Task Allocation: Motivation-Based. Dr. Daisy Tang
|
|
- Austin Horn
- 5 years ago
- Views:
Transcription
1 Task Allocation: Motivation-Based Dr. Daisy Tang
2 Outline Motivation-based task allocation (modeling) Formal analysis of task allocation
3 Motivations vs. Negotiation in MRTA Motivations(ALLIANCE): Pro: Enables robots to make decisions even when communication breaks down Con: Must use L-ALLIANCE to set parameters of the system Negotiation: Pro: Allows decision process to be made explicit Con: Does not provide mechanism for robots to recover from communication breakdown
4 Today s Paper ALLIANCE: An Architecture for Fault Tolerant Multi-Robot Cooperation, by Parker, IEEE Transactions on Robotics and Automation, Presented by Alex Garcia
5 Challenges of Multi-Robot Cooperation Fault tolerance: The ability of the robot team to respond to individual robot failures or failures in communication Adaptivity: The ability of the robot team to changeits behavior over time in response to a dynamic environment, changes in the team mission, or changes in the team capabilities, to either improve performanceor to prevent unnecessary degradation in performance Reliability: The dependability of a system, and whether it functions properly and correctly each time it is utilized
6 Problem Definition, Goal The Problem: Solving the problem of multi-robot cooperation for small-to medium-sized teams of heterogeneousrobots performing missions composed of independentsubtasks that may have ordering constrains Goal: Adaptive, fault tolerant cooperative action selection in multi-robot teams Fault tolerant cooperation: At group level, robots select tasks to ensure that mission is completed by the team as a whole (does not address individual robot fault tolerance)
7 Assumptions Robots can detect the effect of their own actions A robot can detect the actions of other team members Robots do not lie and are not intentionally adversarial Communication is not guaranteed to be available Robots do not possess perfect sensors and effectors If a robot fails, it cannot necessarily communicate its failure to its teammates No centralized store of complete world knowledge is available
8 Overview of ALLIANCE ALLIANCE developed for heterogeneousmulti-robot cooperation Utilizes distributed control Focuses on adaptiveresponse to off-normal events amidst: Robot failures Sensor/actuator uncertainties Dynamic environment Mission changes Demonstrated in 8 proof-of-principle applications Represents current state of the art in multi-robot control for small team sizes
9 The ALLIANCE Architecture Higher-level behaviors achieve a task Behavior set is activated by motivation levels Lower-level behaviors can be inhibited by higher-levels
10 Motivational Behaviors Motivations are designed to allow robot team members to perform tasks only as long as they demonstrate their abilityto have the desired effect on the world Differs from traditional task allocation that begins with task decomposition and then computing the optimal robot-to-task mapping At all times during the mission, each motivational behavior receives input from a number of resources and generates a non-negative number (activation level) When this level exceeds a given threshold, the corresponding behavior set becomes active
11 Two Types of Internal Motivations Motivation is initialized to 0 and increases at a certain rate over time Impatience: Enables a robot to handle situations when other robots (outside itself) fail in performing a given task A robot may be motivated to take over a task from another robot Fast rate vs. slower rate of impatience Acquiescence: Enables a robot to handle situations when itself fails to perform its task A robot may give up for other tasks because other more capable robots can perform the task or it simply cannot fulfill the task in an acceptable period of time
12 Action Recognition in ALLIANCE Issue: How does a robot know what its teammate is doing? Ideally, prefer passive action recognition E.g., vision-based interpretation of actions But, very difficult As substitute, ALLIANCE uses periodic, lowbandwidth broadcastcommunications to inform teammates of current actions
13 ALLIANCE Formal Model
14 Formal Model: Impatience Impatience rate will be the minimum slow rate, if r i has received communication in the last τ i time units, but not for longer than Φ ij time units Reset impatience to 0 if r i has just received its first message from r k δt = time since last communication check No more than once.
15 Formal Model: Acquiescence Give up when: 1) r i has worked on a task for a length of φ ij time and some other robots has taken over the task 2) r i has worked on a task for a length of λ ij time
16 ALLIANCE Formal Model (Con t.) This motivation increases at some positive rate unless one of four situations occurs.
17 Application: Mock Hazardous Waste Cleanup
18 ALLIANCE-Based Control
19 Application: Mock Hazardous Waste Cleanup Part I
20 Application: Mock Hazardous Waste Cleanup Part II
21 Application: Mock Hazardous Waste Cleanup Part III
22 Application: Adaptive Box Pushing
23 Box Pushing: Robot Control
24 Summary of ALLIANCE Fundamental focus: fault tolerance Uses motivations(based upon quality metrics) to cause robots to activate tasks Does not use negotiation Impatiencemotivation: Causes robot to become motivated to start a task Fast impatience: if no other robot is performing task Slow impatience: if some robot is performing task Acquiescencemotivation: Causes robot to give up its task
25 Task Allocation: Formal Analysis A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems, by Gerkey and Mataric, in Intl. Journal of Robotics Research, 2004.
