research-article

CrowdBot: An Open-Environment Robot Management System for On-Campus Services

Authors:

Longbiao ChenAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 2

Article No.: 80, Pages 1 - 27

https://doi.org/10.1145/3659601

Published: 15 May 2024 Publication History

Abstract

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these limitations, we introduce CrowdBot, a robot management system that enhances service in campus environments. Our system leverages a hierarchical reinforcement learning-based cloud-edge hybrid scheduling framework (REDIS), for efficient online streaming task assignment and dynamic action scheduling. To verify the REDIS framework, we have developed a digital twin simulation platform, which integrates large language models and hot-swapping technology. This facilitates seamless human-robot interaction, efficient task allocation, and cost-effective execution through the reuse of robot equipment. Our comprehensive simulations corroborate the system's remarkable efficacy, demonstrating significant improvements with a 24.46% reduction in task completion times, a 9.37% decrease in travel distances, and up to a 3% savings in power usage. Additionally, the system achieves a 7.95% increase in the number of tasks completed and a 9.49% reduction in response time. Real-world case studies further affirm CrowdBot's capability to adeptly execute tasks and judiciously recycle resources, thereby offering a smart and viable solution for the streamlined management of campus services.

References

[1]

R. Benjamin, "The gated community mentality," New York Times, vol. 29, 2012.

[2]

B. Hamama and J. Liu, "What is beyond the edges? gated communities and their role in china's desire for harmonious cities," City, Territory and Architecture, vol. 7, pp. 1-12, 2020.

[3]

A. Lee and A. L. Toombs, "Robots on campus: understanding public perception of robots using social media," in Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing, 2020, pp. 305--309.

Digital Library

[4]

G. G. Garrido, M. Dissanayake, T. Sattar, A. Plastropoulos, and M. Hashim, "Sircaur: Safe inspection of reinforced concrete structures by autonomous robot," in CLAWAR 2020: 23rd International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, 2020.

[5]

L. Fuentes-Moraleda, P. Díaz-Pérez, A. Orea-Giner, A. Muñoz-Mazón, and T. Villacé-Molinero, "Interaction between hotel service robots and humans: A hotel-specific service robot acceptance model (sram)," Tourism Management Perspectives, vol. 36, p. 100751, 2020.

[6]

D. Yu, Y. Wang, and Z. Zhou, "Software crowdsourcing task allocation algorithm based on dynamic utility," IEEE Access, vol. 7, pp. 33 094-33 106, 2019.

[7]

Z. Chen, P. Cheng, L. Chen, X. Lin, and C. Shahabi, "Fair task assignment in spatial crowdsourcing," Proc. VLDB Endow., vol. 13, no. 12, p. 2479-2492, jul 2020. [Online]. Available: https://doi.org/10.14778/3407790.3407839

Digital Library

[8]

Y. Zhao, K. Zheng, J. Guo, B. Yang, T. B. Pedersen, and C. S. Jensen, "Fairness-aware task assignment in spatial crowdsourcing: Game-theoretic approaches," in 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021, pp. 265--276.

[9]

Q. Kang, H. He, and J. Wei, "An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems," Journal of Parallel and Distributed Computing, vol. 73, no. 8, pp. 1106-1115, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S074373151300052X

Digital Library

[10]

G. Attiya and Y. Hamam, "Task allocation for maximizing reliability of distributed systems: A simulated annealing approach," Journal of Parallel and Distributed Computing, vol. 66, no. 10, pp. 1259-1266, 2006. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0743731506001432

Digital Library

[11]

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal policy optimization algorithms," arXiv preprint arXiv:1707.06347, 2017.

[12]

Y. Tu, H. Chen, L. Yan, and X. Zhou, "Task offloading based on lstm prediction and deep reinforcement learning for efficient edge computing in iot," Future Internet, vol. 14, no. 2, p. 30, 2022.

[13]

T. Wu, M. Terry, and C. J. Cai, "Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts," in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 2022, pp. 1--22.

[14]

Y. Liu, Z. Yu, B. Guo, Q. Han, J. Su, and J. Liao, "Crowdos: A ubiquitous operating system for crowdsourcing and mobile crowd sensing," IEEE Transactions on Mobile Computing, vol. 21, no. 3, pp. 878-894, 2020.

