Abstract
We study the Transport and Pick Robots Task Scheduling (TPS) problem, in which two teams of specialized robots, transport robots and pick robots, collaborate to execute multi-station order fulfillment tasks in logistic environments. The objective is to plan a collective time-extended task schedule with the minimization of makespan. However, for this recently formulated problem, it is still unclear how to obtain satisfying results efficiently. In this research, we design several constructive heuristics to solve this problem based on the introduced sequence models. Theoretically, we give time complexity analysis or feasibility guarantees of these heuristics; empirically, we evaluate the makespan performance criteria and computation time on designed dataset. Computational results demonstrate that coupled append heuristic works better for the most cases within reasonable computation time. Coupled heuristics work better than decoupled heuristics prominently on instances with relative few pick robot numbers and large work zones. The law of diminishing marginal utility is also observed concerning the overall system performance and different transport-pick robot numbers.
Highlights
IntroductionTargeting the task allocation and scheduling aspect of the HROFS, we formulate the Transport and Pick Robots Task Scheduling (TPS) problem, which seeks the collective time-extended task schedule for multiple transport robots and multiple pick robots collaboratively performing order fulfillment tasks
In the Heterogeneous Robotic Order Fulfillment System (HROFS), two types of robots with specialized and complementary capabilities exist: 1) transport robots with object transfer and transient storage capability, typically automated guided vehicles (AGVs) or autonomous mobile robots (AMRs), that autonomously receive order fulfillment tasks, travel across the workspace and retrieve items from different storage stations and 2) pick robots with mobile manipulation capability, typically mobile manipulators, mobile dual-arm robots, hybrid leg-wheel robots or even human workers enhanced by mobile platforms (onlyTargeting the task allocation and scheduling aspect of the HROFS, we formulate the Transport and Pick Robots Task Scheduling (TPS) problem, which seeks the collective time-extended task schedule for multiple transport robots and multiple pick robots collaboratively performing order fulfillment tasks
For instances with n = 8, STD works best; in contrast for n = 16, 32, LPT works best. This is because for smaller pick robot numbers, both pick robots’ and transport robots’ routing components are dominant, and STD rule could reduce the time difference that is required for the transport-pick robot pair rendezvous
Summary
Targeting the task allocation and scheduling aspect of the HROFS, we formulate the Transport and Pick Robots Task Scheduling (TPS) problem, which seeks the collective time-extended task schedule for multiple transport robots and multiple pick robots collaboratively performing order fulfillment tasks. In the TPS problem, as customer orders are characterized by multiple lines of miscellaneous items, both two robot types have to travel to different locations to collect items or perform pick&place operations. According to the multi-robot task allocation (MRTA) taxonomy iTax [2], the TPS problem falls in the complexschedule [CD] category, with single-task robots [ST], multiple-robot tasks [MR], and the time-extended allocation [TA] problem (CD[ST-MR-TA]). The robot team comprises of multiple unmanned fire trucks and multiple debris cleaning bulldozers They propose two methods, a hybrid method incorporating tiered auctions and two heuristic techniques, clustering and opportunistic path planning. Strengths and weakness of both methods are analyzed and verified by simulations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
