Mixed-Integer and Constraint Programming Techniques for Mobile Robot Task Planning

Booth, Kyle E. C.; Tran, Tony; Nejat, Goldie; Beck, J. Christopher

doi:10.1109/lra.2016.2522096

Cited by 36 publications

(19 citation statements)

References 26 publications

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“…Multi-robot task allocation (MRTA) is often formulated as a multiple traveling salesman problem (mTSP) [6], a variant of the NP-hard traveling salesman problem (TSP), for which the branch and bound paradigm is commonly used to generate optimal solutions [7]. Large scale TSPs are computationally intractable, and, consequently, popular solution methods employ heuristics-based approximations of linear programs [8] and mixed integer linear programs (MILPs) [9]. For example, a Monte Carlo tree search-based heuristic was used in conjunction with the branch and bound algorithm to yield near optimal robot task allocation for 100 tasks within an hour [10].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

Tereshchuk

Stewart

Bykov

et al. 2019

IEEE Robot. Autom. Lett.

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Efficient utilization of cooperating robots in the assembly of aircraft structures relies on balancing the workload of the robots and ensuring collision-free scheduling. We cast this problem as that of allocating a large number of repetitive assembly tasks, such as drilling holes and installing fasteners, among multiple robots. Such task allocation is often formulated as a Traveling Salesman Problem (TSP), which is NPhard, implying that computing an exactly optimal solution is computationally prohibitive for real-world applications. The problem complexity is further exacerbated by intermittent robot failures necessitating real-time task reallocation. In this letter, we present an efficient method that exploits workpart geometry and problem structure to initially generate balanced and conflict-free robot schedules under nominal conditions. Subsequently, we deal with the failures by allowing the robots to first complete their nominal schedules and then employing a market-based optimizer to allocate the leftover tasks. Results show an improvement of 11.5% in schedule efficiency as compared to an optimized greedy multi-agent scheduler on a four robot system, which is especially promising for aircraft assembly processes that take many hours to complete. Moreover, the computation times are similar and small, typically hundreds of milliseconds.

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Section: Related Workmentioning

confidence: 99%

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

Tereshchuk

Stewart

Bykov

et al. 2019

IEEE Robot. Autom. Lett.

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“…Constraints (7) and (8) represent the time requirements for starting the task, and the execution task must be performed within the scope of the execution. Constraints (9) and (10) define that all mobile robots are in the warehouse position during the initial phase. The constraint (11) guarantees that each feeding operation is only allowed to be executed once.…”

Section: Distance Between Warehouse and Production Linesmentioning

confidence: 99%

“…Flushing et al 9 studied the task assignment of mobile robot team, data routing, and road transmission problems and considered the task and actual data transmission within the scope of planning and scheduling. Booth et al 10 solved the problem of single-robot task planning using mixedinteger programming method and constraint programming method. Research of this kind of problem has certain similarities with satellite mission planning and flight scheduling, and related research can be used as a reference.…”

Section: Introductionmentioning

confidence: 99%

Multi-mobile robots and multi-trips feeding scheduling problem in smart manufacturing system: An improved hybrid genetic algorithm

Yao

Song

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

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Modern manufacturing systems require timely and efficient production tasks. Any mistakes can have serious consequences which effect the production process obviously. The supply of goods is the beginning of the production process, ensuring that production can proceed normally. Using mobile robots for transportation and supply of production lines can achieve automatic manufacturing. We studied the use of multiple mobile robots to supply multiple production lines. Robots need to return to warehouse when no goods exist. This problem is called a multi-mobile robots and multi-trips feeding scheduling problem. We constructed a mathematical model describing multi-mobile robots and multi-trips feeding scheduling problem, and the objective function is to minimize the transportation cost and waiting cost. To solve this problem, we proposed an improved hybrid genetic algorithm, where a strategy of mixing improved genetic algorithm and tabu search algorithm is adopted to find robots with reasonable routes. Combining genetic algorithm with tabu search algorithm can improve the route planning effect and find a lower cost solution. In the experimental part, it is verified that the proposed algorithm could effectively find reasonable ways for robots to provide services. We also put forward suggestions for the scenarios of using robots in actual production.

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“…However, the planning and scheduling are managed independently and the user schedules are not considered as constraints on the robots' tasks. Our previous work studied a similar system, but with a single robot and different restrictions on activities (Booth et al, 2016). All HRI activities in that work needed to be performed and the users associated with the activities were already known.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…To address the lack of human resources, human-robot interaction (HRI) and robot companionship have been proposed and shown to have positive results on the human psychological state (Banks, Willoughby, & Banks, 2008;Turkle, 2006). To that end, we have undertaken a long-term study on the deployment of intelligent humanlike mobile robots in retirement homes to assist and interact with elderly residents (Booth, Tran, Nejat, & Beck, 2016;Louie, Vaquero, Nejat, & Beck, 2014;Louie, Li, Vaquero, & Nejat, 2014Mohamed & Nejat, 2016).…”

Section: Introductionmentioning

confidence: 99%

Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots

Tran¹,

Vaquero²,

Nejat³

et al. 2017

jair

Self Cite

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This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the "model-and-solve" functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the modeland-solve paradigm. It would be misleading to conclude that constraint programming is "better" than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.

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Mixed-Integer and Constraint Programming Techniques for Mobile Robot Task Planning

Cited by 36 publications

References 26 publications

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

Multi-mobile robots and multi-trips feeding scheduling problem in smart manufacturing system: An improved hybrid genetic algorithm

Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots

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