Consensus-based bundle algorithm with local replanning for heterogeneous multi-UAV system in the time-sensitive and dynamic environment

Chen, Jie; Qing, Xianguo; Ye, Fang; Xiao, Kai; You, Kai; Sun, Qian

doi:10.1007/s11227-021-03940-z

Cited by 31 publications

(26 citation statements)

References 25 publications

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“…MRTA is solved by uniquely assigning a subset of robots to optimize the completion of an objective [13]. Authors in [14] include connection limitations and allocate tasks to connected robots using a bundling algorithm. Another example in [15] allocated targets to individual teams and plans rendezvous with team members to reduce uncertainty of targets over time.…”

Section: Related Workmentioning

confidence: 99%

Coordinated Multi-Agent Exploration, Rendezvous, & Task Allocation in Unknown Environments with Limited Connectivity

Bramblett

Peddi

Bezzo

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem). In this work we tackle the latter problem and notice that most stateof-the-art methods assume that robots will be able to execute a predetermined plan; however system failures and changes in environmental conditions can cause the robots to deviate from the plan with cascading effects across the multi-robot system. This paper proposes a coordinated epistemic prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications. Dynamic epistemic logic is the principal component implemented to allow robots to propagate belief states and empathize with other agents. Propagation of belief states and subsequent coverage of the environment is achieved via a frontier-based method within an artificial physics-based framework. The proposed framework is validated with both simulations and experiments with unmanned ground vehicles in various cluttered environments.

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Section: Related Workmentioning

confidence: 99%

Coordinated Multi-Agent Exploration, Rendezvous, & Task Allocation in Unknown Environments with Limited Connectivity

Bramblett

Peddi

Bezzo

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…A task window refers to the period between the earliest and latest times a task may be started. Within a task window, the different execution times of each task will influence the probability of each task being executed [35]. Within the task window, different task execution schedules will cause the interval between execution times of adjacent tasks to be too long or too short, thus making the overall task execution sequence too close or too sparse, which can also affect the overall task execution benefits.…”

Section: Task Allocation Adjustment Based On Time Windowsmentioning

confidence: 99%

Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases

Wang

et al. 2022

Aerospace

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Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.

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“…Their proposal enables robots to reset a portion of their previous allocation and replan as tasks arrive. Recently, Chen et al [99] implemented a replanning approach based on CBBA that considered tasks with timing and capability constraints.…”

Section: Market-based Approachesmentioning

confidence: 99%

Task Allocation Using a Team of Robots

et al. 2022

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Purpose of Review Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. We present a survey of multi-robot task allocation covering many problem variants and solution approaches, both centralized and decentralized. Recent Findings In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research and recent progress in these directions, which are captured in this survey, along with similar examples for the various solutions that have been developed to solve such problems. Summary We first present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate, and we describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.

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Consensus-based bundle algorithm with local replanning for heterogeneous multi-UAV system in the time-sensitive and dynamic environment

Cited by 31 publications

References 25 publications

Coordinated Multi-Agent Exploration, Rendezvous, & Task Allocation in Unknown Environments with Limited Connectivity

Coordinated Multi-Agent Exploration, Rendezvous, & Task Allocation in Unknown Environments with Limited Connectivity

Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases

Task Allocation Using a Team of Robots

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