Self-Reconfiguring Modular Robotics
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Modular self-reconfiguring robotic systems are autonomous kinematic machines with variable morphology. Beyond conventional actuation, sensing and control typically found in fixed-morphology robots, self-reconfiguring robots are also able to deliberately change their own shape by rearranging the connectivity of their parts, in order to adapt to new circumstances, perform new tasks, or recover from damage.
Structure and control
Modular robots are usually composed of multiple building blocks of a relatively small repertoire, with uniform docking interfaces that allow transfer of mechanical forces and moments, electrical power and communication throughout the robot.
The modular building blocks usually consist of some primary structural actuated unit, and potentially additional specialized units such as grippers, feet, wheels, cameras, payload and energy storage and generation.
A taxonomy of architectures
Modular self-reconfiguring robotic systems can be generally classified into several architectural groups by the geometric arrangement of their unit (lattice vs. chain). Several systems exhibit hybrid properties.
- Lattice architectures have units that are arranged and connected in some regular, space-filling three-dimensional pattern, such as a cubical or hexagonal grid. Control and motion are executed in parallel. Lattice architectures usually offer simpler computational representation that can be more easily scaled to complex systems.
- Chain/tree architectures have units that are connected together in a string or tree topology. This chain or tree can fold up to become space filling, but underlying architecture is serial. Chain architectures can reach any point in space, and are therefore more versatile but more computationally difficult to represent and analyze.
Modular robotic systems can also be classified according to the way by which units are reconfigured (moved) into place.
- Deterministic reconfiguration relies on units moving or being directly manipulated into their target location during reconfiguration. The exact location of each unit is known at all times. Reconfiguration times can be guaranteed, but sophisticated feedback control is necessary to assure precise manipulation. Macro-scale systems are usually deterministic.
- Stochastic reconfiguration relies on units moving around using statistical processes (like Brownian motion). The exact location of each unit only known when it is connected to the main structure, but it may take unknown paths to move between locations. Reconfiguration times can be guaranteed only statistically. Stochastic architectures are more favorable at micro scales.
Other modular robotic systems exist which are not self-reconfigurable, and thus do not formally belong to this family of robots though they may have similar appearance. For example, self-assembling systems may be composed of multiple modules but cannot dynamically control their target shape. Similarly, tensegrity robotics may be composed of multiple interchangeable modules but cannot self-reconfigure.
Motivation and inspiration
There are two key motivations for designing modular self reconfiguring robotic systems.
- Functional advantage: Self reconfiguring robotic systems are potentially more robust and more adaptive than conventional systems. The reconfiguration ability allows a robot or a group of robots to disassemble and reassemble machines to form new morphologies that are better suitable for new tasks, such as changing from a legged robot to a snake robot and then to a rolling robot. Since robot parts are interchangeable (within a robot and between different robots), machines can also replace faulty parts autonomously, leading to self-repair.
- Economic advantage: Self reconfiguring robotic systems can potentially lower overall robot cost by making a range of complex machines out of a single (or relatively few) types of mass-produced modules.
Both these advantages have not yet been fully realized. A modular robot is likely to be inferior in performance to any single custom robot tailored for a specific task. However, the advantage of modular robotics is only apparent when considering multiple tasks that would normally require a set of different robot.
The added degrees of freedom make modular robots more versatile in their potential capabilities, but also incur a performance tradeoff and increased mechanical and computational complexities.
The quest for self-reconfiguring robotic structures is to some extent inspired by envisioned applications such as long-term space missions, that require long-term self-sustaining robotic ecology that can handle unforeseen situations and may require self repair. A second source of inspiration are biological systems that are self-constructed out of a relatively small repertoire of lower-level building blocks (cells or amino acids, depending on scale of interest). This architecture underlies biological systems’ ability to physically adapt, grow, heal, and even self replicate – capabilities that would be desirable in many engineered systems.
Given these advantages, where would a modular self-reconfigurable system be used? While the system has the promise of being capable of doing a wide variety of things, finding the “killer application” has been somewhat elusive. Here are several examples:
One application that highlights the advantages of self-reconfigurable systems is long-term space missions. These require long-term self-sustaining robotic ecology that can handle unforeseen situations and may require self repair. Self-reconfigurable systems have the ability to handle tasks that are not known a priori especially compared to fixed configuration systems. In addition, space missions are highly volume and mass constrained. Sending a robot system that can reconfigure to achieve many tasks is better than sending many robots that each can do one task. ___MITTU THOMAS______
Another example of an application has been coined “telepario” by CMU professors Todd Mowry and Seth Goldstien. What the researchers propose to make are moving, physical, three-dimensional replicas of people or objects, so lifelike that human senses would accept them as real. This would eliminate the need for cumbersome virtual reality gear and overcome the viewing angle limitations of modern 3D approaches. The replicas would mimic the shape and appearance of a person or object being imaged in real time, and as the originals moved, so would their replicas. One aspect of this application is that the main development thrust is geometric representation rather than applying forces to the environment as in a typical robotic manipulation task.