[15]

M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng et al., "Ros: an open-source robot operating system," in ICRA workshop on open source software, vol. 3, no. 3.2. Kobe, Japan, 2009, p. 5.

[16]

A. Juliani, V.-P. Berges, E. Teng, A. Cohen, J. Harper, C. Elion, C. Goy, Y. Gao, H. Henry, M. Mattar et al., "Unity: A general platform for intelligent agents," arXiv preprint arXiv:1809.02627, 2018.

[17]

H. Gao and H. Zhao, "A personalized task allocation strategy in mobile crowdsensing for minimizing total cost," Sensors, vol. 22, no. 7, 2022. [Online]. Available: https://www.mdpi.com/1424-8220/22/7/2751

[18]

A. Ali, M. A. Qureshi, M. Shiraz, and A. Shamim, "Mobile crowd sensing based dynamic traffic efficiency framework for urban traffic congestion control," Sustainable Computing: Informatics and Systems, vol. 32, p. 100608, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2210537921000962

[19]

X. Zhang, Y. Wu, L. Huang, H. Ji, and G. Cao, "Expertise-aware truth analysis and task allocation in mobile crowdsourcing," IEEE Transactions on Mobile Computing, vol. 20, no. 3, pp. 1001-1016, 2019.

[20]

A. Zenati, N. Aouf, and O. Kechagias-Stamatis, "Distributed fuzzy semi-infinite auction based optimization for cooperative robots tasks allocation," in 2022 American Control Conference (ACC), 2022, pp. 1898--1903.

[21]

M. Lujak and M. Matezovic, "On efficiency in dynamic multi-robot task allocation." in AIRO@ AI* IA, 2020, pp. 49--53.

[22]

R. Chen, L. Cui, Y. Zhang, J. Chen, K. Yao, Y. Yang, C. Yao, and H. Han, "Delay optimization with fcfs queuing model in mobile edge computing-assisted uav swarms: A game-theoretic learning approach," in 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020, pp. 245--250.

[23]

Y. Wang, Z. Cai, Z.-H. Zhan, B. Zhao, X. Tong, and L. Qi, "Walrasian equilibrium-based multiobjective optimization for task allocation in mobile crowdsourcing," IEEE Transactions on Computational Social Systems, vol. 7, no. 4, pp. 1033-1046, 2020.

[24]

M. Kumar, S. C. Sharma, A. Goel, and S. P. Singh, "A comprehensive survey for scheduling techniques in cloud computing," Journal of Network and Computer Applications, vol. 143, pp. 1-33, 2019.

Digital Library

[25]

R. Benny and I. Wirawan, "Comparison analysis of round robin algorithm with highest response ratio next algorithm for job scheduling problems," International Journal of Open Information Technologies, vol. 10, no. 2, pp. 21-26, 2022.

[26]

Y. Chen, U. Rosolia, and A. D. Ames, "Decentralized task and path planning for multi-robot systems," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4337-4344, 2021.

[27]

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, "Playing atari with deep reinforcement learning," arXiv preprint arXiv:1312.5602, 2013.

[28]

T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, "Continuous control with deep reinforcement learning," arXiv preprint arXiv:1509.02971, 2015.

[29]

Y. Pan, S. Li, Q. Chen, N. Zhang, T. Cheng, Z. Li, B. Guo, Q. Han, and T. Zhu, "Efficient schedule of energy-constrained uav using crowdsourced buses in last-mile parcel delivery," Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 5, no. 1, mar 2021. [Online]. Available: https://doi.org/10.1145/3448079

Digital Library

[30]

C. Le Pape, "A combination of centralized and distributed methods for multi-agent planning and scheduling," in Proceedings., IEEE International Conference on Robotics and Automation, 1990, pp. 488-493 vol.1.

[31]

I. Lee and K. Lee, "The internet of things (iot): Applications, investments, and challenges for enterprises," Business Horizons, vol. 58, no. 4, pp. 431-440, 2015.