26 MRTA Problem Fundamental question: which robot should execute which task? in order to cooperatively achieve the global goal. Task a subgoal that can be achieved independently of other subgoals Approaches: Intentional cooperation (ALLIANCE) Negotiation-based (CNP, MURDOCH) Emergent approaches
27 Utility (Fitness, Valuation and Cost) Assumption: each robot internally estimates the value (or the cost) of executing an action This estimation includes: Expected qualityof task execution, given the method and equipment to be used Expected resource cost, given the requirement of the task Given a robot R and a task T, if R is capable of executing T, then the utility can be defined as:
28 A Taxonomy of MRTA Problems Three axes for describing MRTA: Single-task robots (ST) vs. Multi-task robots (MT) Single-robot tasks (SR) vs. Multi-robot tasks (MR) Instantaneous assignment (IA) vs. Timeextended assignment (TA)
29 ST-SR-IA Problems An instance of the Optimal Assignment Problem Definition: Given mrobots, nprioritized tasks, and utility estimates for each of the mnpossible robot-task pairs, assign at most one task to each robot. Both centralized and distributed approaches exist to find optimal allocation Tradeoffs between solution time and communication overhead Examples: ALLIANCE, MURDOCH, Role-Allocation in Soccer
30 Algorithm 1 (Greedy Assignment) 1. If any robot remains unassigned, find the robot-task pair (i, j) with the highest utility. Otherwise, quit. 2. Assign robot i to task j and remove them from consideration. 3. Go to step 1. Reference: BLE Approach by Werger & Mataric (2001)
31 Algorithm 2 (MURDOCH) Online assignment: 1. When a new task is introduced, assign it to the most fit robot that is currently available
32 ST-SR-TA Problems If there s a model of how tasks will arrive, then robots future utilities for the tasks can be predicted with some accuracy This problem is one of building a timeextended schedule of tasks for each robot Problems are NP-hard
33 Algorithm (Approximation Alg.) 1. Optimally solve the initial mnassignment problem 2. Use the Greedy algorithm to assign the remaining tasks in an online fashion, as the robots become available
34 ST-MR-IA Problems Many problems involve tasks that require the combined effort of multiple robots We must consider combined utilities of groups of robots, which are in general not sums over individual utilities In multi-agent community, the ST-MR-IA problem is referred to as coalition formation It is equivalent to a Set Partitioning Problem(SPP), which is NP-hard Approach: It may be necessary to enumerate a set of feasible coalition-task combinations In the case that the combination space is very large, there s a need to prune
35 ST-MR-TA Problems This problem includes both coalition formationand scheduling Example, delivering a number of packages of various sizes from a single distribution center to different destinations To produce an optimal solution, all possible schedules for all possible coalitions must be considered, which is NP-hard If coalitions are given, with no more than one coalition allowed for each task, the result in an instance of a multiprocessor scheduling problem, still NP-hard
CSCI 445 Laurent Itti. Group Robotics. Introduction to Robotics L. Itti & M. J. Mataric 1
Introduction to Robotics CSCI 445 Laurent Itti Group Robotics Introduction to Robotics L. Itti & M. J. Mataric 1 Today s Lecture Outline Defining group behavior Why group behavior is useful Why group behavior
More informationALLIANCE: An Architecture for Fault Tolerant, Cooperative Control of Heterogeneous Mobile Robots
ALLIANCE: An Architecture for Fault Tolerant, Cooperative Control of Heterogeneous Mobile Robots Lynne E. Parker Center for Engineering Systems Advanced Research Oak Ridge National Laboratory P. O. Box
More informationCS594, Section 30682:
CS594, Section 30682: Distributed Intelligence in Autonomous Robotics Spring 2003 Tuesday/Thursday 11:10 12:25 http://www.cs.utk.edu/~parker/courses/cs594-spring03 Instructor: Dr. Lynne E. Parker ½ TA:
More informationDistributed Control of Multi-Robot Teams: Cooperative Baton Passing Task
Appeared in Proceedings of the 4 th International Conference on Information Systems Analysis and Synthesis (ISAS 98), vol. 3, pages 89-94. Distributed Control of Multi- Teams: Cooperative Baton Passing
More informationMulti-robot task allocation using affect
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School 2004 Multi-robot task allocation using affect Aaron Gage University of South Florida Follow this and additional
More informationDistributed Multi-Robot Coalitions through ASyMTRe-D
Proc. of IEEE International Conference on Intelligent Robots and Systems, Edmonton, Canada, 2005. Distributed Multi-Robot Coalitions through ASyMTRe-D Fang Tang and Lynne E. Parker Distributed Intelligence
