28b

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Published on January 2, 2008

Author: Chan

Source: authorstream.com

Content

Chain-based Reconfigurable Robots: SuperBot and it’s applications:  Chain-based Reconfigurable Robots: SuperBot and it’s applications Ilknur Kaynar-Kabul Fall 2006 Overview:  Overview SuperBot A Deployable, Multi-Functional, and Modular Self-Reconfigurable Robotic System Distributed Control of the Center of Mass of a Modular Robot Mark Moll, Peter Will, Maks Krivokon, and Wei-Min Shen. In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, Beijing, China, October 2006. Multimode Locomotion via SuperBot Robots Wei-Min Shen, Maks Krivokon, Harris Chiu, Jacob Everist, Michael Rubenstein, and Jagadesh Venkatesh In Proc. 2006 IEEE Intl. Conf. on Robotics and Automation, pp. 2552–2557, Orlando, FL, 2006. Self-reconfigurable robots:  Self-reconfigurable robots Lattice-based reconfigurable robots Chain-based reconfigurable robots Polybot Conro SuperBot Hybrid systems M-TRAN module Tetrobot SuperBot:  SuperBot SuperBot is a modular robot that consists of many reconfigurable modules that can demonstrate multifunction and reconfiguration [Salemi 2006] SuperBot is being designed for NASA space exploration programs SuperBot:  SuperBot Each module has 3 revolute joints 6 genderless connectors 2 Atmega 128 CPUs Some modules have wireless capabilities, video cameras SuperBot:  SuperBot More flexible, mobile and efficient compared to the existing robots A module can perform different gaits (e.g., caterpillar, sidewinder, push-and-pull, etc.) and turn and flip without any external help Modules can be packaged in a way that is appropriate for transportation Distributed Control of the Center of Mass of a Modular Robot:  Distributed Control of the Center of Mass of a Modular Robot Mark Moll, Peter Will, Maks Krivokon, and Wei-Min Shen. In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, Beijing, China, October 2006. Motivation:  Motivation Much of work on modular and self-reconfigurable robots focuses on Specific design of robots Reconfiguration planning Gait development Few work on locomotion of modular robots in the presence of uncertainty - uneven and unknown terrain. Idea of the paper:  Idea of the paper A robot can prevent itself from falling over by controlling the center of mass (COM) Uses a gait only as a guideline for locomotion Uses contact information & mass information to ensure a stable pose at all times. Overview of the approach (1):  Overview of the approach (1) Presents a distributed and decentralized algorithm that computes the mass properties of the robot at each step Modules compute the total mass, the center of mass (COM) and the inertia tensor This information enables a module to compute joint displacements that will move the COM towards a desired position Overview of the approach (2):  Overview of the approach (2) A gait is specifies where the COM needs to go and which leg needs to be moved, rather than specifying joint angle for every module. Advantage: Simplify the specification of a gait Allow a modular robot to move over uneven terrain Main issues:  Main issues Computing the mass properties Stabilizing Behavior Computing the mass properties:  Computing the mass properties Assumption: the modules are connected to form a tree-like structure, i.e. there are no loops Each module computes the mass properties of the whole system Based on its own state and on information it receives from its neighbors It receives an estimate of the mass properties from a given connector of just the modules that are connected (directly or indirectly) to that connector Computing the mass properties:  Computing the mass properties A module sends new estimate to its neighbors when the modules move If the modules do not move, the modules will eventually all converge to the true mass properties and stop sending updates to each other Algorithm for Mass Computation:  Algorithm for Mass Computation Algorithm for Mass Computation:  Algorithm for Mass Computation After d iterations of the main loop, each module will have computed the correct COM, assuming the modules do not move d: largest tree distance between 2 modules Stabilizing Behavior:  Stabilizing Behavior To stabilize an arrangement of modules Change the joint angles in the modules OR Rearrange the modules OR Combination of both Option 2 can be slower than option 1 Stable configuration for a simple module:  Stable configuration for a simple module General idea: A configuration is stable if the contact forces can balance the gravitational force Simple case: One point of contact and no friction Stable if the center of mass lies on the support line Support line: the vertical line through the point of contact If it is not stable, then each module should adjust its joint angles Simple case: Revolute joint:  Simple case: Revolute joint Consider one revolute joint: One side of the joint is connected to the contact point and the other side attached to it move