Applications of DEC-MDPs in multi-robot systems

International audienceOptimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera.In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We rst describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decen- tralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed

Multi-robot task allocation system to improve assistance in domestic scenarios

The AURORA project aims at developing new strategies to take assistive robotics a step further. In order to provide extended services to potential users, engaging a team of simpler robots is often preferable to using a unique, super-capable robot. In such multi-robot systems, the coordination for task execution is one of the major challenges to overcome. The present thesis proposes a solution to the specific issue of multi-robot task allocation within an heterogeneous team of robots, with additional inter-task precedence constraints. The main elements of the state of the art that support this project are reported, including useful taxonomies and existing methods. From the analyzed solutions, the one that better fits with the constraints of the project is an iterated auction-based algorithm able to manage precedence constraints, which has been modified to handle heterogeneity and partial scheduling. The selected solution has been designed and implemented with the particular purpose of being applied to the robotized kitchen setup of the AURORA project; it is however flexible and scalable and can therefore be applied to other use-cases, for instance vehicle-routing problems. Several evaluation scenarios have been tested, that demonstrate the good functioning and characteristics of the system, as well as the possibility to integrate humans into the task assignation process

Auction-Based Task Allocation and Motion Planning for Multi-Robot Systems with Human Supervision

This paper presents a task allocation strategy for a multi-robot system with a human supervisor. The multi-robot system consists of a team of heterogeneous robots with different capabilities that operate in a dynamic scenario that can change in the robots’ capabilities or in the operational requirements. The human supervisor can intervene in the operation scenario by approving the final plan before its execution or forcing a robot to execute a specific task. The proposed task allocation strategy leverages an auction-based method in combination with a sampling-based multi-goal motion planning. The latter is used to evaluate the costs of execution of tasks based on realistic features of paths. The proposed architecture enables the allocation of tasks accounting for priorities and precedence constraints, as well as the quick re-allocation of tasks after a dynamic perturbation occurs –a crucial feature when the human supervisor preempts the outcome of the algorithm and makes manual adjustments. An extensive simulation campaign in a rescue scenario validates our approach in dynamic scenarios comprising a sensor failure of a robot, a total failure of a robot, and a human-driven re-allocation. We highlight the benefits of the proposed multi-goal strategy by comparing it with single-goal motion planning strategies at the state of the art. Finally, we provide evidence for the system efficiency by demonstrating the powerful synergistic combination of the auction-based allocation and the multi-goal motion planning approach

Evaluation of control systems for automated aircraft wing manufacturing

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; in conjunction with the Leaders for Global Operations Program at MIT, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesisIncludes bibliographical references (p. 62-64).The Boeing Company is looking to bring aircraft manufacturing technology into the 21st century. As part of this process, several projects have been started to develop the technologies required to achieve Boeing's vision for the future of aircraft manufacturing. To date, much of this work has focused on hardware, including robotic and other automation technologies. However, in order to use this hardware, a significant effort must also be made in the area of factory control and coordination. This thesis advances knowledge in this area by evaluating the suitability of different control system approaches for aircraft wing box assembly. First, general classes of control systems are discussed and several criteria are proposed for evaluating their performance in an aircraft manufacturing environment. The current wing box assembly process is then examined in order to develop simplified but representative task networks to which various algorithms can be applied. The Tercio algorithm, developed at MIT, is used to generate schedules for several problem structures of interest in order to characterize the algorithm's performance in this context. The Tercio algorithm is then benchmarked against the Aurora scheduling tool, showing that Tercio can generate more efficient schedules than Aurora, but at the cost of increased computation time. Next, management considerations with respect to product design, manufacturing technology development, and implementation associated with advanced manufacturing technologies are discussed. Finally, recommendations are provided for how Boeing can accelerate the development of useful and practical advanced, automated manufacturing systems.by Jason Herrera.S.M.M.B.A

A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots