We consider a team of mobile autonomous robots with the aim to cover a given set of targets. Each robot aims to select a target to cover and physically reach it by the final time in coordination with other robots given the locations of targets. Robots are unaware of which targets other robots intend to cover. Each robot can control its mobility and who to send information to. We assume communication happens over a wireless channel that is subject to fading and failures. Given the setup, we propose a decentralized algorithm based on decentralized fictitious play in which robots reason about the selections and locations of other robots to decide which target to select, whether to communicate or not, who to communicate with, and where to move. Specifically, the communication actions of the robots are learning-aware, and their mobility actions are sensitive to the success probability of communication. We show that the decentralized algorithm guarantees that robots will cover their targets in finite time. Numerical simulations and experiments using a team of mobile robots confirm the target coverage in finite time and show that mobility control for communication and learning-aware voluntary communication protocols reduce the number of communication attempts in comparison to a benchmark distributed algorithm that relies on communication after every decision epoch.
Beijing Institute of Aerospace Systems Engineering（北京宇航系统工程研究所）；Beijing Aerospace Automatic Control Institute（北京航天自动控制研究所）；China Academy of Launch Vehicle Technology（中国运载火箭技术研究院）
A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.
Multiple autonomous agents interact over a random communication network to maximize their individual utility functions which depend on the actions of other agents. We consider decentralized best-response with inertia type algorithms in which agents form beliefs about the future actions of other players based on local information, and take an action that maximizes their expected utility computed with respect to these beliefs or continue to take their previous action. We show convergence of these types of algorithms to a Nash equilibrium in weakly acyclic games under the condition that the belie…
Tackling complex team problems requires understanding each team member's skills in order to devise a task assignment maximizing the team performance. This paper proposes a novel quantitative model describing the decentralized process by which individuals in a team learn who has what abilities, while concurrently assigning tasks to each of the team members. In the model, the appraisal network represents team member's evaluations of one another and each team member chooses their own workload. The appraisals and workload assignment change simultaneously: each member builds their own local apprais…
Indian institute of Science（印度科学学院）；National Institute of Technology, Tiruchirappalli（蒂鲁吉拉伯利国家理工学院）
In this paper, a non-iterative spatio-temporal multi-task assignment approach is used for playing piano music by a team of robots. This paper considers the piano playing problem, in which an algorithm needs to compute the trajectories for a dynamically sized team of robots who will play the musical notes by traveling through the specific locations associated with musical notes at their respective specific times. A two-step dynamic resource allocation based on a spatio-temporal multi-task assignment problem (DREAM), has been implemented to assign robots for playing the musical tune. The algorit…
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning
Universidad de la Republica（乌拉圭共和国大学）；Univ. of Pittsburgh（匹兹堡大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Universitat Pompeu Fabra（庞培法布拉大学）；Carnegie Mellon University（卡内基梅隆大学）
Wardropian Cycles make traffic assignment both optimal and fair by eliminating price-of-anarchy with Cyclical User Equilibrium for compliant connected autonomous vehicles
Georgia Institute of Technology（佐治亚理工学院）；Optimus Ride
Multi-robot task allocation is a ubiquitous problem in robotics due to its applicability in a variety of scenarios. Adaptive task-allocation algorithms account for unknown disturbances and unpredicted phenomena in the environment where robots are deployed to execute tasks. However, this adaptivity typically comes at the cost of requiring precise knowledge of robot models in order to evaluate the allocation effectiveness and to adjust the task assignment online. As such, environmental disturbances can significantly degrade the accuracy of the models which in turn negatively affects the quality …
Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation
Contextual Robotics Institute and the Department of Mechanical and Aerospace Engineering, UC San Diego（情境机器人研究所和机械与航空航天工程系，加州大学圣地亚哥分校）；U.S. Army Combat Capabilities Development Command, Army Research Laboratory（美国陆军战斗能力发展司令部，陆军研究实验室）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Brno University of Technology（布尔诺理工大学）
We study the problem of minimizing the resource capacity of autonomous agents cooperating to achieve a shared task. More specifically, we consider high-level planning for a team of homogeneous agents that operate under resource constraints in stochastic environments and share a common goal: given a set of target locations, ensure that each location will be visited infinitely often by some agent almost surely. We formalize the dynamics of agents by consumption Markov decision processes. In a consumption Markov decision process, the agent has a resource of limited capacity. Each action of the ag…
