A congestion-aware path planning method considering crowd spatial-temporal anomalies for long-term autonomy of mobile robots
A congestion-aware path planning method is presented for mobile robots during long-term deployment in human occupied environments. With known spatial-temporal crowd patterns, the robot will navigate to its destination via less congested areas. Traditional traffic-aware routing methods do not consider spatial-temporal anomalies of macroscopic crowd behaviour that can deviate from the predicted crowd spatial distribution. The proposed method improves long-term path planning adaptivity by integrating a partially updated memory (PUM) model that utilizes observed anomalies to generate a multi-layer crowd density map to improve estimation accuracy. Using this map, we are able to generate a path that has less chance to encounter the crowded areas. Simulation results show that our method outperforms the benchmark congestion-aware routing method in terms of reducing the probability of robot’s proximity to dense crowds.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
2023 IEEE International Conference on Robotics and Automation (ICRA)Pages
7930-7936Source
2023 IEEE International Conference on Robotics and Automation (ICRA)Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2023-01-17Publication date
2023-07-04Copyright date
2023ISBN
9798350323658Publisher version
Language
- en