we propose an obstacle avoidance learning method with the temporary target for the robot motion planning in the human-robot integration environment. The temporary target distribution is learned from imitations by using a Conditional Variational Autoencoder (CVAE) framework, whereby the dynamic scenario information including pedestrian information, the environmental information, and the robot information are considered as the generation conditions. With the proposed method, the mobile robot first navigates to the temporary target area, and then plans the path toward the final target point. Experimental studies reveal that the proposed method can achieve satisfactory performance with respect to different scenario conditions.
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ISSN:2379-8920
2379-8939
DOI:10.1109/TCDS.2023.3242373