More informationRearrangement task realization by multiple mobile robots with efficient calculation of task constraints
2007 IEEE International Conference on Robotics and Automation Roma, Italy, 10-14 April 2007 WeA1.2 Rearrangement task realization by multiple mobile robots with efficient calculation of task constraints
More informationAdvanced Topics in AI
Advanced Topics in AI - Task Allocation - Alexander Felfernig and Gerald Steinbauer Institute for Software Technology Inffeldgasse 16b/2 A-8010 Graz Austria Agenda Motivation Examples Formal Problem Description
More informationMulti-Robot Formation. Dr. Daisy Tang
Multi-Robot Formation Dr. Daisy Tang Objectives Understand key issues in formationkeeping Understand various formation studied by Balch and Arkin and their pros/cons Understand local vs. global control
More informationUsing a Sensor Network for Distributed Multi-Robot Task Allocation
In IEEE International Conference on Robotics and Automation pp. 158-164, New Orleans, LA, April 26 - May 1, 2004 Using a Sensor Network for Distributed Multi-Robot Task Allocation Maxim A. Batalin and
More informationTask Allocation: Role Assignment. Dr. Daisy Tang
Task Allocation: Role Assignment Dr. Daisy Tang Outline Multi-robot dynamic role assignment Task Allocation Based On Roles Usually, a task is decomposed into roleseither by a general autonomous planner,
More informationOverview Agents, environments, typical components
Overview Agents, environments, typical components CSC752 Autonomous Robotic Systems Ubbo Visser Department of Computer Science University of Miami January 23, 2017 Outline 1 Autonomous robots 2 Agents
More informationA Taxonomy of Multirobot Systems
A Taxonomy of Multirobot Systems ---- Gregory Dudek, Michael Jenkin, and Evangelos Milios in Robot Teams: From Diversity to Polymorphism edited by Tucher Balch and Lynne E. Parker published by A K Peters,
More informationAn Agent-Based Intentional Multi-Robot Task Allocation Framework
An Agent-Based Intentional Multi-Robot Task Allocation Framework Savas Ozturk 1, Ahmet Emin Kuzucuoglu 2 1 TUBITAK BILGEM, Gebze, Kocaeli, Turkey 2 Department of Computer and Control Education, Marmara
More informationTraffic Control for a Swarm of Robots: Avoiding Group Conflicts
Traffic Control for a Swarm of Robots: Avoiding Group Conflicts Leandro Soriano Marcolino and Luiz Chaimowicz Abstract A very common problem in the navigation of robotic swarms is when groups of robots
More informationUsing Dynamic Capability Evaluation to Organize a Team of Cooperative, Autonomous Robots
Using Dynamic Capability Evaluation to Organize a Team of Cooperative, Autonomous Robots Eric Matson Scott DeLoach Multi-agent and Cooperative Robotics Laboratory Department of Computing and Information
More informationCollective Robotics. Marcin Pilat
Collective Robotics Marcin Pilat Introduction Painting a room Complex behaviors: Perceptions, deductions, motivations, choices Robotics: Past: single robot Future: multiple, simple robots working in teams
More informationCooperative Tracking using Mobile Robots and Environment-Embedded, Networked Sensors
In the 2001 International Symposium on Computational Intelligence in Robotics and Automation pp. 206-211, Banff, Alberta, Canada, July 29 - August 1, 2001. Cooperative Tracking using Mobile Robots and
More informationDistributed, Play-Based Coordination for Robot Teams in Dynamic Environments
Distributed, Play-Based Coordination for Robot Teams in Dynamic Environments Colin McMillen and Manuela Veloso School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, U.S.A. fmcmillen,velosog@cs.cmu.edu
More informationEmergent Task Allocation for Mobile Robots
Robotics: Science and Systems 00 Atlanta, GA, USA, June -0, 00 Emergent Task Allocation for Mobile Robots Nuzhet Atay Department of Computer Science and Engineering Washington University in St. Louis Email:
More informationCS 599: Distributed Intelligence in Robotics
CS 599: Distributed Intelligence in Robotics Winter 2016 www.cpp.edu/~ftang/courses/cs599-di/ Dr. Daisy Tang All lecture notes are adapted from Dr. Lynne Parker s lecture notes on Distributed Intelligence
More informationBuilding large-scale robot systems: Distributed role assignment in dynamic, uncertain domains
Building large-scale robot systems: Distributed role assignment in dynamic uncertain domains Alessandro Farinelli Paul Scerri and Milind Tambe Dipartimento di Informatica e Sistemistica Univerista di Roma
More informationSensor Network-based Multi-Robot Task Allocation
In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS2003) pp. 1939-1944, Las Vegas, Nevada, October 27-31, 2003 Sensor Network-based Multi-Robot Task Allocation Maxim A. Batalin and Gaurav S.