[32]

Ganti, Raghu, Fan, Lei, and Hui, "Mobile crowdsensing: current state and future challenges." IEEE Communications Magazine, 2011.

[33]

B. Guo, C. Chen, D. Zhang, Z. Yu, and A. Chin, "Mobile crowd sensing and computing," IEEE Communications Magazine, 2016.

Digital Library

[34]

Z. Jiang, H. Zhu, B. Zhou, C. Lu, M. Sun, X. Ma, X. Fan, C. Wang, and L. Chen, "Crowdpatrol: A mobile crowdsensing framework for traffic violation hotspot patrolling," IEEE Transactions on Mobile Computing, 2021.

[35]

M. W. Spong, S. Hutchinson, M. Vidyasagar et al., Robot modeling and control. Wiley New York, 2006, vol. 3.

[36]

K. Szocik and R. Abylkasymova, "Ethical issues in police robots. the case of crowd control robots in a pandemic," Journal of Applied Security Research, vol. 17, no. 4, pp. 530-545, 2022.

[37]

Z. Liu, B. Wu, and H. Lin, "Coordinated robot-assisted human crowd evacuation," in 2018 IEEE conference on decision and control (CDC). IEEE, 2018, pp. 4481--4486.

[38]

L. Kästner, B. Fatloun, Z. Shen, D. Gawrisch, and J. Lambrecht, "Human-following and-guiding in crowded environments using semantic deep-reinforcement-learning for mobile service robots," in 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 833--839.

[39]

C.-J. Ho and J. W. Vaughan, "Online task assignment in crowdsourcing markets," in Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, ser. AAAI'12. AAAI Press, 2012, p. 45--51.

[40]

M.-C. Yuen, I. King, and K.-S. Leung, "Task recommendation in crowdsourcing systems," in Proceedings of the First International Workshop on Crowdsourcing and Data Mining, ser. CrowdKDD '12. New York, NY, USA: Association for Computing Machinery, 2012, p. 22--26. [Online]. Available: https://doi.org/10.1145/2442657.2442661

Digital Library

[41]

E. P. de Freitas, M. Basso, A. A. S. da Silva, M. R. Vizzotto, and M. S. C. Corrêa, "A distributed task allocation protocol for cooperative multi-uav search and rescue systems," in 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 909--917.

[42]

Z. Chen, X. Xiong, W. Wang, Y. Xiao, and O. Alfarraj, "A blockchain-based multi-unmanned aerial vehicle task processing system for situation awareness and real-time decision," Sustainability, vol. 15, no. 18, 2023. [Online]. Available: https://www.mdpi.com/2071-1050/15/18/13790

[43]

N. A. Saadawy and S. Hady, "Gated community walkability design efficiency model," Civil Engineering and Architecture, vol. 10, pp. 189-213, 09 2022.

[44]

Z. Y. [WANG Xiao-ning, CUI Zi-yu, "Decision making of opening gated community considering traffic impact on environment," Journal of Transportation Systems Engineering and Information Technology, vol. 22, no. 4, pp. 228-235, 2022.

[45]

A. Odrowaz-Coates, "A gated community as a 'soft' and gendered total institution," International Sociology, vol. 30, no. 3, pp. 233-249, 2015.

[46]

T. Aeppel, "Why china may have the most factory robots in the world by 2017," Wall Street Journal, vol. 1, p. D1, 2015.

[47]

J. Robertson, "Robo sapiens japanicus: Humanoid robots and the posthuman family," Critical Asian Studies, vol. 39, no. 3, pp. 369-398, 2007.

[48]

C. J. Watkins and P. Dayan, "Q-learning," Machine learning, vol. 8, no. 3-4, pp. 279-292, 1992.

Digital Library

[49]

M. P. Deisenroth, G. Neumann, and J. Peters, "A survey on policy search for robotics," Foundations and Trends® in Robotics, vol. 2, no. 1-2, pp. 1-142, 2013.

Digital Library

[50]

V. R. Konda and J. N. Tsitsiklis, "Actor-critic algorithms," in Advances in Neural Information Processing Systems, 2000, pp. 1008--1014.

[51]

M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra, "Habitat: A platform for embodied ai research," in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9338--9346.

