Multi-Robot Task Allocation

Research investigations in the realm of micro-robotics often center around strategies addressing the multi-robot task allocation (MRTA) problem. Our contribution delves into the collaborative dynamics of micro-robots deployed in targeted hostile environments. Employing advanced algorithms, these robots play a crucial role in enhancing and streamlining operations within sensitive areas. We adopt a tailored GREEDY approach, strategically adjusting weight parameters in a multi-objective function that serves as a cost metric. The objective function, designed for optimization purposes, aggregates the cost functions of all agents involved. Our evaluation meticulously examines the MRTA efficiency for each micro-robot, considering dependencies on factors such as radio connectivity, available energy, and the absolute and relative availability of agents. The central focus is on validating the positive trend associated with an increasing number of agents constituting the cluster. Our methodology introduces a trio of micro-robots, unveiling a flexible strategy aimed at detecting individuals at risk in demanding environments. Each microrobot within the cluster is equipped with logic that ensures compatibility and cooperation, enabling them to effectively execute assigned missions. The implementation of MRTA-based collaboration algorithms serves as an adaptive strategy, optimizing agents' mobility based on specific criteria related to the characteristics of the target site.

Normally, the Linear Assignment Problem (LAP) has been solved by successful algorithms such as Lapjv and Munkres programmed as MATLAB codes. This study presented an improved algorithm for solving large scale LAP. A preprocessing (PP) algorithm was proposed to apply for constructing the k th transferred reduced cost matrix then this matrix was solved by Lapjvalgorithm. Performances of PP-Lapjvalgorithm were faster than theoriginal Lapjv about 1.90-8.20% when problem sizes are expanded from problem sizes 18000 to 34000 on integer number range [1,10] and [1,1000]. On the other hand, PP-Lapjvalgorithm was inefficient on integer number range [1, 1000000] due to more timeconsuming for executing Lapjv.m file in PP-Lapjvalgorithm. The enlargement of number ranges is influenced to the average computation time of Lapjvalgorithm raised about 53.63% and 32.05% when the range is expanded from [1,1000] to [1,1000000] on problem sizes 18000 and 30000,respectively. The limitations of problem size were determined by virtual memory of the tested computer that both algorithms enabled to solve at the maximum problem size of 34000.

A Content Distribution Network (CDN) can be defined as an overlay system that replicates copies of contents at multiple points of a network, close to the final users, with the objective of improving data access. CDN technology is widely used for the distribution of large-sized contents, like in video streaming. In this paper we address the problem of finding the best server for each customer request in CDNs, in order to minimize the overall cost. We consider the problem as a transportation problem and a distributed algorithm is proposed to solve it. The algorithm is composed of two independent phases: a distributed heuristic finds an initial solution that may be later improved by a distributed transportation simplex algorithm. It is compared with the sequential version of the transportation simplex and with an auction-based distributed algorithm. Computational experiments carried out on a set of instances adapted from the literature revealed that our distributed approach has a performance similar to its sequential counterpart, in spite of not requiring global information about the contents requests. Moreover, the results also showed that the new method outperforms the based-auction distributed algorithm.

Normally, the Linear Assignment Problem (LAP) has been solved by successful algorithms such as Lapjv and Munkres programmed as MATLAB codes. This study presented an improved algorithm for solving large scale LAP. A preprocessing (PP) algorithm was proposed to apply for constructing the k th transferred reduced cost matrix then this matrix was solved by Lapjvalgorithm. Performances of PP-Lapjvalgorithm were faster than theoriginal Lapjv about 1.90-8.20% when problem sizes are expanded from problem sizes 18000 to 34000 on integer number range [1,10] and [1,1000]. On the other hand, PP-Lapjvalgorithm was inefficient on integer number range [1, 1000000] due to more timeconsuming for executing Lapjv.m file in PP-Lapjvalgorithm. The enlargement of number ranges is influenced to the average computation time of Lapjvalgorithm raised about 53.63% and 32.05% when the range is expanded from [1,1000] to [1,1000000] on problem sizes 18000 and 30000,respectively. The limitations of problem size were determined by virtual memory of the tested computer that both algorithms enabled to solve at the maximum problem size of 34000.

Metareasoning refers to reasoning about one's own decision making process. This paper considers metareasoning about the decision making process in multi-agent settings. We present a multiagent metareasoning approach that enables a multi-agent team to select which task allocation algorithm to use as a function of changing communication quality level. Given a set of multi-agent task allocation algorithms, we synthesize a policy that prescribes the best algorithm to use among a predefined set of algorithms for a given communication level. Since each agent in the team runs the same policy, the team (or a part of the team) will collectively switch between task allocation algorithms as a function of the observed level of communication. We apply reactive synthesis to generate the policy from high-level specifications written in Linear Temporal Logic encoding the agents' switching behavior with respect to the state of the environment. We perform experiments in simulation to identify the best performing algorithms under different communication levels. The communication environment is modeled using the Rayleigh fading model and communication estimation is done through the exchange of heartbeat messages among agents. We test our metareasoning policy in three types of scenarios: search & rescue, fire monitoring, and ship protection scenarios. For each scenario, we demonstrate that our policy achieved better performance with respect to either max distance traveled, max number of transmitted messages or both compared to running any single algorithm.

In this paper, the static layout of a traditional multi-machine factory producing a set of distinct goods is integrated with a set of mobile production units - robots. The robots dynamically change their work position to increment the product rate of the different typologies of products in respect to the fluctuations of the demands and production costs during a given time horizon. Assuming that the planning time horizon is subdivided into a finite number of time periods, this particularly flexible layout requires the definition and the solution of a complex scheduling problem, involving for each  period of the planning time horizon, the determination of the position of the robots, i.e., the assignment to the respective tasks in order to minimize production costs given the product demand rates during the planning time horizon.
We propose a decentralized multi-agent system (MAS) scheduling model with as many agents as there are the tasks in the system, plus a resource (robot) owner which assigns the robots to the tasks in each time period on the basis of the requests coming from the competing task agents. The MAS model is coupled with an iterative auction based negotiation protocol to coordinate the agents’ decisions. The resource  prices are updated using a strategy inspired by the subgradient technique used in the Lagrangian relaxation approach. To measure the effectiveness of the results, the same are  evaluated in respect to that of the benchmark centralized model.

In this paper, we study a distributed intelligent multi-robot system (MRS) in assembly setting where robots have partially overlapping capabilities. We treat the problem of the system's self-(re)configurability and self-optimization. In this light, we propose a distributed and optimized robots configuration and scheduling system ORCAS which integrates the MRS configuration based on semantic descriptions with process scheduling.

In this work we address the Multi-Robot Task Allocation Problem (MRTA). We assume that the decision making environment is decentralized with as many decision makers (agents) as the robots in the system. To solve this problem, we developed a distributed version of the Hungarian Method for the assignment problem. The robots autonomously perform different substeps of the Hungarian algorithm on the base of the individual and the information received through the messages from the other robots in the system. It is assumed that each robot agent has an information regarding its distance from the targets in the environment. The inter-robot communication is performed over a connected dynamic communication network and the solution to the assignment problem is reached without any common coordinator or a shared memory of the system. The algorithm comes up with a global optimum solution in O(n3) cumulative time (O(n2) for each robot), with O(n3) number of messages exchanged among the n robots.

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.
Highlights

► Factories producing different goods with additional mobile resources (robots). ► A two-level multi-agent system framework for production planning and scheduling. ► First: allocate robots to fulfill demands with minimum production cost. ► An iterative auction method inspired by Lagrangean relaxation is adopted. ► Second: robots (re)schedule themselves with a distributed version of Hungarian method.