When robots work together as a team, the members that perform each task should be the ones that promise to use the least resources to do the job.

Dynamic task allocation is an essential requirement for multi-robot systems functioning in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of individual robots and the collective behavior. We analyze the effect that the number of observations and the choice of decision functions have on the performance of the system. We validate the mathematical models on a multi-foraging scenario in a multi-robot system. We find that the model’s predictions agree very closely with experimental results from sensor-based simulations. 1

Abstract—As the community strives towards autonomous multirobot systems, there is a need for these systems to autonomously form coalitions to complete assigned missions. Numerous coalition formation algorithms have been proposed in the software agent literature. Algorithms exist that form agent coalitions in both super additive and non-super additive environments. The algorithmic techniques vary from negotiation-based protocols in multi-agent system (MAS) environments to those based on computation in distributed problem solving (DPS) environments. Coalition formation behaviors have also been discussed in relation to game theory. Despite the plethora of MAS coalition formation literature, to the best of our knowledge none of the proposed algorithms have been demonstrated with an actual multi-robot system. There exists a discrepancy between the multi-agent algorithms and their applicability to the multi-robot domain. This paper aims to bridge that discrepancy by unearthing the issues that arise while attempting to tailor these algorithms to the multi-robot domain. A well-known multi-agent coalition formation algorithm has been studied in order to identify the necessary modifications to facilitate its application to the multi-robot domain. This paper reports multi-robot coalition formation results based upon simulation and actual robot experiments. A multi-agent coalition formation algorithm has been demonstrated on an actual robot system. Index Terms—Coalition formation, coalition imbalance, task allocation.

A key challenge in deploying teams of robots in real-world applications is to automate the control of teamwork, such that the designer can focus on the taskwork. Existing teamwork architectures seeking to address this challenge are monolithic, in that they commit to interaction protocols at the architectural level, and do not allow the designer to mix and match protocols for a given task. We present BITE, a behaviorbased teamwork architecture that automates collaboration in physical robots, in a distributed fashion. BITE separates task behaviors that control a robot's interaction with its task, from interaction behaviors that control a robot's interaction with its teammates. This distinction provides for flexibility and modularity in terms of the interactions used by teammates to collaborate effectively. It also allows BITE to synthesize and significantly extend existing teamwork architectures. BITE also incorporates key lessons learned in applying multi-agent teamwork architectures in physical robot teams. We present empirical results from experiments with teams of Sony AIBO robots executing BITE, and discuss the lessons learned.

In this article, we analyze the behavior of a group of robots involved in an object retrieval task. The robots ’ control system is inspired by a model of ants ’ foraging. This model emphasizes the role of learning in the individual. Individuals adapt to the environment using only locally available information. We show that a simple parameter adaptation is an effective way to improve the efficiency of the group and that it brings forth division of labor between the members of the group. Moreover, robots that are best at retrieving have a higher probability of becoming active retrievers. This selection of the best members does not use any explicit representation of individual capabilities. We analyze this system and point out its strengths and its weaknesses.

Abstract — This paper describes a general approach for automatically synthesizing task solutions for heterogeneous robot teams. In particular, our approach enables multiple robots to coalesce into teams to solve a task through tightly-coupled sensor sharing. Instead of designing special solution strategies for the team, our ASyMTRe approach enables the robot team to generate solutions autonomously according to the current robot team composition. In this paper, we first formulate the problems that the ASyMTRe approach addresses, and then present the anytime ASyMTRe configuration algorithm. We prove that the configuration algorithm is correct, and is guaranteed to find the optimal solution given enough time. Empirical results are also presented validating this analysis, and showing that the ASyMTRe configuration algorithm has good scalability and can quickly find a good solution with the solution quality increasing as additional planning time is available. By analyzing the configuration algorithm, we show that ASyMTRe is applicable to a large class of challenging multi-robot problems. I.

Abstract — This work presents the evaluation of two mission specification and task allocation architectures. These architectures, described in part 1 of this paper, present novel means with which to integrate a case-based reasoning (CBR) mission planner with contract net protocol (CNP) based task allocation. In the first design, the CBR and runtime-CNP architecture, the case-based mission planner generates mission plans that support necessary behaviors for CNP-based task allocation and execution. In the second design, the CBR and premission-CNP architecture, task allocation takes place during mission specification. The results of an empirical evaluation of the CBR and runtime-CNP across three naval scenarios is described. Finally, we briefly describe an earlier usability evaluation of the CBR and premission-CNP architecture using goals, operators, methods, and selection rules modeling. I.

In this paper we compare the costs and benefits of market-based and thresholdbased approaches to task allocation in real world conditions, where information and communication may be limited or inaccurate. We have performed extensive comparative experiments in an event-handling domain. Our results indicate that when information is accurate, market-based approaches are more efficient; when it is not, threshold-based approaches offer the same quality of allocation at a fraction of the expense. Additionally, both approaches are robust to low communication and task perception ranges in our experimental domain. 1

Coordinated multi-robot systems (MRS) have been successfully demonstrated in a variety of task domains. However, the design of these systems is typically ad hoc and centered around resource-intensive trial-and-error design processes. To fully exploit the power and potentials of MRS, the community is in need of additional principled design methodologies. In this paper, we present an overview of our ongoing work on three different principled methodologies, each of which addresses a different type of MRS, based on the scale of the system or mechanisms by which coordination is achieved. First, we present a formal study and analysis of task allocation in MRS, including a domain-independent taxonomy of multi-robot task allocation (MRTA) problems. Second, we present a principled controller design methodology for MRS executing sequential tasks. This design methodology provides a formal framework which serves as the foundation for an integrated set of systematic MRS controller synthesis and analysis methods. Third, we discuss the application of a formal analytical approach to the study of large-scale MRS motivated by work from the physics community. Through the use of principled methods such as these, we hope to provide a formal footing for the design of MRS. I.

An original Reinforcement Learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. To that end, we design simple reactive agents in a decentralized way as independent learners. But to cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face a sequence of progressively more complex tasks. We illustrate this general framework by computer experiments where agents have to coordinate to reach a global goal.