More informationSurveillance strategies for autonomous mobile robots. Nicola Basilico Department of Computer Science University of Milan
Surveillance strategies for autonomous mobile robots Nicola Basilico Department of Computer Science University of Milan Intelligence, surveillance, and reconnaissance (ISR) with autonomous UAVs ISR defines
More informationCooperative Tracking with Mobile Robots and Networked Embedded Sensors
Institutue for Robotics and Intelligent Systems (IRIS) Technical Report IRIS-01-404 University of Southern California, 2001 Cooperative Tracking with Mobile Robots and Networked Embedded Sensors Boyoon
More informationMulti-Platform Soccer Robot Development System
Multi-Platform Soccer Robot Development System Hui Wang, Han Wang, Chunmiao Wang, William Y. C. Soh Division of Control & Instrumentation, School of EEE Nanyang Technological University Nanyang Avenue,
More informationCS494/594: Software for Intelligent Robotics
CS494/594: Software for Intelligent Robotics Spring 2007 Tuesday/Thursday 11:10 12:25 Instructor: Dr. Lynne E. Parker TA: Rasko Pjesivac Outline Overview syllabus and class policies Introduction to class:
More informationAtsushi Yamashita and Hajime Asama
24 Int. J. Mechatronics and Automation, Vol. 2, No. 4, 212 Moving task allocation and reallocation method based on body expansion behaviour for distributed multi-robot coordination Guanghui Li* Department
More informationCIS 480/899 Embedded and Cyber Physical Systems Spring 2009 Introduction to Real-Time Scheduling. Examples of real-time applications
CIS 480/899 Embedded and Cyber Physical Systems Spring 2009 Introduction to Real-Time Scheduling Insup Lee Department of Computer and Information Science University of Pennsylvania lee@cis.upenn.edu www.cis.upenn.edu/~lee
More informationTeam-Triggered Coordination of Robotic Networks for Optimal Deployment
Team-Triggered Coordination of Robotic Networks for Optimal Deployment Cameron Nowzari 1, Jorge Cortés 2, and George J. Pappas 1 Electrical and Systems Engineering 1 University of Pennsylvania Mechanical
More informationTowards Quantification of the need to Cooperate between Robots
PERMIS 003 Towards Quantification of the need to Cooperate between Robots K. Madhava Krishna and Henry Hexmoor CSCE Dept., University of Arkansas Fayetteville AR 770 Abstract: Collaborative technologies
More informationApplication of congestion control algorithms for the control of a large number of actuators with a matrix network drive system
Application of congestion control algorithms for the control of a large number of actuators with a matrix networ drive system Kyu-Jin Cho and Harry Asada d Arbeloff Laboratory for Information Systems and
More informationMulti-Robot Coordination. Chapter 11
Multi-Robot Coordination Chapter 11 Objectives To understand some of the problems being studied with multiple robots To understand the challenges involved with coordinating robots To investigate a simple
More informationMulti-Robot Teamwork Cooperative Multi-Robot Systems
Multi-Robot Teamwork Cooperative Lecture 1: Basic Concepts Gal A. Kaminka galk@cs.biu.ac.il 2 Why Robotics? Basic Science Study mechanics, energy, physiology, embodiment Cybernetics: the mind (rather than
More informationOutline. What is AI? A brief history of AI State of the art
Introduction to AI Outline What is AI? A brief history of AI State of the art What is AI? AI is a branch of CS with connections to psychology, linguistics, economics, Goal make artificial systems solve
More informationConfidence-Based Multi-Robot Learning from Demonstration
Int J Soc Robot (2010) 2: 195 215 DOI 10.1007/s12369-010-0060-0 Confidence-Based Multi-Robot Learning from Demonstration Sonia Chernova Manuela Veloso Accepted: 5 May 2010 / Published online: 19 May 2010
More informationUniversity of Bristol - Explore Bristol Research. Peer reviewed version. Link to published version (if available): /VETECF.2011.
