Complex Tasks Allocation for Multi Robot Teams under Communication Constraints (original) (raw)
Related papers
Integrated Mission Specification and Task Allocation for Robot Teams - Design and Implementation
Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007
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.
Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams
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.
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.
A Generic Framework for Distributed Multirobot Cooperation
Journal of Intelligent and Robotic Systems
DEMiR-CF is a generic framework designed for a multirobot team to efficiently allocate tasks among themselves and achieve an overall mission. In the design of DEMiR-CF, the following issues were particularly investigated as the design criteria: efficient and realistic representation of missions, efficient allocation of tasks to cooperatively achieve a global goal, maintenance of the system coherence and consistency by the team members, detection of the contingencies and recover from various failures that may arise during runtime, efficient reallocation of tasks (if necessary) and reorganization of team members (if necessary). DEMiR-CF is designed to address different types of missions from the simplest to more complex ones, including missions with interrelated tasks and multi-resource (robot) requirements. Efficiency of the framework is validated through experiments in three different types of domains.
Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams
IEEE Access, 2021
This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.
Analysis of Dynamic Task Allocation in Multi-Robot Systems
The International Journal of Robotics Research, 2006
Dynamic task allocation is an essential requirement for multi-robot systems operating 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 tasks, 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 obse...
Multiagent Task Allocation and Planning with Multi-Objective Requirements
2021
In service robot applications, planning is often integrated with task allocation. Linear Temporal Logic (LTL) as an expressive high-level formalism is widely used for task specification, and allows for formalised restrictions on temporal sequences of tasks. In multiagent planning, a Multi-Objective Markov Decision Process extends the standard model with vector rewards capturing possibly conflicting planning objectives. Such objectives include the success rates of accomplishing individual tasks, and the cost budgets for individual agents. In this paper, we consider the problem of concurrently allocating LTL task sequences to a team of agents and calculating optimal task schedulers simultaneously, satisfying cost and probability thresholds. We reduce this problem to multi-objective scheduler synthesis for a team MDP structure, whose size is linear in the number of agents. Our preliminary experiment demonstrates the scalability of our approach.
A Distributed Multi-Robot Cooperation Framework for Real Time Task Achievement
In this paper, we propose a general framework, DEMiR-CF, for a multi-robot team to achieve a complex mission including inter-related tasks that require diverse capabilities and/or simultaneous executions. Our framework integrates a distributed task allocation scheme, cooperation mechanisms and precaution routines for multi-robot team execution. Its performance has been demonstrated in Naval Mine Countermeasures, Multi-robot Multi-Target Exploration and Object Construction domains. The framework not only ensures near-optimal solutions for task achievement but also efficiently responds to real time contingencies.
Multi-robot Cooperation : Architectures and Paradigms
This paper presents a generic architecture for the operation of a team of autonomous robots to achieve complex missions. Its interest stems from its ability to provide a framework for cooperative decisional processes at different levels : high level plan synthesis, task allocation and task achievement. It is based on a combination of local individual planning and coordinated decision for incremental plan adaptation to the multi-robot context. Indeed, we claim that it is often possible (and useful) to treat these three issues separately. As we will see, this levels deal with problems of different nature, leading to specific representations, algorithms and protocols.