Roderich Gross

Autonomous Self-Assembly in Swarm-Bots

In this paper, we discuss the self-assembling capabilities of the swarm-bot, a distributed robotics concept that lies at the intersection between collective and self-reconfigurable robotics. A swarm-bot is comprised of autonomous mobile robots called s-bots. S-bots can either act independently or self-assemble into a swarm-bot by using their grippers. We report on experiments in which we study the process that leads a group of s-bots to self-assemble. In particular, we present results of experiments in which we vary the number of s-bots (up to 16 physical robots), their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly

Self-Assembly at the Macroscopic Scale

In this paper, we review half a century of research on the design of systems displaying (physical) self-assembly of macroscopic components. We report on the experience gained in the design of 21 such systems, exhibiting components ranging from passive mechanical parts to mobile robots. We present a taxonomy of the systems and discuss design principles and functions. Finally, we summarize the main achievements and indicate potential directions for future research.

Towards group transport by swarms of robots

We examine the ability of a swarm robotic system to transport cooperatively objects of different shapes and sizes. We simulate a group of autonomous mobile robots that can physically connect to each other and to the transported object. Controllers – artificial neural networks – are synthesised by an evolutionary algorithm. They are trained to let the robots self-assemble, that is, organise into collective physical structures and transport the object towards a target location. We quantify the performance and the behaviour of the group. We show that the group can cope fairly well with objects of different geometries as well as with sudden changes in the target location. Moreover, we show that larger groups, which are made of up to 16 robots, make possible the transport of heavier objects. Finally, we discuss the limitations of the system in terms of task complexity, scalability and fault tolerance and point out potential directions for future research.

Object transport by modular robots that self-assemble

We present a first attempt to accomplish a simple object manipulation task using the self-reconfigurable robotic system swarm-bot. The number of modular entities involved, their global shape or size and their internal structure are not pre-determined, but result from a self-organized process in which the modules autonomously grasp each other and/or an object. The modules are autonomous in perception, control, action, and power. We present quantitative results, obtained with six physical modules, that confirm the utility of self-assembling robots in a concrete task

Transport of an object by six pre-attached robots interacting via physical links

This paper addresses the cooperative transport of a heavy object by a group of mobile robots. We present a system in which group members lacking knowledge about the position of the transport target exploit physical interactions with other members of the group that have such knowledge. This is the first such system to achieve a performance superior to that of a passive caster. The system is fully decentralized and the information flow between the robots is limited to physical interactions. The robots have no knowledge about their relative positions. A comprehensive experimental study with up to six physical robots confirms the effectiveness, reliability, and robustness of the system. Finally, the system is examined in rough terrain conditions

A strategy for transporting tall objects with a swarm of miniature mobile robots

This paper proposes a strategy for transporting a tall, and potentially heavy, object to a goal using a large number of miniature mobile robots. The robots move the object by pushing it. The direction in which the object moves is controlled by the way in which the robots distribute themselves around its perimeter - if the robots dynamically reallocate themselves around the section of the objects perimeter that occludes their view of the goal, the object will eventually be transported to the goal. This strategy is fully distributed, and makes no use of communication between the robots. A controller based on this strategy was implemented on a swarm of 12 physical e-puck robots, and a systematic experiment with 30 randomized trials was performed. The object was successfully transported to the goal in all the trials. On average, the path traced by the object was about 8.4% longer than the shortest possible path.

Performance benefits of self-assembly in a swarm-bot

Mobile robots are said to be capable of self- assembly when they can autonomously form physical connections with each other. Despite the recent proliferation of self- assembling systems, little work has been done on using self- assembly to add functional value to a robotic system, and even less on quantifying the contribution of self-assembly to system performance. In this study we demonstrate and quantify the performance benefits of i) acting as a physically larger self-assembled entity, ii) using self-assembly adaptively and iii) making the robots morphologically aware (the self-assembled robots leverage their new connected morphology in a task specific way). In our experiments, two real robots must navigate to a target over a-priori unknown terrain. In some cases the terrain can only be overcome by a self-assembled connected entity. In other cases, the robots can reach the target faster by navigating individually.

HiGen: A High-Speed Genderless Mechanical Connection Mechanism with Single-Sided Disconnect for Self-Reconfigurable Modular Robots

The practical effectiveness of modular robotic systems depends heavily on the connection mechanisms used to join their separate entities, particularly for those systems capable of self-reconfiguration. This work presents HiGen, a high-speed genderless mechanical connection mechanism for the docking of robotic modules. HiGen connectors can join with one another in a manner that allows either side to disconnect in the event of failure. During connection electrical contacts are mated, supporting the concurrent use of local and global communication protocols, as well as power sharing techniques. Rapid actuation of the mechanism allows connections to be made and broken at a speed that is, to our knowledge, an order of magnitude faster than existing mechanical genderless approaches that feature single-sided disconnect, benefiting the reconfiguration time of modular robots. The HiGen connector is intended for future work in modular robotics, but could also see use in other areas of robotics for tool and payload attachment.

OpenSwarm: An event-driven embedded operating system for miniature robots

This paper presents OpenSwarm, a lightweight easy-to-use open-source operating system. To our knowledge, it is the first operating system designed for and deployed on miniature robots. OpenSwarm operates directly on a robots microcontroller. It has a memory footprint of 1 kB RAM and 12 kB ROM. OpenSwarm enables a robot to execute multiple processes simultaneously. It provides a hybrid kernel that natively supports preemptive and cooperative scheduling, making it suitable for both computationally intensive and swiftly responsive robotics tasks. OpenSwarm provides hardware abstractions to rapidly develop and test platform-independent code. We show how OpenSwarm can be used to solve a canonical problem in swarm robotics-clustering a collection of dispersed objects. We report experiments, conducted with five e-puck mobile robots, that show that an OpenSwarm implementation performs as good as a hardware-near implementation. The primary goal of OpenSwarm is to make robots with severely constrained hardware more accessible, which may help such systems to be deployed in real-world applications.

A coevolutionary approach to learn animal behavior through controlled interaction

This paper proposes a method that allows a machine to infer the behavior of an animal in a fully automatic way. In principle, the machine does not need any prior information about the behavior. It is able to modify the environmental conditions and observe the animal; therefore it can learn about the animal through controlled interaction. Using a competitive coevolutionary approach, the machine concurrently evolves animats, that is, models to approximate the animal, as well as classifiers to discriminate between animal and animat. We present a proof-of-concept study conducted in computer simulation that shows the feasibility of the approach. Moreover, we show that the machine learns significantly better through interaction with the animal than through passive observation. We discuss the merits and limitations of the approach and outline potential future directions.