Simultaneous Task Allocation and Planning Under Uncertainty

Faruq, Fatma; Parker, David; Laccrda, Bruno; Hawes, Nick

doi:10.1109/iros.2018.8594404

Cited by 36 publications

(20 citation statements)

References 18 publications

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“…This MITL approach allowed agents to have both agentspecific goals and global goals that need to be satisfied by all the team's agents. Faruq et al (2018) building on the work of Schillinger et al (2018a) presented an algorithm that uses an LTL and Markov decision process (MDP) approach to solve the MAP problem in uncertain environments.…”

Section: Simultaneous Task Allocation and Planningmentioning

confidence: 99%

“…� The free configuration space of each robot is static and does not change when robots move objects in the environment. It is assumed that modern motion controllers can handle avoiding collisions with moved objects (Faruq et al, 2018;Lo et al, 2018;Schillinger et al, 2018a). � Motion planning only considers collisions with the environment when planning paths for robots and collisions.…”

Section: Algorithmic Assumptionsmentioning

confidence: 99%

“…It does not consider robot on robot collisions. It is assumed that modern motion controllers can handle avoiding collisions with other robots (Faruq et al, 2018;Lo et al, 2018;Schillinger et al, 2018a).…”

Section: Algorithmic Assumptionsmentioning

confidence: 99%

See 2 more Smart Citations

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling

Messing

Neville

Chernova

et al. 2021

The International Journal of Robotics Research

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Effective deployment of multi-robot teams requires solving several interdependent problems at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must answer four important questions: what (task planning), how (motion planning), who (task allocation), and when (scheduling). Although there are rich bodies of work dedicated to various combinations of these questions, a fully integrated treatment of all four questions lies beyond the scope of the current literature, which lacks even a formal description of the complete problem. In this article, we address this absence, first by formalizing this class of multi-robot problems under the banner Simultaneous Task Allocation and Planning with Spatiotemporal Constraints (STAP-STC), and then by proposing a solution that we call Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (GRSTAPS). GRSTAPS interleaves task planning, task allocation, scheduling, and motion planning, performing a multi-layer search while effectively sharing information among system modules. In addition to providing a unified solution to STAP-STC problems, GRSTAPS includes individual innovations both in task planning and task allocation. At the task planning level, our interleaved approach allows the planner to abstract away which agents will perform a task using an approach that we refer to as agent-agnostic planning. At the task allocation level, we contribute a search-based algorithm that can simultaneously satisfy planning constraints and task requirements while optimizing the associated schedule. We demonstrate the efficacy of GRSTAPS using detailed ablative and comparative experiments in a simulated emergency-response domain. Results of these experiments conclusively demonstrate that GRSTAPS outperforms both ablative baselines and state-of-the-art temporal planners in terms of computation time, solution quality, and problem coverage.

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Section: Simultaneous Task Allocation and Planningmentioning

confidence: 99%

Section: Algorithmic Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling

Messing

Neville

Chernova

et al. 2021

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…The authors handled constraints like resources in the LTL formulation. Faruq et al [16] further incorporated uncertainties in their modelling, while trying to compute a policy that maximizes the probability of completion of the mission. To make the approach computationally efficient, the problem was solved through local Markov Decision Processes for the individual robots with a switching mechanism for task allocations.…”

Section: Related Workmentioning

confidence: 99%

Multi-robot mission planning using evolutionary computation with incremental task addition

Kala

2021

Intel Serv Robotics

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Mission planning asks the robot to solve complex missions, requiring the robot to execute several actions such that a complex goal condition (called mission) is satisfied. The missions can be specified by using Linear Temporal Logic or any custom domain-specific language. Here, we consider service robots in the home or office environments with multiple users, each specifying a task that together makes a mission. Conventional mission planning techniques assume that the complete mission specification is known in advance, while mission planning is incremental wherein tasks keep getting added and deleted. The exponential computational complexity of the current model verification-based approaches is not a viable solution. The paper presents an evolutionary framework for incrementally solving the problem of mission planning for the problems for which a polynomial-time verification system exists for the tasks. The optimal solution is represented in an encoding-specific format that is compiled to form a linear trajectory of the robots. Optimization is done as the robot operates. Thus, the robot at any time has a partial solution to every task that has already been executed that (along with the robot assigned to the task) cannot be altered, while a part representing the next steps of the robot to be optimized. This acts as a constraint in the continuous optimization. The proposed approach uses Dynamic Programming to integrate the solutions of tasks and as a distance function in the diversity preserving evolutionary computation since the mission constantly changes. Comparisons are made with a baseline evolutionary computation without Dynamic Programming, a non-incremental version of the proposed algorithm, and several greedy strategies. The proposed algorithm is seen to perform better than all other methods. Further, the algorithm is implemented and demonstrated on a Pioneer LX robot.

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“…Ding et al 37 present probabilistically guaranteed optimal control policies over an infinite time horizon for an MDP system with LTL constraints seeking to minimize the average cost per cycle starting from the initial state. Faruq et al 38 develop simultaneous task allocation and planning for multi-robot systems which are operating in uncertain environments and prone to failures. The robotic system is modeled as an MDP, and the effect of increasing the number of failure points on computational time and reallocation frequency is studied.…”

Section: Handling Stochastic Eventsmentioning

confidence: 99%

Modeling and verification of contingency resolution strategies for multi-robot missions using temporal logic

Shriyam

Gupta

2019

International Journal of Advanced Robotic Systems

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This article presents an approach for assessing contingency resolution strategies using temporal logic. We present a framework for nominal mission modeling, then specifying contingency resolution strategies and evaluating their effectiveness for the mission. Our approach focuses on leveraging the use of model checkers to the domain of multi-robot missions to assess the adequacy of contingency resolution strategies that minimize the adverse effects of contingencies on the mission execution. We consider missions with deterministic as well as probabilistic transitions. We demonstrate our approach using two case studies. We consider the escorting of a ship in a port where multiple contingencies may occur concurrently and assess the adequacy of the proposed contingency resolution strategies. We also consider a manufacturing scenario where multiple assembly stations collaborate to create a product. In this case, assembly operations may fail, and human intervention is needed to complete the assembly process. We investigate several different strategies and assess their effectiveness based on mission characteristics.

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Simultaneous Task Allocation and Planning Under Uncertainty

Cited by 36 publications

References 18 publications

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling

Multi-robot mission planning using evolutionary computation with incremental task addition

Modeling and verification of contingency resolution strategies for multi-robot missions using temporal logic

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