Vatsikas, S., Armour, SMD., De Vos, M., & Lewis, T. (2011). A fast and fair algorithm for distributed subcarrier allocation using coalitions and the Nash bargaining solution. In IEEE Vehicular Technology
More informationPrincipled Construction of Software Safety Cases
Principled Construction of Software Safety Cases Richard Hawkins, Ibrahim Habli, Tim Kelly Department of Computer Science, University of York, UK Abstract. A small, manageable number of common software
More informationGateways Placement in Backbone Wireless Mesh Networks
I. J. Communications, Network and System Sciences, 2009, 1, 1-89 Published Online February 2009 in SciRes (http://www.scirp.org/journal/ijcns/). Gateways Placement in Backbone Wireless Mesh Networks Abstract
More informationSAFETY CASES: ARGUING THE SAFETY OF AUTONOMOUS SYSTEMS SIMON BURTON DAGSTUHL,
SAFETY CASES: ARGUING THE SAFETY OF AUTONOMOUS SYSTEMS SIMON BURTON DAGSTUHL, 17.02.2017 The need for safety cases Interaction and Security is becoming more than what happens when things break functional
More informationTravel time uncertainty and network models
Travel time uncertainty and network models CE 392C TRAVEL TIME UNCERTAINTY One major assumption throughout the semester is that travel times can be predicted exactly and are the same every day. C = 25.87321
More informationAN INTEGRATED APPROACH TO SOLVING THE REAL-WORLD MULTIPLE TRAVELING ROBOT PROBLEM
AN INTEGRATED APPROACH TO SOLVING THE REAL-WORLD MULTIPLE TRAVELING ROBOT PROBLEM Sanem Sariel * Nadia Erdogan * Tucker Balch + e-mail: sariel@itu.edu.tr e-mail: nerdogan@itu.edu.tr e-mail: tucker.balch@gatech.edu
More informationRobust Multirobot Coordination in Dynamic Environments
Robust Multirobot Coordination in Dynamic Environments M. Bernardine Dias, Marc Zinck, Robert Zlot, and Anthony (Tony) Stentz The Robotics Institute Carnegie Mellon University Pittsburgh, USA {mbdias,
More informationMulti-Robot Systems, Part II
Multi-Robot Systems, Part II October 31, 2002 Class Meeting 20 A team effort is a lot of people doing what I say. -- Michael Winner. Objectives Multi-Robot Systems, Part II Overview (con t.) Multi-Robot
More informationMetaphor of Politics: A Mechanism of Coalition Formation
Metaphor of Politics: A Mechanism of Coalition Formation R. Sorbello and A. Chella Dipartimento di Ingegneria Informatica Universita di Palermo R.C. Arin Mobile Robot Lab. Georgia Institute of Technology
More informationAdaptive Action Selection without Explicit Communication for Multi-robot Box-pushing
Adaptive Action Selection without Explicit Communication for Multi-robot Box-pushing Seiji Yamada Jun ya Saito CISS, IGSSE, Tokyo Institute of Technology 4259 Nagatsuta, Midori, Yokohama 226-8502, JAPAN
More informationJoint work with Dragana Bajović and Dušan Jakovetić. DLR/TUM Workshop, Munich,
Slotted ALOHA in Small Cell Networks: How to Design Codes on Random Geometric Graphs? Dejan Vukobratović Associate Professor, DEET-UNS University of Novi Sad, Serbia Joint work with Dragana Bajović and
More informationHeuristic Search with Pre-Computed Databases
Heuristic Search with Pre-Computed Databases Tsan-sheng Hsu tshsu@iis.sinica.edu.tw http://www.iis.sinica.edu.tw/~tshsu 1 Abstract Use pre-computed partial results to improve the efficiency of heuristic
More informationRoboCup. Presented by Shane Murphy April 24, 2003
RoboCup Presented by Shane Murphy April 24, 2003 RoboCup: : Today and Tomorrow What we have learned Authors Minoru Asada (Osaka University, Japan), Hiroaki Kitano (Sony CS Labs, Japan), Itsuki Noda (Electrotechnical(
More informationThe Effect of Action Recognition and Robot Awareness in Cooperative Robotic Team* Lynne E. Parker. Oak Ridge National Laboratory
The Effect of Action Recognition and Robot Awareness in Cooperative Robotic Team* Lynne E. Parker Center for Engineering Systems Advanced Research Oak Ridge National Laboratory P.O. Box 2008 Oak Ridge,
More informationIQ-ASyMTRe: Synthesizing Coalition Formation and Execution for Tightly-Coupled Multirobot Tasks
Proc. of IEEE International Conference on Intelligent Robots and Systems, Taipai, Taiwan, 2010. IQ-ASyMTRe: Synthesizing Coalition Formation and Execution for Tightly-Coupled Multirobot Tasks Yu Zhang
More informationCORC 3303 Exploring Robotics. Why Teams?
Exploring Robotics Lecture F Robot Teams Topics: 1) Teamwork and Its Challenges 2) Coordination, Communication and Control 3) RoboCup Why Teams? It takes two (or more) Such as cooperative transportation:
More informationMulti-Robot Task-Allocation through Vacancy Chains
In Proceedings of the 03 IEEE International Conference on Robotics and Automation (ICRA 03) pp2293-2298, Taipei, Taiwan, September 14-19, 03 Multi-Robot Task-Allocation through Vacancy Chains Torbjørn
More informationToward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach
Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach Michael A. Goodrich 1 and Daqing Yi 1 Brigham Young University, Provo, UT, 84602, USA mike@cs.byu.edu, daqing.yi@byu.edu Abstract.