Bucket of stuff
A third long term vision for these systems has been called “bucket o stuff”. In this vision, consumers of the future have a container of self-reconfigurable modules say in their garage, basement, or attic. When the need arises, the consumer calls forth the robots to achieve a task such as “clean the gutters” or “change the oil in the car” and the robot assumes the shape needed and does the task. One source of inspiration for the development of these systems comes from the application. A second source is biological systems that are self-constructed out of a relatively small repertoire of lower-level building blocks (cells or amino acids, depending on scale of interest). This architecture underlies biological systems’ ability to physically adapt, grow, heal, and even self replicate – capabilities that would be desirable in many engineered systems.
History and state of the art
The roots of the concept of modular self-reconfigurable robots can be traced back to the “quick change” end effector and automatic too changers in computer numerical controlled machining centers in the 1970’s. Here, special modules each with a common connection mechanism could be automatically swapped out on the end of a robotic arm. However, taking the basic concept of the common connection mechanism and applying it to the whole robot was introduced by Toshio Fukuda with the CEBOT (short for cellular robot) in the late 1980’s.
The early 1990’s saw further development from Greg Chirikjian, Mark Yim, Joseph Michael, and Satoshi Murata. Chirikjian, Michael, and Murata developed lattice reconfiguration systems and Yim developed a chain based system. While these researchers started with from a mechanical engineering emphasis, designing and building modules then developing code to program them, the work of Daniela Rus and Wei-min Shen developed hardware but had a greater impact on the programming aspects. They started a trend towards provable or verfiable distributed algorithms for the control of large numbers of modules.
One of the more interesting hardware platforms recently has been the MTRAN II and III systems developed by Satoshi Murata et. al. This system is a hybrid chain and lattice system. It has the advantage of being able to achieve tasks more easily like chain systems, yet reconfigure like a lattice system.
More recently new efforts in stochastic self-assembly have been persued by Hod Lipson and Eric Klavins. A large effort at CMU headed by Seth Goldstien and Todd Mowry has started looking at issues in developing millions of modules.
Many tasks have been shown to be achievable, especially with chain reconfiguration modules. This demonstrates the versatility of these systems however, the other two advantages, robustness and low cost have not been demonstrated. In general the prototype systems developed in the labs have been fragile and expensive as would be expected during any initial development.
There is a growing number of research groups actively involved in modular robotics research. To date, about 30 systems have been designed and constructed, some of which are shown below.
Some current systems
PolyBot G3 (2002)
A chain self-reconfiguration system. Each module is about 50mm on a side, and has 1 rotational DOF. It is part of the PolyBot modular robot family that has demonstrated many modes of locomotion including walking: biped, 14 legged, slinky-like, snake-like: concertina in a gopher hole, inchworm gaits, rectilinear undulation and sidewinding gaits, rolling like a tread at up to 1.4m/s, riding a tricycle, climbing: stairs, poles pipes, ramps etc. More information can be found at the polybot webpage at PARC.
High spatial resolution for arbitrary three-dimensional shape formation with modular robots can be accomplished using lattice system with large quantities of very small, prospectively microscopic modules. At small scales, and with large quantities of modules, deterministic control over reconfiguration of individual modules will become unfeasible, while stochastic mechanisms will naturally prevail. Microscopic size of modules will make the use of electromagnetic actuation and interconnection prohibitive, as well, as the use of on-board power storage.
Three large scale prototypes were built in attempt to demonstrate dynamicaly programmable three-dimensional stochastic reconfiguration in a neutral-buoyancy environment. The first prototype used electromagnets for module reconfiguration and interconnection. The modules were 100 mm cubes and weighed 0.81 kg. The second prototype used stochastic fluidic reconfiguration and interconnection mechanism. It's 130 mm cubic modules weighed 1.78 kg each and made reconfiguration experiments excessively slow. Current, third implementation inherits the fluidic reconfiguration principle. The lattice grid size is 80 mm, and the reconfiguration experiments are under way. More information can be found at the CCSL Stochastic Modular Robotics webpage.
This chain self-reconfiguring system was built to physically demonstrate artificial kinematic self-reproduction. Each module is a 0.65 kg cube with 100 mm long edges and one rotational degree of freedom. The axis of rotation is aligned with the cube's longest diagonal. Physical self-reproduction of a three- and a four-module robots was demonstrated. It was also shown that, disregarding the gravity constraints, an infinite number of self-reproducing chain meta-structures can be built from Molecubes. More information can be found at the CCSL Self-Replication webpage.
The Programmable Parts (2005)
The programmable parts are stirred randomly on an air-hockey table by randomly actuated air jets. When they collide and stick, they can communicate and decide whether to stay stuck, or if and when to detach. Local interaction rules can be devised and optimized to guide the robots to make any desired global shape. More information can be found at the programmable parts web page.
The SuperBot modules fall into the chain/tree architecture. The modules have three degrees of freedom each. The design is based on two previous systems: Conro (by the same research group) and MTRAN (by Murata et al.). Each module can connect to another module through one of its six dock connectors. They can communicate and share power through their dock connectors. Several locomotion gaits have been developed for different arrangements of modules. For high-level communication the modules use hormone-based control, a distributed, scalable protocol that does not require the modules to have unique ID's.
