SCIENTIA SINICA Informationis, Volume 48, Issue 2: 143-176(2018) https://doi.org/10.1360/N112017-00154

## Path planning for self-reconfigurable modular robots: a survey

• AcceptedSep 25, 2017
• PublishedJan 24, 2018
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### Abstract

Self-reconfigurable modular robots (SRMRs) are a special type of robots that can change their shapes and functions according to different tasks and environments. Such a robot is usually constructed using connected modules, each of which can encapsulate a simple function independently and also communicate with each other. Complex tasks can be completed by those connected modules collaboratively. In recent years, SRMRs have attracted considerable attention from both the academia and industry because of their versatility and flexibility. The path planning problem for the transformation of an SRMR is an important but not a well-solved problem, which can be considered as finding an optimal path in the configuration space where every point represents a feasible configuration of the SRMR. To provide a systematic overview of this research, we review the existing approaches considering five different aspects of SRMRs, including the type of motion on a single module, hardware for different motions, connectivity between modules, representation of a configuration space, and path planning algorithms. Aiming at motivating more research into SRMRs, the problems in existing approaches are analyzed and challenges in future work are summarized at the end of this paper.

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• Figure 1

(Color online) The M-TRAN III modular robot in national institute of advanced industrial science and technology (AIST), Japan [2]. (a) A single M-TRAN III module; (b) the reconfiguration from a quadruped robot to snake robot. Both robots are made up of the same number of modules. Photos are courtesy of professor Haruhisa Kurokawa

• Figure 2

(Color online) Double-unit modules. (a) M-TRAN III module [2] is an edge-hinged double-unit module (Subsection 2.3). It consists of two (i.e., female and male) semi-cylindrical cubes. Two cubes are connected by a linker. Each cube can independently rotate with respect to the linker and the rotation angle is ranged from $-90^{\circ}$ to $90^{\circ}$; (b) the CellRobot in KEYi technology incorporated consists of two symmetrically hemispherical units. By rotating around the center of the module, each unit can relatively move with respect to each other (Subsection 2.2)

• Figure 3

(Color online) Single-unit modules. (a) M-Blocks [4] modules locomote by pivoting about edges, and the movements are driven by a torque generated by rapidly decelerating an internal flywheel. The permanent magnets embedded in edges are free to rotate to allow 2–4 modules to form structural magnetic bonds. (b) crystalline [5] modules move by expanding and contracting and the length of the side when expanded is two times that of contraction. Photos are courtesy of professor Daniela Rus and doctor John Romanishin

• Figure 4

(Color online) Central-point-hinged type I. (a) The rotate axis is the line connecting the centers of two faces opposite with each other; (b) Atron [10] proposed by University of Southern Denmark modules are in this type. Photos are courtesy of professor Henrik Hautop Lund

• Figure 5

(Color online) Central-point-hinged type II. (a) The rotation axis is the diagonal of bounding box. (b) Molecube modules [9]proposed by Cornell University cannot move relative to each other. (b) Roombots [10,11]modules proposed by Swiss Federal Institute of Technology in Lausanne (EPFL) are able to move relative to each other. Photos are courtesy of BioRob, EPFL

• Figure 6

(Color online) Possible grid-reconfigurations with two Roombots because of the different relationship between rotate axes [10]. (a) Skew: 5 options; (b) parallel, 4 options; (c) orthogonal, 4 options

• Figure 7

(Color online) The edge-hinged type. (a) The sub-modules of SuperBot [6]can rotate around the link (DoF 1 and 2) and with each other (DoF 3); (b) the sub-modules of iMobot [3]can rotate around the link (DoF 2 and 3), and the modules can rotate with each other (DoF 1 and 4). Photos are courtesy of professor Wei-Min Shen

• Figure 8

(Color online) (a) SMORES modules and (b) their motion modes [14]. Each module has two wheels (DoF 1 and 2), and the connected face is a rotatable disk. Once the connection of two modules is made, there are DoF 3 and 4. Photos are courtesy of professor Mark Yim

• Figure 9

(Color online) Pivoting modules. (a) The modules move by pivoting about edges. (b) Modules in this type cannot control the angle of rotation accurately because of lacking in connector. They cannot stop at the position showed in red cross and the only way to stop pivoting is other modules block the way, as show in green circle

• Figure 10

(Color online) A sketch map of reconfiguration by expanding and contracting. Modules 1, 2, 3, 4 of (a) were contracted, and the configuration is transformed into (b). Then modules 3 and 4 were expanded, transformed into (c). Then modules 1 and 2 were expanded, transformed into (d). Because the modules are all the same, the reconfiguration between (a) to (d) can be regarded as that module 4 moved to the position of module 5