More informationDipartimento di Elettronica Informazione e Bioingegneria Robotics
Dipartimento di Elettronica Informazione e Bioingegneria Robotics Behavioral robotics @ 2014 Behaviorism behave is what organisms do Behaviorism is built on this assumption, and its goal is to promote
More informationAN INTEGRATED APPROACH TO SOLVING THE REAL-WORLD MULTIPLE TRAVELING ROBOT PROBLEM
AN INTEGRATED APPROACH TO SOLVING THE REAL-WORLD MULTIPLE TRAVELING ROBOT PROBLEM Sanem Sariel * Nadia Erdogan * Tucker Balch + e-mail: sariel@itu.edu.tr e-mail: nerdogan@itu.edu.tr e-mail: tucker.balch@gatech.edu
More informationA MARINE FAULTS TOLERANT CONTROL SYSTEM BASED ON INTELLIGENT MULTI-AGENTS
A MARINE FAULTS TOLERANT CONTROL SYSTEM BASED ON INTELLIGENT MULTI-AGENTS Tianhao Tang and Gang Yao Department of Electrical & Control Engineering, Shanghai Maritime University 1550 Pudong Road, Shanghai,
More informationAutonomous Robotic (Cyber) Weapons?
Autonomous Robotic (Cyber) Weapons? Giovanni Sartor EUI - European University Institute of Florence CIRSFID - Faculty of law, University of Bologna Rome, November 24, 2013 G. Sartor (EUI-CIRSFID) Autonomous
More informationLand. Site. Preparation. Select. Site. Deploy. Transport
Cooperative Robot Teams Applied to the Site Preparation Task Lynne E. Parker, Yi Guo, and David Jung Center for Engineering Science Advanced Research Computer Science and Mathematics Division Oak Ridge
More informationMulti-Agent Task Allocation for Robot Soccer
Multi-Agent Task Allocation for Robot Soccer Khashayar R. Baghaei and Arvin Agah Department of Electrical Engineering and Computer Science The University of Kansas, Lawrence, KS 66045 USA ABSTRACT This
More informationMulti-Robot Team Design for Real-World Applications
. 4 Multi-Robot Team Design for Real-World Applications L. E. Parker ~oaifcs6/0/68--/ Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge, Tennessee 3783 1 To be presented
More informationReal-Time Spectrum Management for Wireless Networks
Real-Time Spectrum Management for Wireless Networks Dan Stevenson, Arnold Bragg RTI International, Inc. Research Triangle Park, NC Outline Problem statement Disruptive idea Details: approach, issues, architecture
More informationPrincipled Approaches to the Design of Multi-Robot Systems
In Proc. Workshop on Networked Robotics, IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS-04) Sendai, Japan, Sep 24-Oct 2, 2004 Principled Approaches to the Design of Multi-Robot Systems Chris
More informationHedonic Coalition Formation for Distributed Task Allocation among Wireless Agents
Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents Walid Saad, Zhu Han, Tamer Basar, Me rouane Debbah, and Are Hjørungnes. IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 10,
More informationEfficiency of Dynamic Arbitration in TDMA Protocols
Efficiency of Dynamic Arbitration in TDMA Protocols April 22, 2005 Jens Chr. Lisner Introduction Arbitration methods in TDMA-based protocols Static arbitration C1 C1 C2 C2 fixed length of slots fixed schedule
More informationTimed Games UPPAAL-TIGA. Alexandre David
Timed Games UPPAAL-TIGA Alexandre David 1.2.05 Overview Timed Games. Algorithm (CONCUR 05). Strategies. Code generation. Architecture of UPPAAL-TIGA. Interactive game. Timed Games with Partial Observability.
