The University of Sheffield Natural Robotics Lab
 Aggregating a swarm of robots without computation
We present a solution to the problem of self-organized aggregation of embodied robots that requires no arithmetic computation. The robots have no memory and are equipped with one binary sensor, which informs them whether or not there is another robot in their line of sight. It is proven that the sensor needs to have a sufficiently long range; otherwise aggregation cannot be guaranteed, irrespective of the controller used. The optimal controller is found by performing a grid search over the space of all possible controllers. With this controller, robots rotate on the spot when they perceive another robot, and move backwards along a circular trajectory otherwise. This controller is proven to always aggregate two simultaneously moving robots in finite time, an upper bound for which is provided. Simulations show that the controller also aggregates at least 1000 robots into a single cluster consistently. Moreover, in 30 experiments with 40 physical e-puck robots, 98.6% of the robots aggregated into one cluster. The results obtained have profound implications for the implementation of multi-robot systems at scales where conventional approaches to sensing and information processing are no longer applicable.

1. Self-Organised Aggregation without Computation
The International Journal of Robotics Research, OnlineFirst doi> | bibtex | pdf
2. Evolving Aggregation Behaviors in Multi-Robot Systems with Binary Sensors
Proc. of the 2012 Int. Symposium on Distributed Autonomous Robotic Systems (DARS 2012), Springer, (2014) 355-367 bibtex | pdf

 Cooperative transport of tall objects (plus feasibility study in a domestic environment)
cover We propose 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 object's 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.

1. A Strategy for Transporting Tall Objects with a Swarm of Miniature Mobile Robots
Proc. of the 2013 IEEE Int. Conf. on Robotics and Automation, ICRA 2013 (2013), 863-869 doi> | bibtex | pdf

 Self-folding modular robots
cover We are currently developing a self-folding modular robotic system. The system can transform from 2-D configurations to 3-D configurations. This is work in progress, jointly undertaken by Ahmed and Chris. The picture shows the first prototype.
 A coevolutionary approach to learn animal behavior through controlled interaction
We propose 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.

1. A Coevolutionary Approach to Learn Animal Behavior Through Controlled Interaction
Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2013 (2013) 223-230 doi> | bibtex | pdf

 Spatial segregation based on the Brazil nut effect
cover We study a simple algorithm inspired by the Brazil nut effect for achieving segregation in a swarm of mobile robots. The algorithm lets each robot mimic a particle of a certain size and broadcast this information locally. The segregation task requires the swarm to self-organize into a spatial arrangement in which the robots are ranked by particle size (e.g., annular structures or stripes). Using a physics-based computer simulation, we study the segregation performance of swarms of 50 mobile robots. We show that the system is very robust to noise on inter-robot perception and communication. Moreover, we investigate a simplified version of the control algorithm, which does not rely on communication. Finally, we show that the mean percentage of errors in rank decreases exponentially as the particles' size ratio increases.

1. Segregation in Swarms of e-puck Robots Based On the Brazil Nut Effect
Proc. of the 11th Int. Conf. on Autonomous Agents and Multiagent Systems, AAMAS 2012 (2012) 163-170 ACM | bibtex | pdf
2. Segregation in swarms of mobile robots based on the Brazil nut effect
Proc. of the 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS 2009 (2009) 4349-4356 doi> | bibtex | pdf

 Artificial life
cover We propose an experimental study where simplistic organisms rise from inanimate matter and evolve solely through physical interactions. These organisms are composed of three types of macroscopic building blocks floating in an agitated medium. The dynamism of the medium allows the blocks to physically bind with and disband from each other. This results in the emergence of organisms and their reproduction. The process is governed solely by the building blocks' local interactions in the absence of any blueprint or central command. We demonstrate the feasibility of our approach by realistic computer simulations and a hardware prototype. Our results suggest that an autonomous evolution of non-biological organisms can be realized in human-designed environments and, potentially, in natural environments as well.

