Recent Research in Cooperative Control of Multivehicle Systems

This paper presents a survey of recent research in cooperative control of multivehicle systems, using a common mathematical framework to allow different methods to be described in a unified way. The survey has three primary parts: an overview of current applications of cooperative control, a summary of some of the key technical approaches that have been explored, and a description of some possible future directions for research. Specific technical areas that are discussed include formation control, cooperative tasking, spatiotemporal planning, and consensus

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

A Survey of User Interfaces for Robot Teleoperation

Robots are used today to accomplish many tasks in society, be it in industry, at home, or as helping tools on tragic incidents. The human-robot systems currently developed span a broad variety of applications and are typically very different from one another. The interaction techniques designed for each system are also very different, although some effort has been directed in defining common properties and strategies for guiding human-robot interaction (HRI) development. This work aims to present the state-of-the-art in teleoperation interaction techniques between robots and their users. By presenting potentially useful design models and motivating discussions on topics to which the research community has been paying little attention lately, we also suggest solutions to some of the design and operational problems being faced in this area

A Predictive Model for Human-Unmanned Vehicle Systems : Final Report

Advances in automation are making it possible for a single operator to control multiple unmanned vehicles (UVs). This capability is desirable in order to reduce the operational costs of human-UV systems (HUVS), extend human capabilities, and improve system effectiveness. However, the high complexity of these systems introduces many significant challenges to system designers. To help understand and overcome these challenges, high-fidelity computational models of the HUVS must be developed. These models should have two capabilities. First, they must be able to describe the behavior of the various entities in the team, including both the human operator and the UVs in the team. Second, these models must have the ability to predict how changes in the HUVS and its mission will alter the performance characteristics of the system. In this report, we describe our work toward developing such a model. Via user studies, we show that our model has the ability to describe the behavior of a HUVS consisting of a single human operator and multiple independent UVs with homogeneous capabilities. We also evaluate the model’s ability to predict how changes in the team size, the human-UV interface, the UV’s autonomy levels, and operator strategies affect the system’s performance.Prepared for MIT Lincoln Laborator

HUMAN CONTROL OF COOPERATING ROBOTS

Advances in robotic technologies and artificial intelligence are allowing robots to emerge fromresearch laboratories into our lives. Experiences with field applications show that we haveunderestimated the importance of human-robot interaction (HRI) and that new problems arise inHRI as robotic technologies expand. This thesis classifies HRI along four dimensions - human,robot, task, and world and illustrates that previous HRI classifications can be successfullyinterpreted as either about one of these elements or about the relationship between two or moreof these elements. Current HRI studies of single-operator single-robot (SOSR) control andsingle-operator multiple-robots (SOMR) control are reviewed using this approach.Human control of multiple robots has been suggested as a way to improve effectiveness inrobot control. Unlike previous studies that investigated human interaction either in low-fidelitysimulations or based on simple tasks, this thesis investigates human interaction with cooperatingrobot teams within a realistically complex environment. USARSim, a high-fidelity game-enginebasedrobot simulator, and MrCS, a distributed multirobot control system, were developed forthis purpose. In the pilot experiment, we studied the impact of autonomy level. Mixed initiativecontrol yielded performance superior to fully autonomous and manual control.To avoid limitation to particular application fields, the present thesis focuses on commonHRI evaluations that enable us to analyze HRI effectiveness and guide HRI design independentlyof the robotic system or application domain. We introduce the interaction episode (IEP), whichwas inspired by our pilot human-multirobot control experiment, to extend the Neglect ToleranceHUMAN CONTROL OF COOPERATING ROBOTSJijun Wang, Ph.D.University of Pittsburgh, 2007vmodel to support general multiple robots control for complex tasks. Cooperation Effort (CE),Cooperation Demand (CD), and Team Attention Demand (TAD) are defined to measure thecooperation in SOMR control. Two validation experiments were conducted to validate the CDmeasurement under tight and weak cooperation conditions in a high-fidelity virtual environment.The results show that CD, as a generic HRI metric, is able to account for the various factors thataffect HRI and can be used in HRI evaluation and analysis

Modeling Team Performance For Coordination Configurations Of Large Multi-Agent Teams Using Stochastic Neural Networks

Coordination of large numbers of agents to perform complex tasks in complex domains is a rapidly progressing area of research. Because of the high complexity of the problem, approximate and heuristic algorithms are typically used for key coordination tasks. Such algorithms usually require tuning algorithm parameters to yield the best performance under particular circumstances. Manually tuning parameters is sometimes difficult. In domains where characteristics of the environment can vary dramatically from scenario to scenario, it is desirable to have automated techniques for appropriately configuring the coordination. This research presents an approach to online reconfiguration of heuristic coordination algorithms. The approach uses an abstract simulation to produce a large performance data set to train a stochastic neural network that concisely models the complex, probabilistic relationship between configurations, environments and performance metrics. The final stochastic neural network, referred as the team performance model, is then used as the core of a tool that allows rapid online or offline configuration of coordination algorithms to particular scenarios and user preferences. The overall system allows rapid adaptation of coordination, leading to better performance in new scenarios. Results show that the team performance model captured key features of a very large configuration space and mostly captured the uncertainty in performance well. The tool was shown to be often capable of reconfiguring the algorithms to meet user requests for increases or decreases in performance parameters. This work represents the first practical approach to quickly reconfiguring a complex set of algorithms for a specific scenario