[52]

F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas, and H. Su, "Sapien: A simulated part-based interactive environment," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11 094-11 104.

Cited By

Hu CMa XHuang XShen YMa D(2024)LR-Auth: Towards Practical Implementation of Implicit User Authentication on EarbudsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997938:4(1-27)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699793
Li BRen YWang YYang J(2024)SpaceBeat: Identity-aware Multi-person Vital Signs Monitoring Using Commodity WiFiProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785908:3(1-23)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678590
Pettys-Baker RClarke MHolschuh BKostakos VKay JHoang T(2024)Functional Now, Wearable Later: Examining the Design Practices of Wearable TechnologistsProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676615(71-81)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676615
Show More Cited By

Index Terms

CrowdBot: An Open-Environment Robot Management System for On-Campus Services
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

Recommendations

Teleoperation System for Real World Robots - Adaptive Robot Navigation Based on Sensor Fusion
ICPADS '00: Proceedings of the Seventh International Conference on Parallel and Distributed Systems: Workshops

In this paper, we propose a teleoperation system with an autonomous robot, which is able to solve tasks even without a large load for the operator, and the system. Most teleoperation systems require skilled operators and expensive interfaces to solve ...
A biologically inspired method for robot navigation in a cluttered environment

The problem of wheeled mobile robot (WMR) navigation toward an unknown target in a cluttered environment has been considered. The biologically inspired navigation algorithm is the equiangular navigation guidance (ENG) law combined with a local obstacle ...
Vision-Based Humanoid Robot Navigation in a Featureless Environment
MCPR 2015: Proceedings of the 7th Mexican Conference on Pattern Recognition - Volume 9116

One of the most basic tasks for any autonomous mobile robot is that of safely navigating from one point to another e.g. service robots should be able to find their way in different kinds of environments. Typically, vision is used to find landmarks in ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 2

June 2024

1330 pages

EISSN:2474-9567

DOI:10.1145/3665317

Issue’s Table of Contents

Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 May 2024

Published in IMWUT Volume 8, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

NSF of China
the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City (University of Macau)
the University of Macau
the Science and Technology Development Fund, Macau SAR
the FuXiaQuan National Independent Innovation Demonstration Zone Collaborative Innovation Platform

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
358
Total Downloads

Downloads (Last 12 months)358
Downloads (Last 6 weeks)33

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hu CMa XHuang XShen YMa D(2024)LR-Auth: Towards Practical Implementation of Implicit User Authentication on EarbudsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997938:4(1-27)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699793
Li BRen YWang YYang J(2024)SpaceBeat: Identity-aware Multi-person Vital Signs Monitoring Using Commodity WiFiProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785908:3(1-23)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678590
Pettys-Baker RClarke MHolschuh BKostakos VKay JHoang T(2024)Functional Now, Wearable Later: Examining the Design Practices of Wearable TechnologistsProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676615(71-81)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676615
Leng ZJung MHwang SOh SZhang LPlötz TKim KKostakos VKay JHoang T(2024)Emotion Recognition on the Go: Utilizing Wearable IMUs for Personalized Emotion RecognitionCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678452(537-544)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678452
Fu YZhang YPan HLu YLi XChen LRen JLi XZhang XZhang Y(2024)Pushing the Limits of Acoustic Spatial Perception via Incident Angle EncodingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595838:2(1-28)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659583
Singhal YHonrales DWang HKim J(2024)Thermal In Motion: Designing Thermal Flow Illusions with Tactile and Thermal InteractionProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676460(1-13)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676460
Fu YZhang YLu YQiu LChen YWang YWang MLi YRen JZhang YOkoshi TKo JLiKamWa R(2024)Adaptive Metasurface-Based Acoustic Imaging using Joint OptimizationProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661863(492-504)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661863
Ruotsalo TTraver VKawala-Sterniuk ALeiva L(2024)Affective RelevanceIEEE Intelligent Systems10.1109/MIS.2024.339150839:4(12-22)Online publication date: 19-Apr-2024
https://dl.acm.org/doi/10.1109/MIS.2024.3391508

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents