Spherical Modular Self-reconfigurable Robots (SMSRs) have been popular in recent years. Their Self-reconfigurable nature allows them to adapt to different en-vironments and tasks, and achieve what a single module could not achieve. To collaborate with each other, relative localization between each module and assembly is crucial. Existing relative localization methods either have low accuracy, which is unsuit-able for short-distance collaborations, or are designed for fixed-shape robots, whose visual features remain static over time. This paper proposes the first visual relative localization method for SMSRs. We first detect and identify individual modules of SMSRs, and adopt visual tracking to improve the detection and identification robustness. Using an optimization-based method, tracking result is then fused with odometry to estimate the relative pose between assemblies. To deal with the non-convexity of the optimization problem, we adopt semi-definite relaxation to transform it into a convex form. The proposed method is validated and analysed in real-world experiments. The overall localization performance and the performance under time-varying configuration are evaluated. The result shows that the relative position estimation accuracy reaches 2%, and the orientation estimation accuracy reaches 6.64°, and that our method surpasses the state-of-the-art methods.

Energy sharing among modular robots enables sustainable operation of the system by maintaining energy balance among the modules. In this paper, we propose a novel energy sharing mechanism for FreeSN, a modular self-reconfigurable robot consisting of node and strut modules. Utilizing the feature that our modules are connected in a face-to-face manner, our method successfully establishes an energy sharing channel at almost any point on a sphere by placing transmission intermediaries at the interfacing face between modules, which is facilitated by the combination of brush contact and shell decomposition. Such mechanism also allows the utilization of the node module’s inner space for extra energy storage. A prototype of this energy sharing system has been implemented on FreeSN and rigorously tested. Our findings indicate that energy sharing is reliably established between modules; for strut modules positioned randomly on a node module’s surface, the probability of forming a valid connection is 56.6%. With orientation adjustment, a connection is achievable at nearly any position on the sphere, barring a few exceptional points. As a result, the operational endurance of the strut modules, which provide all the driving forces in the system, is markedly enhanced. This technique also holds potential for broader application across other freeform robotic platforms that incorporate conductive spherical surfaces for sliding connections.

Modular self-reconfigurable robotic systems are potentially more robust and adaptive than conventional systems. This article proposes a novel freeform and truss-structured modular self-reconfigurable robot called FreeSN, containing node and strut modules. A node module contains a low-carbon steel spherical shell. A strut module contains two magnetic-based freeform connectors, which can connect to any position of the node module and provide spherical motions. Accurate configuration identification is essential for the automation of modular robot systems. This article presents a novel configuration identification system for FreeSN, including connection point magnetic localization, module identification, module orientation fusion, and system configuration fusion. A magnetic sensor array is integrated into the node module. A graph convolutional network-based magnetic localization algorithm is proposed, which can efficiently locate a variable number of magnet arrays under ferromagnetic material distortion. The module relative orientation is then estimated by fusing the magnetic localization result with the inertia moment unit and wheel odometry. Finally, the system configuration can be estimated, including the connection topology graph and the poses of modules. The configuration identification system is validated by a series of accuracy evaluation experiments and two library-based automation demonstrations based on closed-loop control.

Energy sharing in modular self-reconfigurable robots ensures the energy balance of the modules, thus allowing the system to work sustainably. This paper proposes an energy sharing mechanism for a novel modular self-reconfigurable robot that allows free connections among modules, termed as FreeBOT, such that each FreeBOT can share energy with peers through surface contact. Corresponding energy sharing rules are proposed to achieve an energy sharing network structure without invalid components. As alternative choices, several types of networks subjected to the above requirements are provided, which also maximize the number of FreeBOTs joining to share energy. We implement and test the prototype of the energy sharing mechanism on FreeBOT. The experimental results show that the mechanism can effectively achieve energy sharing among FreeBOTs.

This paper proposes a novel freeform strut-node structured modular self-reconfigurable robot (MSRR) called FreeSN, consisting of strut and node modules. A node module is mainly a low-carbon steel spherical shell. A strut module contains two freeform connectors, which provide strong magnetic connections and flexible spherical motions. The FreeSN system shares the benefits of freeform connection and strut-node structures. The freeform connection brings good adaptability to the environment. The triangle substructures inside the system configuration significantly improve the structural stability. The parallel execution of module motions can superpose the module capabilities and makes the system more scalable. The modules can combine these robot features by selecting the system configuration and better fit different circumstances and tasks. Four demonstrations, including assembly, obstacle crossing, transportation, and object manipulation, are designed to show the capabilities of the FreeSN system in different aspects. The results show the great performance and versatility of this MSRR system.

Modular self-reconfigurable robotic (MSRR) systems are potentially more robust and more adaptive than conventional systems. Following our previous work where we proposed a freeform MSRR module called FreeBOT, this paper presents a novel configuration detection system for FreeBOT using a magnetic sensor array. A FreeBOT module can be connected by up to 11 modules, and the proposed configuration detection system can locate a variable number of connection points accurately in real-time. By equipping FreeBOT with 24 magnetic sensors, the magnetic field density produced by magnets and steel spherical shells can be monitored. The connectable area is split into 199 non-uniform regions, including 84 uniform regions. Using a Graph Convolutional Network (GCN) based algorithm, the connection points can be located accurately under ferromagnetic environments. The system can locate a variable number of connection points for such a region division with only single connection point training data. Finally, the localization algorithm can run faster than 40 Hz on FreeBOT. With the real-time configuration detection system, the FreeBOT system has the potential to reconfigure automatically and accurately.