1. Towards an Autonomous Evolution of Non-Biological Physical Organisms
Lecture Notes in Computer Science, 5777:173-180, 2011 doi> | bibtex | pdf

 Self-assembly at the macroscopic scale
cover We have reviewed 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.

1. Self-Assembly at the Macroscopic Scale
Proceedings of the IEEE, 96(9):1490-1508, 2008 doi> | bibtex | pdf

 Simple learning rules to cope with changing environments
We consider an agent that must choose repeatedly among several actions. Each action has a certain probability of giving the agent an energy reward, and costs may be associated with switching between actions. The agent does not know which action has the highest reward probability, and the probabilities change randomly over time. We study two learning rules that have been widely used to model decision-making processes in animals - one deterministic and the other stochastic. Over relatively short periods of time, both rules are successful in enabling agents to exploit their environment. Moreover, under a range of effective learning rates, both rules are equivalent, and can be expressed by a third rule that requires the agent to select the action for which the current run of unsuccessful trials is shortest. However, the performance of both rules is relatively poor over longer periods of time, and under most circumstances no better than the performance an agent could achieve without knowledge of the environment. We propose a simple extension to the original rules that enables agents to learn about and effectively exploit a changing environment for an unlimited period of time.

Simple learning rules to cope with changing environments
Journal of the Royal Society Interface, 5(27):1193-1202, 2008 doi> | bibtex | pdf

 Cooperation in groups of solitary or social individuals (evolution of self-assembly)
We simulate a system of simple, insect-like robots that can move autonomously and grasp objects as well as each other. We use artificial evolution to produce solitary transport and group transport behaviors. We show that robots, even though not aware of each other, can be effective in group transport. Group transport can even be performed by robots that behave as in solitary transport. Still, robots engaged in group transport can benefit from behaving differently from robots engaged in solitary transport. Moreover, we provide evidence that self-assembly can provide adaptive value to individuals that compete in an artificial evolution based on task performance.

1. Evolution of Solitary and Group Transport Behaviors for Autonomous Robots Capable of Self-Assembling
Adaptive Behavior, 16 (5): 285-305, 2008 doi> | bibtex | pdf
2. Evolving a Cooperative Transport Behavior for Two Simple Robots
Lecture Notes in Computer Science, 2936:305-317, 2004 doi> | bibtex | pdf

 Self-assembly with swarm-bot
Self-assembly of 16 mobile autonomous robots We report on experiments in which we study the process that leads a group of robots to self-assemble. In particular, we present results of experiments in which we vary the number of robots (up to 16 physical robots, and up to 100 in simulation), 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.

1. Autonomous Self-Assembly in Swarm-Bots
IEEE Transactions on Robotics, 22(6):1115-1130, 2006 doi> | bibtex | pdf
2. Autonomous Self-assembly in a Swarm-bot
Proc. of the 3rd Int. Symp. on Autonomous Minirobots for Research and Edutainment, Springer (2006) 314-322 doi> | bibtex | pdf

 Collective choices in house-hunting ants
Many decisions involve a trade-off between commitment and flexibility.We show here that the collective decisions ants make over new nest sites are sometimes sufficiently flexible that the ants can change targets even after an emigration has begun. Our findings suggest that, in this context, the ants' procedures are such that they can sometimes avoid 'negative information cascades' which might lock them into a poor choice. The ants are more responsive to belated good news of a higher quality nest than they are when the nest they had initially chosen degraded to become worse than an alternative. Our study confirms, in a new way, that ant colonies can be very powerful 'search engines'.

1. Moving targets: collective decisions and flexible choices in house-hunting ants
Swarm Intelligence, 1(2):81-94, 2007 doi> | bibtex | pdf

 Division of labor in self-organized groups
In social insect colonies, many tasks are performed by higher-order group or team entities, whose task solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher-order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are ``identical'' in hardware and control. They make little use of memory and take actions purely on the basis of local information. Our study advances the current state of the art in swarm robotics with regard to the number of real-world robots engaging in teamwork (up to 12 robots in the most challenging experiment). To the best of our knowledge, in this paper we present the first self-organised system of robots that displays a dynamical hierarchy of teamwork (with cooperation also occurring among higher-order entities). Our study shows that teamwork requires neither individual recognition nor differences between individuals. This result might also contribute to the ongoing debate on the role of these characteristics in the division of labour in social insects.