More informationLecture 13 Register Allocation: Coalescing
Lecture 13 Register llocation: Coalescing I. Motivation II. Coalescing Overview III. lgorithms: Simple & Safe lgorithm riggs lgorithm George s lgorithm Phillip. Gibbons 15-745: Register Coalescing 1 Review:
More informationMobile Crowdsensing enabled IoT frameworks: harnessing the power and wisdom of the crowd
Mobile Crowdsensing enabled IoT frameworks: harnessing the power and wisdom of the crowd Malamati Louta Konstantina Banti University of Western Macedonia OUTLINE Internet of Things Mobile Crowd Sensing
More informationEvent-Driven Scheduling. (closely following Jane Liu s Book)
Event-Driven Scheduling (closely following Jane Liu s Book) Real-Time Systems, 2009 Event-Driven Systems, 1 Principles Admission: Assign priorities to Jobs At events, jobs are scheduled according to their
More informationPlanning to Fail: Incorporating Reliability into Design and Mission Planning for Mobile Robots
Planning to Fail: Incorporating Reliability into Design and Mission Planning for Mobile Robots Stephen B. Stancliff CMU-RI-TR-09-38 Submitted in partial fulfillment of the requirements for the degree of
More informationRobotic Systems ECE 401RB Fall 2007
The following notes are from: Robotic Systems ECE 401RB Fall 2007 Lecture 14: Cooperation among Multiple Robots Part 2 Chapter 12, George A. Bekey, Autonomous Robots: From Biological Inspiration to Implementation
More informationMulti-Agent Planning
25 PRICAI 2000 Workshop on Teams with Adjustable Autonomy PRICAI 2000 Workshop on Teams with Adjustable Autonomy Position Paper Designing an architecture for adjustably autonomous robot teams David Kortenkamp
More informationCollaborative Robotic Navigation Using EZ-Robots
, October 19-21, 2016, San Francisco, USA Collaborative Robotic Navigation Using EZ-Robots G. Huang, R. Childers, J. Hilton and Y. Sun Abstract - Robots and their applications are becoming more and more
More informationDealing with Perception Errors in Multi-Robot System Coordination
Dealing with Perception Errors in Multi-Robot System Coordination Alessandro Farinelli and Daniele Nardi Paul Scerri Dip. di Informatica e Sistemistica, Robotics Institute, University of Rome, La Sapienza,
More informationNon-preemptive Coflow Scheduling and Routing
IEEE Globecom 2016 SAC-ANS 3 Non-preemptive Coflow Scheduling and Routing Ruozhou Yu, Guoliang Xue, and Xiang Zhang Arizona State University Jian Tang Syracuse University 1/22 Outline q Introduction and
More informationNew task allocation methods for robotic swarms
New task allocation methods for robotic swarms F. Ducatelle, A. Förster, G.A. Di Caro and L.M. Gambardella Abstract We study a situation where a swarm of robots is deployed to solve multiple concurrent
More informationGilbert Peterson and Diane J. Cook University of Texas at Arlington Box 19015, Arlington, TX
DFA Learning of Opponent Strategies Gilbert Peterson and Diane J. Cook University of Texas at Arlington Box 19015, Arlington, TX 76019-0015 Email: {gpeterso,cook}@cse.uta.edu Abstract This work studies
More informationMission Reliability Estimation for Repairable Robot Teams
Carnegie Mellon University Research Showcase @ CMU Robotics Institute School of Computer Science 2005 Mission Reliability Estimation for Repairable Robot Teams Stephen B. Stancliff Carnegie Mellon University
More informationEnergy Efficient Scheduling Techniques For Real-Time Embedded Systems
Energy Efficient Scheduling Techniques For Real-Time Embedded Systems Rabi Mahapatra & Wei Zhao This work was done by Rajesh Prathipati as part of his MS Thesis here. The work has been update by Subrata
More informationVariable Bit Rate Transmission Schedule Generation in Green Vehicular Roadside Units
Variable Bit Rate Transmission Schedule Generation in Green Vehicular Roadside Units Abdulla A. Hammad 1, Terence D. Todd 1 and George Karakostas 2 1 Department of Electrical and Computer Engineering McMaster
More informationLogic Solver for Tank Overfill Protection
Introduction A growing level of attention has recently been given to the automated control of potentially hazardous processes such as the overpressure or containment of dangerous substances. Several independent
More informationCoordination in dynamic environments with constraints on resources
Coordination in dynamic environments with constraints on resources A. Farinelli, G. Grisetti, L. Iocchi and D. Nardi Dipartimento di Informatica e Sistemistica Università La Sapienza, Roma, Italy Abstract
More informationAn Agent-based Heterogeneous UAV Simulator Design
An Agent-based Heterogeneous UAV Simulator Design MARTIN LUNDELL 1, JINGPENG TANG 1, THADDEUS HOGAN 1, KENDALL NYGARD 2 1 Math, Science and Technology University of Minnesota Crookston Crookston, MN56716
More informationOpportunistic Communications under Energy & Delay Constraints
