We present a distributed control strategy for the aggregation of multiple modular robots into one connected structure optimized for use with 3D modular pivoting cube robots such as the 3D M-Blocks [1]. We use the intensity from a light source as input to a decentralized control algorithm that drives the robots together. We describe the algorithm, give provable guarantees on convergence, and discuss experiments carried out in simulation and with a hardware platform of ten 3D M-Blocks modules. In this paper we contribute provably correct algorithms for the aggregation of generic modular robots; we show how these algorithms can be applied on real hardware by evaluating them on the 3D M-Blocks platform.

Roombots (RB) are self-reconfigurable modular robots designed to explore physical structure change by robotic reconfiguration and adaptive locomotion on structured grid environments or unstructured environments. The primary goal of RB is to create adaptive furniture. In this study, we propose a novel and user-friendly interface to control position and intensity of a mobile desk light using RB modules. In the proposed method, the user interacts with the RB with only hand/arm gestures. The user's arm is tracked with a single Kinect having bird's eye view. We demonstrate the effectiveness of the proposed interface in real hardware setup and discuss contributions of it.

This paper presents the results of a study on the effect of in-series compliance on the locomotion of a simulated 8-DoF Lola-OP™ Modular Snake Robot with added compliant elements. We explore whether there is an optimal stiffness for gait, terrain type, or several gaits and several terrains (i.e. a good “general-purpose” stiffness). Compliance was simulated using ball joints with eight different levels of stiffness. Two snake locomotion gaits (rolling and sidewinding) were tested over flat ground and three different types of rough terrains. We performed grid search and Particle Swarm Optimization to identify the locomotion parameters leading to fast locomotion and analyzed the best candidates in terms of locomotion speed and energy efficiency (cost of transport). Contrary to our expectations, we did not observe a clear trend that would favor the use of compliant elements over rigid structures. For sidewinding, compliant and stiff elements lead to comparable performances. For rolling gait, the general rule seems to be “the stiffer, the better”.

Roombots (RB) are self-reconfigurable modular robots designed to study robotic reconfiguration on a structured grid and adaptive locomotion off grid. One of the main goals of this platform is to create adaptive furniture inside living spaces such as homes or offices. To ease the control of RB modules in these environments, we propose a novel and more natural way of interaction with the RB modules on a RB grid, called the Natural Roombots User Interface. In our method, the user commands the RB modules using pointing gestures. The user's body is tracked using multiple Kinects. The user is also given real-time visual feedback of their physical actions and the state of the system via LED illumination electronics installed on both RB modules and the grid. We demonstrate how our interface can be used to efficiently control RB modules on simple point-to-point grid locomotion and conclude by discussing future extensions.

Manipulation and transport of objects using mobile robotic platforms is a well studied field with several successful approaches. The main difficulty while using such platforms is the lack of adaptation capabilities to changes in the environment and the restriction to flat working areas. In this paper, we present a novel manipulation and transport framework using the self-reconfigurable modular robots Roombots to collaboratively carry arbitrarily shaped passive elements in a non-regular 3D environment equipped with passive connectors. A hierarchical planner based on the notion of virtual kinematic chain is used to generate collision-free and hardware-friendly paths as well as sequences of collaborative manipulations. To the best of our knowledge, this is the first example of manipulation of fully passive elements in an arbitrary 3D environment using mobile self-reconfigurable robots. The simulated results show that the planner is robust to arbitrary complex environments with randomly distributed connectors. In addition to simulation results, a proof of concept of the manipulation of one passive element with two real Roombots meta-modules is described.

The design of efficient locomotion gaits for robots with many degrees of freedom is challenging and time consuming even if optimization techniques are applied. Control parameters can be found through optimization in two ways: (i) through online optimization where the performance of a robot is measured while trying different control parameters on the actual hardware and (ii) through offline optimization by simulating the robot's behavior with the help of models of the robot and its environment. In this paper, we present a hybrid optimization method that combines the best properties of online and offline optimization to efficiently find locomotion gaits for arbitrary structures. In comparison to pure online optimization, both the number of experiments using robotic hardware as well as the total time required for finding efficient locomotion gaits get highly reduced by running the major part of the optimization process in simulation using a cluster of processors. The presented example shows that even for robots with a low number of degrees of freedom the time required for optimization can be reduced by a factor of 2.5 to 30, at least, depending on how extensive the search for optimized control parameters should be. Time for hardware experiments becomes minimal. More importantly, gaits that can possibly damage the robotic hardware can be filtered before being tried in hardware. Yet in contrast to pure offline optimization, we reach well matched behavior that allows a direct transfer of locomotion gaits from simulation to hardware. This is because through a meta-optimization we adapt not only the locomotion parameters but also the parameters for simulation models of the robot and environment allowing for a good matching of the robot behavior in simulation and hardware. We validate the proposed hybrid optimization method on a structure composed of two Roombots modules with a total number of six degrees of freedom. Roombots are self-reconfigurable modular robots that can form arbitrary structures with many degrees of freedom through an integrated active connection mechanism.

This paper presents the results of a study on the exploitation of compliance in structures made of self-reconfigurable modular robots - Roombots. This research was driven by the following three hypotheses: (1) compliance can improve locomotion performance; (2) different types of compliance will result in diverse locomotion behaviors; (3) control parameters optimized for a medium level of compliance will perform better for other values of compliance than parameters optimized for extremal compliance. Two types of in-series compliant elements were tested, with five different stiffness values for each of them, on a structure made of two Roombots modules. We ran dedicated on-line locomotion parameter optimizations for six different configurations and evaluated their performance for different stiffness values. Hypothesis 1 was confirmed for both types of compliant elements, with a peak of performance for an optimal level of compliance. The variety of locomotion strategies obtained for the different structures confirms hypothesis 2. Hypothesis 3 was only partially confirmed.

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We present the design and evaluation of an iPad application that will be used to operate the modular robots “Roombots”. Roombots are the building blocks for adaptive pieces of furniture. The application allows a user to arrange adaptive furniture within a room. We conducted a user study with 24 participants to evaluate our approach and to freely explore people's interaction. Data suggests that the ability to move with the tablet leads to a better precision of the furniture arrangement. No significant difference has been observed between using the application through a virtual representation of the room in contrast to an augmented reality environment, even if participants mentioned in a post-study questionnaire their preference for the augmented condition. Users described the interface as intuitive and easy to use.

This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots (RB) metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules-two Roombots modules connected serially-that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planner.