Real-Time Support for Mobile Robotics

Proceedings of International Conference on Robotics and Automation, 1997

We present and discuss the use of a generic scheme for multi-robot cooperation called the \Plan-Merging Paradigm" for managing a large eet of autonomous mobile robots. Each robot, autonomously and incrementally builds and executes its own plans taking into account the multi-robot context obtained by collecting the current plans and goals of the other robots. We describe the overall system architecture and discuss the properties of our cooperative s c heme. We show h o w the Plan-Merging Paradigm (PMP) can be used in a hierarchical manner and how it \ lls the gap" between centralized planning and distributed execution. We nally illustrate this scheme through an implemented system which allows a eet of autonomous mobile robots to perform load transfer tasks in a route network environment. The central activity is limited to task allocation and important gains are obtained in system exibility and robustness to execution contingencies. Simulations (using up to 30 robots) as well as experiments with real robots (3) are presented and discussed.

Our work is motivated by mobile robotic applications where a team of autonomous robots cooperate in achieving a goal, e.g., using sensor feeds to locate trapped humans in a building on fire. To attain the goal, (a) kinematic Line-Of-Sight (LOS) constraints must be enforced to build interference free communication paths, and (b) precedence, communication and location constrained tasks must be allocated and scheduled across the processing entities in a predictable and scalable fashion.

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018

Field multi-robot missions face numerous unavoidable disturbances, such as delays in executing tasks and intermittent communications. Coping with such disturbances requires to endow the robots with high-level decision skills. We present a distributed decision architecture based first on a hybrid planner that can manage decentralized repairs with partial communication, and secondly on a distributed execution algorithm that efficiently propagates delays. This architecture has been successfully experimented on the field for the achievement of surveillance missions involving eight (8) real autonomous aerial and ground robots.

… and Automation, 2003 …, 2003

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010

We consider the following problem: GIVEN (1) a set of service requests occurring at known locations in an environment, (2) a set of temporal and logical constraints on how the requests need to be serviced, (3) a team of robots and their capacities to service the requests individually or through collaboration, FIND robot control and communication strategies guaranteeing the correct servicing of the requests. Our approach is hierarchical. At the top level, we check whether the specification, which is a regular expression over the requests, is distributable among the robots given their service and cooperation capabilities; if the answer is positive, we generate individual specifications in the form of finite state automata, and interaction rules in the form of synchronizations on shared requests. At the bottom level, we check whether the local specifications and the synchronizations can be implemented given the motion and communication constraints of the robots; if the answer is positive, we generate robot motion and service plans, which are then mapped to control and communication strategies. We illustrate the method with experimental and simulation results.

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

In multi-robot systems, task allocation and coordination are two fundamental problems that share high synergy. Although multi-robot architectures typically separate them into distinct layers, relevant improvement may be expected from solutions that are able to concurrently handle them at the same "level". This paper proposes a novel framework, called CoMutaR (Coalition formation based on Multitasking Robots), which is used for both tackle task distribution among teams of mobile robots, and to guarantee the coordination within the formed teams. Robot capabilities are modelled as actions, independent modules whose inputs do not depend on the robot that generated it. Solutions to tasks are devised as coalitions of actions, that can be spread amongst the available robots. We also define the concept of share-restricted resources, which are periodically checked and updated by the actions in the system. In contrast to prior approaches, this mechanism enables to quickly determine if two actions can be executed simultaneously, allowing a single robot to execute multiple tasks concurrently. A single-round auction protocol is used to automatically discover and form coalitions. Once a coalition is formed, coordination among robots is modelled as constraints imposed over the share-restricted resources. Finally, we have successfully implemented and applied CoMutaR in typical scenarios like object transportation, area surveillance, and multi-robot box pushing. Experimental results demonstrate that the system is able to provide good solutions even in the case of severe failures in participating robots.

ArXiv, 2021

In this paper, we propose a distributed multi-stage optimization method for planning complex missions for heterogeneous multi-robot teams. This class of problems involves tasks that can be executed in different ways and are associated with cross-schedule dependencies that constrain the schedules of the different robots in the system. The proposed approach involves a multi-objective heuristic search of the mission, represented as a hierarchical tree that defines the mission goal. This procedure outputs several favorable ways to fulfil the mission, which directly feed into the next stage of the method. We propose a distributed metaheuristic based on evolutionary computation to allocate tasks and generate schedules for the set of chosen decompositions. The method is evaluated in a simulation setup of an automated greenhouse use case, where we demonstrate the method’s ability to adapt the planning strategy depending on the available robots and the given optimization criteria.

IEEE Robotics and Automation Letters

We propose a loosely-coupled framework for integrated task assignment, motion planning, coordination and control of heterogeneous fleets of robots subject to non-cooperative tasks. The approach accounts for the important real-world requirement that tasks can be posted asynchronously. We exploit systematic search for optimal task assignment, where interference is considered as a cost and estimated with knowledge of the kinodynamic models and current state of the robots. Safety is guaranteed by an online coordination algorithm, where the absence of collisions is treated as a hard constraint. The relation between the weight of interference cost in task assignment and computational overhead is analyzed empirically, and the approach is compared against alternative realizations using local search algorithms for task assignment. Index Terms-Planning, scheduling and coordination, task and motion planning, multi-robot systems. I. INTRODUCTION R EALIZING fleets of autonomous robots requires solving several problems: the task assignment problem, namely, that of deciding which tasks should be allocated to which robots so as to optimize fleet performance; the coordination problem, that is, to decide how robots should negotiate the use of shared resources (most notably, the space to be traversed); the motion planning problem, the solution of which determines the trajectories robots take in completing tasks; and the control problem, that is, to ensure the realization of these trajectories under given constraints. Realizing safe, efficient and flexible multi-robot systems that operate in the real world requires considering these problems jointly, as the solution of one problem may affect the search space of another [1]. Three important issues need to be accounted for at all levels of decision making. First, the performance of the fleet depends on both individual robot capabilities and the extent to which common resources are contended among robots, i.e., the cost of interference. Second, the tasks to be carried out by robots become known asynchronously online, while part of the multi-robot system is in motion. Third,

Proceedings of the IEEE/RSJ …, 2002

This paper describes an algorithm for deploying the members of a mobile robot team into an unknown en-vironment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next ...

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005., 2005

The objective of this work is to investigate on-line optimization-based coordination strategies for robot teams to efficiently accomplish a mission (e.g., reach a set of assigned targets) while avoiding collisions. The multi-robot coordination problem is addressed by solving an on-line receding-horizon mixed-integer program to find some suitable inputs for the vehicles. Simulations results verify the feasibility of our approach.