Introduction to Robotics & Multi-robot systems Speaker : Wen-Chieh Fang Time : 2005/08

Agenda  The Study of Agency  Related Courses  Mobile robots  Architecture  Hierarchical Paradigm  Reactive Paradigm  Hybrid Paradigm  Communication  5 Categories of Communication  Communication Structure  What Do Robots Say to Each Other?  Languages for multi-agents  Applications  Multi-robot Sensing  Sensory coverage  Control  Reference

The Study of Agency (after Stone and Veloso 2002) [Murphy 2000 slides] Distributed Artificial Intelligence Distributed Problem Solving Multi- Agent Systems How to solve problems Or meet goals by “divide and conquer” Single computer: How to decompose task? How to synthesize solutions? Divide among agents: Who to subcontract to? How do they cooperate?

Related Courses  Robotics  Artificial Intelligence  Distributed Artificial Intelligence (DAI)  Multi-agent systems  Animal behavior (optional)

Mobile robots  Navigation  Maximum Navigation Test (MNT) The robot is placed in an environment that is unknown, large, complex and dynamic. After a time needed by the robot to explore the environment, the robot must be able to go to any selected place, trying to minimize a cost function (e.g. time, energy, etc).

Mobile robots (Cont.)  Motion Control problem  World Modeling problem  Localization problem  Planning problem  Architecture problem

Architecture  Hierarchical Paradigm  Reactive Paradigm  Hybrid Paradigm

Hierarchical Paradigm  Organization PLANSENSEACT World model: 1.A priori rep 2.Sensed info 3.Cognitive

Reactive Paradigm  Vertical decomposition of tasks

Hybrid Paradigm  Organization

5 Categories of Communication [Murphy 2000 slides]  Infinite  comms are free  Motion  costs as much to communicate as it would to move  ex. Box pushing (if other robot can feel the box, it ’ s comms)  Low  comms costs more than moving from one location to another  Zero  no communication between agents  Topology  Broadcast, address, tree, graph

Communication Structure  Interaction via Environment :  Environment is the communication medium (a shared memory)  Interaction via Sensing :  Without explicit communication  Interaction via Communications :  Explicit communication by either directed or broadcast intentional messages Adopted from [ Parker et.al.2003 ] Adopted from [ Yoshida et.al ]

What Do Robots Say to Each Other? [Murphy 2000 slides]  How do they “ talk ” ?  Implicit: signaling, postures, smell  Explicit: language  Who does the talking?  “ the boss ” -Centralized control  Everybody - Distributed control

What do Robots Say? (after Jung and Zelinsky 02) [Murphy 2000 slides]  Communication without meaning preservation  Emitter can ’ t interpret its own signal  Receiver reacts in a specific way (stimulus-response)  Ex. Mating displays, bacteria emit chemicals  Communication with meaning preservation  Shared common representation  Ex. Ant leaves pheromone trail to food, itself & peers can follow  Ex. Wolves leave scent markings

Languages for multi-agents  To abstract the important information and minimize explicit communication  Does an increase on the amount of transmitted data imply better performance?  Does an increase on the amount of transmitted data imply better performance? [ Castelpietra et. al ]  How to make agents to speak the “ same language ” ? (how to translate syntactically and semantically the data or information structures of the sender to the receiver?)  How to make agents to speak the “ same language ” ? (how to translate syntactically and semantically the data or information structures of the sender to the receiver?) [ Ye et. al ]  How to make agents mean the same “ meaning ” when they communicate? (how to make sure that agents use the same ontology?)  How to make agents mean the same “ meaning ” when they communicate? (how to make sure that agents use the same ontology?) [ Ye et. al ]

Multi-robot Sensing [Murphy 2000]  Proprioceptive sensors ( which robots measures a signal originating within itself ):  Shaft encoder  GPS  Proximity sensors :  Sonar or ultrasonics  Infrared (IR)  Bump and feeler sensors  Computer Vision  Range from vision  Stereo camera pairs  Light stripers  Laser ranging Adopted from [ Werger & Mataric 2000 ]

Sensory coverage  Topics  Target tracking/search  Variations  Numbers & speeds of sensor & targets  Communication, sensing & movement capabilities  Terrain  Predictability of targets  Multi-sensor fusion Adopted from [ Jung & Sukhatme 2002 ]

Control  Centralized control  Distributed control

Reference  English reference  R. R. Murphy, Introduction to AI Robotics. The MIT Press,  Chinese reference  彼得‧曼瑟, 費斯‧德魯修著, “ 機器人的進化 : 人工智慧與機器人 學的新世紀 ”, 商周出版, 2002  羅德尼‧布魯克斯著, " 我們都是機器人：人機合一的大時代 ", 究 竟, 2003  漢斯‧摩拉維克著, " 機器人：由機器邁向超越人類心智之路 ", 台 灣商務, 2004

Reference   [ Castelpietra et. al ] C. Castelpietra, L. Iocchi, D. Nardi, and R. Rosati, “Coordination in multi-agent autonomous cognitive robotic systems,” in Proceedings of 2nd International Cognitive Robotics Workshop,   [ Ye et. al ] Y. Ye, S. Boies, J. Liu, and X. Yi, “Collective perception in massive, open, and heterogeneous multi-agent environment,” in Proceedings of 1st International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS’02), 2002.