Multi-Robot Task Allocation: A Spatial Queuing Approach

Multi-Robot Task Allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to match a set of robots to a set of tasks so that the tasks can be completed by the robots while optimizing a certain metric such as the time required to complete all tasks, distance traveled by the robots and energy expended by the robots. We consider a scenario where the tasks can appear dynamically and the location of tasks are not known a priori by the robots. Additionally, for a task to be completed, it needs to be performed by multiple robots. This setting is called the MR-ST-TA (multi-robot, single-task, time- extended assginment) category of MRTA; solving the MRTA problem for this category is a known NP-hard problem. In this thesis, we address this problem by proposing a new algorithm that uses a spatial queue-based model to allocate tasks between robots while comparing its performance to several other known methods. We have implemented these algorithms on an accurately simulated model of Corobot robots within the Webots simulator for different numbers of robots and tasks. The results show that our method is adept in all proffered environments, especially scenarios that benefit from path planning, whereas other methods display inherent weakness at one end of the spectrum: a decentralized greedy approach exhibits inefficient behavior as the robot to task ratio dips below one, whereas the Hungarian method (an offline algorithm) fails to keep pace as the robot count increases

Learning task performance in market-based task allocation

© 2012 Springer-Verlag. The original publication is available at www.springerlink.com.Presented at the 12th International Conference on Intelligent Autonomous Systems (IAS-12) held June 26-29, 2012, Jeju Island, Korea.DOI: 10.1007/978-3-642-33932-5_57Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. An approach to learning team member performance is presented, which enables more efficient task assignment. A policy gradient reinforcement learning algorithm is used to learn a cost factor that can be applied individually to auction bids. Experimental results demonstrate that agents that model team member performance using this approach can more efficiently distribute tasks in multi-agent auctions

AS-470-96 Resolution on 1995-96 Program Review and Improvement Committee Report of Findings and Recommendations

Accepts 1995-96 PRAIC report of program findings and recommendations

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Building a resilient skills ecosystem: situational analysis report on the forward path of education and training in light of the pandemic and 4IR/5IR shifts in the ETD sector: higher education subsector

The Coronavirus (COVID-19) pandemic has significantly disrupted various sectors, including the education and training sector, posing unprecedented challenges to skills development and workforce readiness. The Education, Training, and Development Practices Sector Education and Training Authority (ETDP SETA) commissioned the current study to assess the implications of COVID-19 on its constituencies. Understanding the intersection of the 4IR and the pandemic is crucial, given the dynamic shifts in technology, skills demand, and learning paradigms. This report delves into the profound impact of the COVID-19 pandemic within the context of the Fourth Industrial Revolution (4IR) on the Higher Education subsector.