along an arc of a circle Simple case: Revolute joint:  Simple case: Revolute joint p1: COM of the part of the system that remains fixed p2: COM of the part of the system that is going to be rotated q: the position of the joint w = p2 − q Rθ is a 3-by-3 rotation matrix representing a rotation of θ radians about u. Stabilizing all revolute joints:  Stabilizing all revolute joints Finding optimal displacements for all joints simultaneously is very difficult Solution: Use an approximate solution which tends to converge to a desired configuration very quickly. Each joint computes its own optimal displacement independently of each other Solving oscillation problem :  Solving oscillation problem This solution computes a desired direction to move in for all modules Problem: Modules can oscillate around the support line due to the momentum Solution: 2 heuristics Based on the distance between the estimated COM and the support line Based on momentum Heuristic 1: Distance based:  Heuristic 1: Distance based Reduce the gains as the COM gets closer to the support line, so that the robot does not overshoot the goal position. Proportional gain is adjusted as follows: c0 and c1 are constants dsupport is the distance to the support line KP0 is the nominal proportional gain Heuristic 2: Momentum based:  Heuristic 2: Momentum based An ensemble of modules should not gain too much momentum For each joint, consider the mass and the distance to the joint of the COM of the modules that will be moved by this joint Proportional gain is adjusted as follows: Simulation Results:  Simulation Results Random trees of modules are used as robots 20 modules divided into 4 branches of 5 modules Each module has 3 DOF, the whole tree has 60 DOF The root is always in vertical direction and fixed to the ground Simulation Results:  Simulation Results To evaluate the performance, distance between the COM and the support line as function of time is used Tested on 3 different control schemes: Default: The gains on all modules are identical and constant Distance: The gains depend on the estimated distance to the support line Momentum: The gains depend on the momentum Performance for Robot (a):  Performance for Robot (a) Performance for Robot (b):  Performance for Robot (b) Performance for Robot (c):  Performance for Robot (c) Performance for Robot (d):  Performance for Robot (d) Conclusion:  Conclusion Presents the feasibility of using distributed control to move the COM of a modular robot to a desired position Control methods with heuristics move the COM to a desired position No control method outperforms the others Momentum heuristic gives the best overall behavior All methods exhibit the desired behavior most of the time Future work:  Future work The performance can be improved if each module computes the optimal joint angles for all three joints simultaneously Inertia tensor can be used in balancing the behavior External forces, such as gravity and friction, at the contact points can be taken into account Multimode Locomotion via SuperBot Robots:  Multimode Locomotion via SuperBot Robots Wei-Min Shen, Maks Krivokon, Harris Chiu, Jacob Everist, Michael Rubenstein, and Jagadesh Venkatesh In Proc. 2006 IEEE Intl. Conf. on Robotics and Automation, pp. 2552–2557, Orlando, FL, 2006. Overview:  Overview Presents SuperBot for multiple locomotion modes based on reconfigurable modules Shows the validity of the SuperBot for the movements of forward, backward, turn, sidewinder, maneuver, and travel on batteries up to 500 meters on a flat terrain Multimode locomotion:  Multimode locomotion Multimode locomotion : Ability to use different moving modes in different environments. “climb” if it is to go up a slope “run” if it is to cover more distance with less energy “balance” if the terrain is rugged and uneven “get up on feet” if it fell down by mistake Multimode locomotion:  Multimode locomotion To support multimode locomotion, a robot must have at least four capabilities. it must be able to perform different locomotion mode. it must be able to recover from unexpected locomotion failures. it must be able to shift from one mode to another. it must be able to choose the correct mode for the correct environment. This paper focuses Multimode locomotion:  Multimode locomotion 2 competing and even conflicting criteria for multimode locomotion: the robot must be general To deal with many types of environments and difficulty tasks the robot must be special To achieve goals with greater efficiency. Reconfigurable robots can achieve these goals Locomotion modes:  Locomotion modes Each mode consists of characteristics for the environment type speed turning-ability energy-efficiency recoverability from failures The 6M-loop mode:  The 6M-loop mode 6 M-modules are in a ring configuration of hexagon shape Advantage: Energy efficient and allows high speeds Disadvantage: Tolerance to environment obstacles is limited by the size of the wheel The robot cannot stand up once it falls down The 6M-loop mode:  The 6M-loop mode Shapes alter between a regular hexagon and a deformed hexagon that tends to fall forward. Starting from the regular hexagon, the movement is controlled by the deformation of the shape to change the centre of gravity of the traveller. 