Chain-based Reconfigurable Robots: SuperBot and it’s applications

1. Chain-based Reconfigurable Robots: SuperBot and it’s applications Ilknur Kaynar-Kabul Fall 2006

2. 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.

3. Self-reconfigurable robots • Lattice-based reconfigurable robots • Chain-based reconfigurable robots • Polybot • Conro • SuperBot • Hybrid systems • M-TRAN module • Tetrobot

4. 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

5. SuperBot • Each module has • 3 revolute joints • 6 genderless connectors • 2 Atmega 128 CPUs • Some modules have wireless capabilities, video cameras

6. 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

7. 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.

8. 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.

9. 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.

10. 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

11. 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

12. Main issues • Computing the mass properties • Stabilizing Behavior

13. 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

14. 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

15. Algorithm for Mass Computation

16. 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 Algorithm for Mass Computation

17. 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

18. 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

19. 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

20. 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 Rθis a 3-by-3 rotation matrix representing a rotation of θ radians about u.

21. 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

22. 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

23. 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

24. 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:

25. 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

26. 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

27. Performance for Robot (a)

28. Performance for Robot (b)

29. Performance for Robot (c)

30. Performance for Robot (d)

31. 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

32. 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

33. 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.

34. 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

35. 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

36. 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

37. 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

38. Locomotion modes • Each mode consists of • characteristics for the environment type • speed • turning-ability • energy-efficiency • recoverability from failures

39. 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

40. 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

41. The 6M-loop mode

42. 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

43. 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.

44. The 10C-Loop ModeRecovery from fall down

45. 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

46. 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

47. 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

48. 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

49. 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

50. The 6M4C-training-wheel modeRecovering 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