Opportunistic Communications under Energy & Delay Constraints Narayan Mandayam (joint work with Henry Wang) Opportunistic Communications Wireless Data on the Move Intermittent Connectivity Opportunities
More informationENHANCED HUMAN-AGENT INTERACTION: AUGMENTING INTERACTION MODELS WITH EMBODIED AGENTS BY SERAFIN BENTO. MASTER OF SCIENCE in INFORMATION SYSTEMS
BY SERAFIN BENTO MASTER OF SCIENCE in INFORMATION SYSTEMS Edmonton, Alberta September, 2015 ABSTRACT The popularity of software agents demands for more comprehensive HAI design processes. The outcome of
More informationMobile Robot Task Allocation in Hybrid Wireless Sensor Networks
Mobile Robot Task Allocation in Hybrid Wireless Sensor Networks Brian Coltin and Manuela Veloso Abstract Hybrid sensor networks consisting of both inexpensive static wireless sensors and highly capable
More informationFIFO WITH OFFSETS HIGH SCHEDULABILITY WITH LOW OVERHEADS. RTAS 18 April 13, Björn Brandenburg
FIFO WITH OFFSETS HIGH SCHEDULABILITY WITH LOW OVERHEADS RTAS 18 April 13, 2018 Mitra Nasri Rob Davis Björn Brandenburg FIFO SCHEDULING First-In-First-Out (FIFO) scheduling extremely simple very low overheads
More informationConstellation Scheduling Under Uncertainty: Models and Benefits
Unclassified Unlimited Release (UUR) Constellation Scheduling Under Uncertainty: Models and Benefits GSAW 2017 Securing the Future March 14 th 2017 Christopher G. Valica* Jean-Paul Watson *Correspondence:
More informationMulti-robot Heuristic Goods Transportation
Multi-robot Heuristic Goods Transportation Zhi Yan, Nicolas Jouandeau and Arab Ali-Chérif Advanced Computing Laboratory of Saint-Denis (LIASD) Paris 8 University 93526 Saint-Denis, France Email: {yz, n,
More informationUncertainty Feature Optimization for the Airline Scheduling Problem
1 Uncertainty Feature Optimization for the Airline Scheduling Problem Niklaus Eggenberg Dr. Matteo Salani Funded by Swiss National Science Foundation (SNSF) 2 Outline Uncertainty Feature Optimization (UFO)
More informationCyber-Physical Systems: Challenges for Systems Engineering
Cyber-Physical Systems: Challenges for Systems Engineering agendacps Closing Event April 12th, 2012, EIT ICT Labs, Berlin Eva Geisberger fortiss An-Institut der Technischen Universität München Cyber-Physical
More informationUsing Critical Junctures and Environmentally-Dependent Information for Management of Tightly-Coupled Cooperation in Heterogeneous Robot Teams
Using Critical Junctures and Environmentally-Dependent Information for Management of Tightly-Coupled Cooperation in Heterogeneous Robot Teams Lynne E. Parker, Christopher M. Reardon, Heeten Choxi, and
More informationRobot formations: robots allocation and leader follower pairs
200 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 200 Robot formations: robots allocation and leader follower pairs Sérgio Monteiro Estela Bicho Department of Industrial
More informationHuman-Swarm Interaction
Human-Swarm Interaction a brief primer Andreas Kolling irobot Corp. Pasadena, CA Swarm Properties - simple and distributed - from the operator s perspective - distributed algorithms and information processing
More informationHow (Information Theoretically) Optimal Are Distributed Decisions?
How (Information Theoretically) Optimal Are Distributed Decisions? Vaneet Aggarwal Department of Electrical Engineering, Princeton University, Princeton, NJ 08544. vaggarwa@princeton.edu Salman Avestimehr
More informationMobile Terminal Energy Management for Sustainable Multi-homing Video Transmission
1 Mobile Terminal Energy Management for Sustainable Multi-homing Video Transmission Muhammad Ismail, Member, IEEE, and Weihua Zhuang, Fellow, IEEE Abstract In this paper, an energy management sub-system
More informationECE2019 Sensors, Circuits, and Systems A2015. Lab #1: Energy, Power, Voltage, Current
ECE2019 Sensors, Circuits, and Systems A2015 Lab #1: Energy, Power, Voltage, Current Introduction This lab involves measurement of electrical characteristics for two power sources: a 9V battery and a 5V
More informationColumn Generation. A short Introduction. Martin Riedler. AC Retreat
Column Generation A short Introduction Martin Riedler AC Retreat Contents 1 Introduction 2 Motivation 3 Further Notes MR Column Generation June 29 July 1 2 / 13 Basic Idea We already heard about Cutting
More informationIntelligent Power Economy System (Ipes)
American Journal of Engineering Research (AJER) e-issn : 2320-0847 p-issn : 2320-0936 Volume-02, Issue-08, pp-108-114 www.ajer.org Research Paper Open Access Intelligent Power Economy System (Ipes) Salman
More informationWORLDSKILLS STANDARD SPECIFICATION
WORLDSKILLS STANDARD SPECIFICATION Skill 04 Mechatronics WSC2015_WSSS04 THE WORLDSKILLS STANDARDS SPECIFICATION (WSSS) GENERAL NOTES ON THE WSSS The WSSS specifies the knowledge, understanding and specific
More information