2 commands governing the shape transformation: One is to retain the regular hexagon shape. One is to let the rolling traveller to “squeeze” itself to a deformed hexagon. Commands are selected using gravity sensors The 6M-loop mode:  The 6M-loop mode The 10C-Loop Mode:  The 10C-Loop Mode Uses all CONRO-like modules each module can control its pitch and yaw movement Flexible and can run, turn, and recover from falling down Can deal with environments where obstacles do not exceed in size the height of the robot configuration The 10C-Loop Mode:  The 10C-Loop Mode Achieves the rolling track locomotion At a fixed time interval (OR when all modules have bended forward to the desired angle) each module begins to bend forward again to reach the angle that is equal to the current angle of the module that is in front of it. When this process repeats, the rolling track will move forward in a straight path. The 10C-Loop Mode Recovery from fall down:  The 10C-Loop Mode Recovery from fall down The 9M-walker mode:  The 9M-walker mode H-Walker is a 4-legged walker using 2 DOF on each module 3 possible local topologies: Torso, upper leg, and lower leg The 9M-walker mode:  The 9M-walker mode Distributed locomotion control was achieved using the digital hormone method [Shen 2002] 4 hormones are used to control each leg Torso sends the hormone messages to the legs and synchronizes their coordinated actions The 9M-walker mode:  The 9M-walker mode H-walker mode has symmetric design Prevents it from falling into any unrecoverable position Its topology is in the shape of an 'H' Can walk forwards and backwards using the same strategy The 9M-walker mode:  The 9M-walker mode Fall down: It is easy to achieve the relaxed position in which the legs are straightened out to the sides in a double-caterpillar shape. It stands up using the following steps The 6M4C-training-wheel mode:  The 6M4C-training-wheel mode Modified version of 6M Added 4 extra legs as “training wheels” to 6M-loop It can run fast, and can turn and recover from falling The 6M4C-training-wheel mode Recovering from falling:  The 6M4C-training-wheel mode Recovering from falling Straightens all the “leg” modules and collapses the hexagon to a flat loop The hexagon plane can then be made vertical and the flat loop will change back to its hexagon shape and continue to roll The 2M4C-loop mode:  The 2M4C-loop mode It uses 6 modules for the loop : MCCMCC It alternates the types of module to enable the loop to turn and recover from falling The 2M4C-loop mode Recovery from fall down:  The 2M4C-loop mode Recovery from fall down The loop straightens itself by bending the 2 Mmodules into 180 degrees Resets the shape of all 4 C-modules The C-modules then change their yaw servos so that the robot is rising up yet unbalanced. The unusual movements of the C-modules will cause the robot to fall sideways The loop will then straighten up again Goes back to its original hexagon shape The 2M4C-loop mode Recovery from fall down:  The 2M4C-loop mode Recovery from fall down The 8M-climbing mode:  The 8M-climbing mode 8 M-shape Superbot modules forming a rolling track that is only 1.5-module in height The advantage of this configuration is to make use its low height property to stabilize it on the slope The mode climbs up the slope slowly by moving module by module The 8M-climbing mode:  The 8M-climbing mode Conclusion:  Conclusion Presents the concept of multimode locomotion for the Superbot robot and a list of locomotion modes The effectiveness of these modes are demonstrated by the Superbot modules and configurations in simulation Future work: the process of how to reconfigure the robot from one mode to another through self-reconfiguration References:  References [Shen 2002] W.-M. Shen, B. Salemi, and P. Will, Hormone-Inspired Adaptive Communication and Distributed Control for CONRO Self-Reconfigurable Robots, IEEE Transactions on Robotics and Automation, 18(5), October, 2002. [Salemi 2006] Behnam Salami, Mark Moll, and Wei-Min Shen. SUPERBOT: A Deployable, Multi-Functional, and Modular Self-Reconfigurable Robotic System. In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, Beijing, China, October 2006. [Moll 2006] Mark Moll, Peter Will, Maks Krivokon, and Wei-Min Shen, Distributed Control of the Center of Mass of a Modular Robot,In Proc. 2006 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, Beijing, China, October 2006. [Shen 2006] Wei-Min Shen, Maks Krivokon, Harris Chiu, Jacob Everist, Michael Rubenstein, and Jagadesh Venkatesh, Multimode Locomotion via SuperBot Robots, In Proc. 2006 IEEE Intl. Conf. on Robotics and Automation, pp. 2552–2557, Orlando, FL, 2006. Questions:  Questions

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