Polymorphic Robotics at ISI

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Information about Polymorphic Robotics at ISI

Published on January 7, 2008

Author: Lassie

Source: authorstream.com

ISI Polymorphic Robotics Lab http://www.isi.edu/robots:  ISI Polymorphic Robotics Lab http://www.isi.edu/robots Mission To build Self-Reconfigurable Systems such as metamorphic robots, agents, and smart structures that go where biological systems have not gone before!!! Projects and Awards YODA (1996) The 2nd place in AAAI competition Dreamteam (1997) RoboCup World Champion Intelligent Motion Surface in MEMS (1996-98) CONRO Self-Reconfigurable Robots (1998-) People, Robots and Facilities Experienced and talented research team 3 Denny robots, 5 SoccerBots, 18 CONRO modls Large labs and workshops, many instrumentations Self-Reconfigurable Robots:  Self-Reconfigurable Robots CONRO Self-Reconfigurable Modules:  CONRO Self-Reconfigurable Modules A network of physically coupled agents Self-assembling into various configurations! Relentless CONRO Robots:  Relentless CONRO Robots “Live Surgery” Reconfiguration:  “Live Surgery” Reconfiguration Beyond-Bio Self-Reconfiguration:  Beyond-Bio Self-Reconfiguration Challenges in Control :  Challenges in Control Distributed Autonomous modules must be coordinated by local configuration information (no unique IDs or brain modules) Dynamic Network and configuration topology changes Asynchronous Communication with no real-time clocks, global or local Scalable The size and shape are not determined dynamically Fault-tolerant Miniature and self-sufficient Digital Hormones:  Digital Hormones Content-based messages No addresses nor identifiers Have finite life time Trigger different actions at different sites Floating in a global medium Propagated, not broadcast Internal circulation, not external deposit (pheromones) Preserve local autonomy for individual sites Hormones can modify module behaviors (RNA) The Uses of Digital Hormones:  The Uses of Digital Hormones Communication in dynamic network Cooperation among distributed autonomous modules Locomotion Reconfiguration Synchronization Global effects by weak local actions Conflict resolution (multi hormone management) Navigation Shape adaptation and self-repairing Hormones for Caterpillar Move:  A simple one-pass hormone from head to tail Controls and synchronizes all motor actions Independent from the length of the snake Hormones for Caterpillar Move Reconfigure Insect  Snake:  Reconfigure Insect  Snake Autonomous Docking:  Autonomous Docking A great challenge for self-reconfiguration Require precise sensor guidance Demand precision movement Complex dynamics in micro-gravity environment Self-Assembly in Space:  Self-Assembly in Space Cost Effective For a 10km long SSPS >2,500 hours of astronaut space walk 4/11/2002, girder assembly (2*6 hours) >$5 billion for assembly cost Feasible Strategy Most jobs by self-assembly Critical jobs done by astronauts A Vision for Space Self-Assembly:  A Vision for Space Self-Assembly Three Enabling Technologies:  Three Enabling Technologies Intelligent and Reconfigurable Component (IRC) Can free-float and dock to form structures Distributed Process Controller (DPC) Can plan self-assembly in a distributed manner and recover from unexpected situations Free-flying Fiber Match-Maker Robots (FIMER) Can search, navigate, bring-together and dock IRCs Intelligent Reconfigurable Components:  Intelligent Reconfigurable Components An IRC has (1) a controller, (2) a set of named connectors, (3) wireless communication, (4) self-locating system, and (5) short-range sensors for docking guidance Reconfigurable Connectors:  Reconfigurable Connectors The Global Process Control:  The Global Process Control How do modules know when and where to connect? Advantages for distributed control Coordination of autonomous modules without fixed brain Support dynamic configuration topology Asynchronous: communication without global clocks Scalable: support growing structures Fault-tolerance Self-repairing capability Self-replanning for unexpected events Proposed Process Control:  Proposed Process Control Assumptions Components/connectors have unique identifiers Assembly sequence embedded in components Procedures Activate self when receiving a call for its ID or type Call FIMER robots to assist docking (when activated) Activate the next connectors to be docked FIMER Robots:  FIMER Robots Two-headed robot with fiber/rope Free-flying head (6DOF) Navigate and dock to the connectors Rail-in fiber to bring parts together Simple arms to assist dock Onboard power or refuel capability FIMER Dynamics and Control:  FIMER Dynamics and Control Find relevant connectors based on their location information Railing in the fiber only when there is no tension Research Issues: * Dynamics of tethered objects in zero-gravity environment * Speed control * Collision control * Prevent tangling Proposed Experiments:  Proposed Experiments Build modules for autonomous planning, navigation, & docking “2D flight-test” on an air hockey table Extensible to future 3D flight-test in micro-gravity environment Flying Module Prototype:  Flying Module Prototype Slide24:  ISI Polymorphic Robotics Laboratory http://www.isi.edu/robots A Model of Flying Puck:  A Model of Flying Puck M(n)n’ + C(n)n + Dn + g(h) = t M(n) is the moment of inertia matrix, C(n) is the centrifugal and Coriolis forces, D is the damping matrix, and n =[u v r]’ is the velocity vector and h=[x y y]’ is the configuration vector g(h) is the vector of gravitational and buoyant forces and t = [Tu Tv Tr] is the control forces and torques The Control of Flying Puck:  The Control of Flying Puck Using vision to sense the location/speed Analog control of speed via wireless comm. Close-loop control The desired x and y position are the target position The desired orientation is equal to the “target angle” so that the vehicle is always facing the target.  Tu = m(Tx cos y + Ty sin y) Tv = 0 Tr = Iz Ty Conclusions and Future Work:  Conclusions and Future Work Self-reconfigurable robots provide a very rich source for new research problems Hormone-inspired approach is very promising direction for distributed control Self-assembly in space is a good application of self-reconfiguration Future directions: Environmental driven self-reconfiguration New generation of enhanced and robust modules Hormone-based programming

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