Route Planning and Operator Allocation in Robot Fleets

In this thesis, we address various challenges related to optimal planning and task allocation in a robot fleet supervised by remote human operators. The overarching goal is to enhance the performance and efficiency of the robot fleets by planning routes and scheduling operator assistance while accounting for limited human availability. The thesis consists of three main problems, each of which focuses on a specific aspect of the system. The first problem pertains to optimal planning for a robot in a collaborative human-robot team, where the human supervisor is intermittently available to assist the robot to complete its tasks faster. Specifically, we address the challenge of computing the fastest route between two configurations in an environment with time constraints on how long the robot can wait for assistance at intermediate configurations. We consider the application of robot navigation in a city environment, where different routes can have distinct speed limits and different time constraints on how long a robot is allowed to wait. Our proposed approach utilizes the concepts of budget and critical departure times, enabling optimal solution and enhanced scalability compared to existing methods. Extensive comparisons with baseline algorithms on a city road network demonstrate its effectiveness and ability to achieve high-quality solutions. Furthermore, we extend the problem to the multi-robot case, where the challenge lies in prioritizing robots when multiple service requests arrive simultaneously. To address this challenge, we present a greedy algorithm that efficiently prioritizes service requests in a batch and has a remarkably good performance compared to the optimal solution. The next problem focuses on allocating human operators to robots in a fleet, considering each robot's specified route and the potential for failures and getting stuck. Conventional techniques used to solve such problems face scalability issues due to exponential growth of state and action spaces with the number of robots and operators. To overcome these, we derive conditions for a technical requirement called indexability, thereby enabling the use of the Whittle index heuristic. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be easily verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in supervised robot fleets. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal scalable approach that outperforms existing scalable methods. Finally, we investigate the impact of interruptions on human supervisors overseeing a fleet of robots. Human supervisors in such systems are primarily responsible for monitoring robots, but can also be assigned with secondary tasks. These tasks can act as interruptions and can be categorized as either intrinsic, i.e., being directly related to the monitoring task, or extrinsic, i.e., being unrelated. Through a user study involving $39$ participants, the findings reveal that task performance remains relatively unaffected by interruptions, and is primarily dependent on the number of robots being monitored. However, extrinsic interruptions led to a significant increase in perceived workload, creating challenges in switching between tasks. These results highlight the importance of managing user workload by limiting extrinsic interruptions in such supervision systems. Overall, this thesis contributes to the field of robot planning and operator allocation in collaborative human-robot teams. By incorporating human assistance, addressing scalability challenges, and understanding the impact of interruptions, we aim to enhance the performance and usability of robot fleets. Our work introduces optimal planning methods and efficient allocation strategies, empowering the seamless operation of robot fleets in real-world scenarios. Additionally, we provide valuable insights into user workload, shedding light on the interactions between humans and robots in such systems. We hope that our research promotes the widespread adoption of robot fleets and facilitates their integration into various domains, ultimately driving advancements in the field

Coordinating Team Tactics for Swarm-vs.-Swarm Adversarial Games

While swarms of UAVs have received much attention in the last few years, adversarial swarms (i.e., competitive, swarm-vs.-swarm games) have been less well studied. In this dissertation, I investigate the factors influential in team-vs.-team UAV aerial combat scenarios, elucidating the impacts of force concentration and opponent spread in the engagement space. Specifically, this dissertation makes the following contributions: (1) Tactical Analysis: Identifies conditions under which either explicitly-coordinating tactics or decentralized, greedy tactics are superior in engagements as small as 2-vs.-2 and as large as 10-vs.-10, and examines how these patterns change with the quality of the teams' weapons; (2) Coordinating Tactics: Introduces and demonstrates a deep-reinforcement-learning framework that equips agents to learn to use their own and their teammates' situational context to decide which pre-scripted tactics to employ in what situations, and which teammates, if any, to coordinate with throughout the engagement; the efficacy of agents using the neural network trained within this framework outperform baseline tactics in engagements against teams of agents employing baseline tactics in N-vs.-N engagements for N as small as two and as large as 64; and (3) Bio-Inspired Coordination: Discovers through Monte-Carlo agent-based simulations the importance of prioritizing the team's force concentration against the most threatening opponent agents, but also of preserving some resources by deploying a smaller defense force and defending against lower-penalty threats in addition to high-priority threats to maximize the remaining fuel within the defending team's fuel reservoir.Ph.D