• Figure 11

(Color online) The 3D expand and contract operation of Telecubes [16]. (a) CAD model of Telecubes module; (b) the physical prototype of Telecubes module expanded; (c) the physical prototype of Telecubes module contracted. Photos are courtesy of professor Mark Yim

• Figure 12

(Color online) The (a) four-legged walkers and (b) snake forms assembled by four M-TRAN modules [2]. Each module should bend or rotate by the rules to walk or crawl. (c) The automatic metamorphosis between configurations. Photos are courtesy of professor Haruhisa Kurokawa

• Figure 13

(Color online) (a) The rolling Roombots modules can be used to moving furniture; (b) a Roombots [10,11]chair. The rolling bottom modules enable the chair to move. Photos are courtesy of BioRob, EPFL

• Figure 14

(Color online) The chain-typed self-reconfiguration. (a) Modules 9–16 are regarded as a chain, and it can be fold through the cooperation among modules; (b) module 16 moves to connect module 5, then module 11 disconnects module 12; (c) modules 12–16 are regarded as a new chain

• Figure 15

(Color online) The one-by-one-type self-reconfiguration. Only one module moves in each step

• Figure 16

(Color online) Two configurations composed by two M-TRAN modules. A M-TRAN module consists of female cube (white) and male cube (black). (+,$-$) represents that the hook in male cube is inserted into the slit in female cube of another module. The male and female cube without +,$-$ is free to rotate. Configurations (a) and (b) are different because their connection methods are different

• Figure 17

(Color online) (a) The configuration 1 is composed of three M-TRAN modules; (b) the configuration 2 is composed of three M-TRAN modules; (c) graph representation for the configuration. Each module is regarded as a node. There is an edge between two nodes if two modules are connected; (d) weighted digraph representations for two configurations. This method separates configurations 1 and 2 easily

• Figure 18

(Color online) (a) Each face is uniquely identified with an ID; (b) a configuration of three M-TRAN modules; (c) the weighted graph representation of configuration in (b); (d) the matrix representation of configuration in (b). Two columns of the matrix correspond to the two joint faces in (b). Three rows of the matrix correspond to three modules. Two non-zero units in each column correspond to the id of the faces which are connected to other modules. For example, column $C$1 means the face 6 of module 1 and the face 2 of module 2 are connected

• Figure 20

(Color online) Three graphs above are isomorphic

• Figure 21

(Color online) The relationship between number of configurations and number of modules. The horizontal axis is the number of modules, and the vertical axis is the logarithm of the configuration number. The result is approximately a straight line. The expression of the fitting function is $f(n)={\rm~e}^{1.62n-3.7}$, which indicates that the number of configuration increases exponentially with the increase of module number

• Figure 22

The overlap metric. Modules 1, 2, 3 are overlapped in (a), (b) and (a), (c), so both the overlap metrics are 1. However, transformation from (a) to (b) needs only one step while (a) to (c) needs two steps

• Figure 23

(Color online) The principle of expanded-and-extracted modules. Module 1, 2, 3, 4 of (a) were contracted, and the configuration is transformed into (b). Then modules 3 and 4 were expanded, transformed into (c). Then modules 1 and 2 were expanded, transformed into (d). Because the modules are all the same, the reconfiguration between (a) to (d) can be regarded as that module 4 moved to the position of module 5

• Figure 24

(Color online) An example of “melt-grow algorithm. The original shape are reconfigured to a linear form, then reconfigured to the target shape

• Figure 25

(Color online) (a) The sliding module is allowed only sliding movement across other modules surfaces; (b) the sliding module is not allowed rotate around other modules

• Figure 26

(Color online) The seed modules (red) help work module (yellow) move to another surface [62]. (a) Initial state. Seed module 1 is on the target surface while seed module 2 and work module are on the original surface; (b) seed module 1 slides on the target surface to connect the seed module 2; (c) work module slides from seed module 2 to 1; (d) seed module 1protectłinebreak and work module slide together to target surface

• Figure 27

(Color online) The meta-module composed of M-TRAN modules [62]. (a) The expanding state of meta-module, which includes six arms assembled by 12 M-TRAN modules; (b) central blocks; (c) the contracting state of meta-module

• Figure 28

(Color online) An example of self-reconfiguration of 4 M-TRAN modules. Upper-left is original shape and lower-left is the target shape. The difference between them is shown by orange arrow. It needs 9 moves to change only one modules position [61]

• Table 1   Comparison of different actuators used in reconfigurable modular robots
 Type Torque Size Control complexity Cost Brushless DC motor Large Medium Complex High Brush DC motor Medium Large Easy High DC stepping motor Medium Large Moderate Moderate Shape memory alloy Small Small Complex Low
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