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 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.
History and state of the art
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Current systems
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.
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Quantitative Accomplishment
- The robot with most modules has <add> units <add refs>
- The smallest actuated modular unit has a size of <add>mm <add refs>
- The largest actuated modular unit (by volume) has the size of <add>mm <add refs>
- The most powerful modules are able to lift <add> identical horizontally cantilevered units. <add refs>
- The fastest legged modular robot can move at <add> unit-sizes/second. <add refs>
- The largest simulated system contained <add> units. <add refs>
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 30 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 of bonding/docking interfaces between modules.
- Limits on motor power, motion precision and energetic efficiency of units
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
Application challenges
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 mission applications, e.g. Lunar colonization
- Construction of large architectural systems
Grand Challenges
Several robotic fields have identified ‘’Grand Challenges’’ that act as a catalyst for development and 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.