The Miche system is a modular lattice system capable of arbitrary shape formation. Each module is an autonomous robot module capable of connecting to and communicating with its immediate neighbors. When assembled into a structure, the modules form a system that can be virtually sculpted using a computer interface and a distributed process. The group of modules collectively decide who is on the final shape and who is not using algorithms that minimize the information transmission and storage. Finally, the modules not in the structure let go and fall off under the control of an external force, in this case gravity. More details at Miche (Rus et al).
Starfish robot (2006)
A four-legged robot (although reconfigurable to any number of limbs) that can sense damage to its body and figure out how to adjust and keep going. While most robots operate using a computer model they have been programmed with, this one develops its own model by analyzing how its parts respond to commands to move. The robot has tilt sensors and angle sensors in each of its joints and uses the readings from these devices to create a computer model of its own structure and movement. When the sensors indicate a change, it can then alter the model to compensate. Reported in Science, 17 Nov 2006. See AP Wire. Image at Starfish Robot.
<Add one image of your system here. Sort by date (earlier first), add reference.>
- The robot with most active modules has 56 units <polybot centipede, PARC>
- The smallest actuated modular unit has a size of <add>mm <add refs>
- The largest actuated modular unit (by volume) has the size of 8 m^3 <(GHFC)giant helium filled catoms, CMU>
- The strongest actuation modules are able to lift 5 identical horizontally cantilevered units. <PolyBot g1v5, PARC>
- The fastest modular robot can move at 23 unit-sizes/second. <CKbot, dynamic rolling, ISER'06>
- The largest simulated system contained many 100,000's of units. <DPRSim www.pittsburgh.intel-research.net/dprweb/>
Challenges and opportunities
Since the early demonstrations of early modular self-reconfiguring systems, the size, robustness and performance has been continuously improving. In parallel, planning and control algorithms have been progressing to handle thousands on units. There are, however, several key steps that are necessary for these systems to realize their promise of adaptability, robustness and low cost. These steps can be broken down into challenges in the hardware design, in planning and control algorithms and in application. These challenges are often intertwined.
Hardware design challenges
The extent to which the promise of self-reconfiguring robotic systems can be realized depends critically on the numbers of modules in the system. To date, only systems with up to about 50 units have been demonstrated, with this number stagnating over almost a decade. There are a number of fundamental limiting factors that govern this number:
- Limits on strength, precision, and field robustness (both mechanical and electrical) of bonding/docking interfaces between modules
- Limits on motor power, motion precision and energetic efficiency of units, (i.e. specific power, specific torque)
- Hardware/software design. Hardware that is designed to make the software problem easier. Self-reconfiguring systems have more tightly coupled hardware and software than any other existing system.
Planning and control challenges
Though algorithms have been developed for handling thousands of units in ideal conditions, challenges to scalability remain both in low-level control and high-level planning to overcome realistic constraints:
- Algorithms for parallel-motion for large scale manipulation and locomotion
- Algorithms for robustly handling a variety of failure modes, from misalignments, dead-units (not responding, not releasing) to units that behave erratically.
- Algorithms that determine the optimal configuration for a given task
- Algorithms for optimal (time, energy) reconfiguration plan
- Efficient and scalable (asynchronous) communication among multiple units
Though the advantages of Modular self-reconfiguring robotic systems is largely recognized, it has been difficult to identify specific application domains where benefits can be demonstrated in the short term. Some suggested applications are
- Space exploration andSpace colonization applications, e.g. Lunar colonization
- Construction of large architectural systems
- Deep sea exploration/mining
- Search and rescue in unstructured environments
- Rapid construction of arbitrary tools under space/weight constraints
Several robotic fields have identified ‘’Grand Challenges’’ that act as a catalyst for development and serve as a short-term goal in absence of immediate ‘’killer apps’’. The Grand Challenge is not in itself a research agenda or milestone, but a means to stimulate and evaluate coordinated progress across multiple technical frontiers. Several Grand Challenges have been proposed for the modular self-reconfiguring robotics field:
- Demonstration of a system with >1000 units. Physical demonstration of such a system will inevitably require rethinking key hardware and algorithmic issues, as well as handling noise and error.
- Robosphere. A self-sustaining robotic ecology, isolated for a long period of time (1 year) that needs to sustain operation and accomplish unforeseen tasks withut any human presence.
- Self replication A system with many units capable of self replication by collecting scattered building blocks will require solving many of the hardware and algorithmic challenges.
- Ultimate Construction A system capable of making objects out of the components of, say, a wall.
- Biofilter analogy If the system is ever made small enough to be injected into a mammal, one task may be to monitor molecules in the blood stream and allow some to pass and others not to, somewhat like the Blood-brain barrier. As a challenge, an analogy may be made where system must be able to:
- be inserted into a hole one module’s diameter.
- travel some specified distance in a channel that is say roughly 40 x 40 module diameters in area.
- form a barrier fully conforming to the channel (whose shape is non-regular, and unknown beforehand).
- allow some objects to pass and others not to (not based on size).
- Since sensing is not the emphasis of this work, the actual detection of the passable objects should be made trivial.