1. Distributed Adaptive Locomotion Learning in ModRED Modular Self-Reconfigurable Robot
Ayan Dutta1, Prithviraj Dasgupta1, Carl Nelson2
1 University of Nebraska at Omaha, 2University of Nebraska-Lincoln
Why Modular Robots?
Modular self-reconfigurable robots (MSR) are multiple identical (for unit modular system) and interchangeable autonomous robot modules.
MSR are capable of interfacing and communicating with each other.
Highly scalable, reconfigurable and adaptable.
Highly robust in unstructured environments.
M-TRAN modular robot by Murata et al. [2007] & Superbot by Shen et al. [2008]
Abstract
We study the problem of adaptive locomotion learning for modular self-reconfigurable robots (MSRs). MSRs are mostly used in unknown and difficult-to-navigate environments where they can take a completely new shape to accomplish the current task at hand. Therefore it is almost impossible to develop the control sequences for all possible configurations with varying shape and size. The modules have to learn and adapt their locomotion in dynamic time to be more robust in nature. In this paper, we propose a Q-learning based locomotion adaptation strategy which balances exploration versus exploitation in a more sophisticated fashion. We have applied our proposed strategy mainly on the ModRED modular robot within the Webots simulator environment. To show the generalizability of our approach, we have also applied it on a Yamor modular robot. Experimental results show that our proposed technique outperforms a random locomotion strategy and it is able to adapt to module failures.
Exploration and Discovery
A scalable and robust robot system is required to explore unstructured terrains such as planetary surfaces
Wheeled and legged rovers are not scalable
A sustainable planetary exploration needs a robot system capable of self-healing and multitasking
Wheeled locomotion: NASA Mars rover and legged locomotion: DARPA BigDog
ModRED Module Quick Facts
Mechanical:
Electronic:
Proposed Approach
We have proposed a stateless Q-learning based strategy for adaptive locomotion pattern generation for modular self-reconfigurable robots.
Each module learns the best action to perform from its own past actions as well as from the relationship between its own actions and its neighboring modules’ actions.
Our approach has also been shown to be adaptive to module failures – if one or more modules become un-operational during the mission, then other modules will adapt their locomotion patterns to keep moving.
Demonstrated Locomotion Patterns
Inchworm locomotion in both ModRED and Yamor robots.
Rolling locomotion in ModRED robot.
Size (inches)
14.5 × 4.5 × 4.7
Weight (lbs)
6.5
Primary Material
Aluminum
DOF
RRPR (Independent DOF)
Actuators
4 (3 bipolar steppers, 1 bipolar stepper linear actuator)
Number of docking faces
2 ( Up to 5 in improved connector)
Type of docking
Mechanical latching (Mechanical locking in improved module)
Type of modular robot system
Chain (Improved modules are hybrid)
Dimension 3D
Processing
Arduino Fio (Atmel ATmega328P)
Motor Driver
Easy Driver Stepper Motor Driver (8 step microstepping, 750 mA per phase current rating)
Sensing
Infrared with a range of 4-30 cm
Bump switches for tactile sensing
Navigation
IMU modules (9 dof Razor Inertial Measurement Unit)
Communication
XBee radio modems (Unobstructed range: 120m, transmission power: 1 mW)
Power source
3.7 V Lithium-polymer (Li-Po) battery packs
ModRED Module
Various Configurations with ModRED II
five module quadruped
scorpion-like configuration
six-module configuration with elevator platform
four-wheel-drive vehicle
simple hexapod walker
hybrid configuration: front three legs used for legged locomotion; tail can be used for a forward rolling locomotion
Ongoing and Future Work
Applying proposed method on various ModRED II configurations (shown above).
Modification of the algorithm for goal-directed locomotion.
Improving locomotion performance (e.g., distance travelled).
Implementing the proposed algorithm on ModRED hardware.
Results I
Results II
CMANTIC Lab University of Nebraska at Omaha