Generating Task Reallocation Suggestions to Handle Contingencies in Human-Supervised Multi-Robot Missions

被引:14
作者
Al-Hussaini, Sarah [1 ]
Gregory, Jason M. [2 ]
Gupta, Satyandra K. [1 ]
机构
[1] Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90007 USA
[2] DEVCOM Army Res Lab, Adelphi, MD 20783 USA
关键词
Task analysis; Robots; Contingency management; Probabilistic logic; Resource management; Relays; Uncertainty; Multi-robot task allocation; mission planning; contingency management; heuristic algorithms; ALLOCATION; PERFORMANCE; TAXONOMY;
D O I
10.1109/TASE.2022.3227415
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In a mission with significant uncertainty due to intermittent communications, delayed information flow, and robotic failures, the role of human supervisors is extremely challenging. As and when any new information arrives, humans must infer both the existing and predicted future states, identify potential contingencies, and update task assignments to robots rapidly. We propose methodologies for automated generation of task reallocation suggestions to humans to assist in the decision-making process. Our generated robot retasking plan minimizes a modified makespan of the mission, which incorporates task criticality and penalty for incomplete tasks. The plan considers the effects of potential mission contingencies on the tasks, the robots, and the future performance of the robots operating based on the previous task plans. Our method includes the incorporation of two optional tasks, i.e., relay and robot rescue, for performance improvement. The rescue task has probabilistic outcomes affecting the team size. One or more rescues are incorporated in a way that can minimize the expected value of the modified makespan overall possibilities of rescue outcomes. We have conducted performance evaluation using simulation, demonstrating the value of the optional tasks and performance enhancement using our method of incorporating them.Note to Practitioners-The work reported in this paper will be useful in applications where a team of agents is deployed to carry on a large-scale mission with communication constraints where the number of functional agents can change probabilistically. Typically, such uncertainty is encountered in applications that are challenging or dangerous in nature. Agents can have non-zero probabilities to fail while doing certain risky tasks and to get recovered by other agents. The proposed centralized multi-agent task reallocation method can help in proactively addressing potential contingencies in surveillance, search and rescue, or disaster relief to support resilient operations while having a supervisor in the higher chain of command.
引用
收藏
页码:367 / 381
页数:15
相关论文
共 48 条
[41]  
Shriyam S, 2018, PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2018, VOL 5A
[42]  
Shriyam S, 2018, IEEE INT CONF ROBOT, P3709
[43]   Performance-effective and low-complexity task scheduling for heterogeneous computing [J].
Topcuoglu, H ;
Hariri, S ;
Wu, MY .
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2002, 13 (03) :260-274
[44]   Learning Scheduling Policies for Multi-Robot Coordination With Graph Attention Networks [J].
Wang, Zheyuan ;
Gombolay, Matthew .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2020, 5 (03) :4509-4516
[45]  
Yang F, 2020, IEEE INT CONF ROBOT, P6661, DOI [10.1109/icra40945.2020.9197354, 10.1109/ICRA40945.2020.9197354]
[46]  
Zhang B., 2020, INT J PROGN HEALTH M, V5, P1
[47]  
Zhang Cong, 2020, Advances in Neural Information Processing Systems, V33
[48]  
Zhang Y, 2013, IEEE INT CONF ROBOT, P2992, DOI 10.1109/ICRA.2013.6630992