1.Teamwork in Self-Organized Robot Colonies
IEEE Transactions on Evolutionary Computation, 13(4):695-711, 2009 doi> | bibtex | pdf
2. Division of Labour in Self-Organised Groups
Lecture Notes in Artificial Intelligence, 5040:426-436, 2008 doi> | bibtex | pdf
3. Group Transport Along a Robot Chain in a Self-Organised Robot Colony
Proc. of the 9th Int. Conf. on Intelligent Autonomous Systems, IOS Press (2006) 433-442 © IOS | bibtex | pdf

 Cooperative transport of objects of different shapes and sizes
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.

1. Towards group transport by swarms of robots
Int. J. Bio-Inspired Computation, , 1(1-2):1-13, 2009 doi> | bibtex | pdf
2. Cooperative Transport of Objects of Different Shapes and Sizes
Lecture Notes in Computer Science, 3172:106-117, 2004 doi> | bibtex | pdf

 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.

1. Object Transport by Modular Robots that Self-assemble
Proc. of the 2006 IEEE Int. Conf. on Robotics and Automation, IEEE (2006) 2558-2564 doi> | bibtex | pdf
2. Cooperation through self-assembly in multi-robot systems
ACM Transactions on Autonomous and Adaptive Systems, 1(2):115-150, 2006 doi> | bibtex | pdf

 Self-assembly with a super-mechano Colony
We show that a control algorithm for autonomous self-assembly can be ported from a source multi-robot platform (i.e., the swarm-bot system) to a different target multi-robot platform (i.e., a super-mechano colony system). Although there are substantial differences between the two robotic platforms, it is possible to qualitatively reproduce the functionality of the source platform on the target platform. Therefore, the transfer does neither require modifications in the hardware nor an extensive redesign of the control. The results of a set of experiments demonstrate that a controller that was developed for the source platform lets robots of the target platform self-assemble with high reliability.

Self-assembly of Mobile Robots - From Swarm-bot to Super-mechano Colony
Proc. of the 9th Int. Conf. on Intelligent Autonomous Systems, IOS Press (2006) 487-496 © IOS | bibtex | pdf

 Evolving self-organizing behaviors
In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution. Specifically, we study aggregation and coordinated motion of the swarm-bot using a physics-based simulation of the system. Experiments, using a simplified simulation model of the s-bots, show that evolution can discover simple but effective controllers for both the aggregation and the coordinated motion of the swarm-bot. Analysis of the evolved controllers shows that they have properties of scalability, that is, they continue to be effective for larger group sizes, and of generality, that is, they produce similar behaviors for configurations different from those they were originally evolved for.

1. Evolving Self-Organizing Behaviors for a Swarm-bot
Autonomous Robots, 17(2-3):223-245, 2004 doi> | bibtex | pdf
2. Evolving Aggregation Behaviors in a Swarm of Robots
Lecture Notes in Artificial Intelligence, 2801:865-874, 2003 doi> | bibtex | pdf

 Fault tolerance in groups of physically assembled robots
We address the problem of how a group of mobile robots can self-assemble into physical structures that cooperatively transport a heavy object towards a target location. We examine the situation that some robots of the transport structure exhibit partial failure, that is, they are unable to perceive the target location. We compare the performance of this system with the performance of systems in which the robots that are unable to perceive the target location (i) exhibit complete failure (they are switched off); (ii) get manually removed from the experiment; and (iii) get replaced by fully functional robots. Robots unable to perceive the target location can, by interacting with other members of the group, contribute to task performance, that is, achieve a performance superior to that of a passive caster.

1. Group Transport of an Object to a Target that Only Some Group Members May Sense
Lecture Notes in Computer Science, 3242:852-861, 2004 doi> | bibtex | pdf
2. Transport of an Object by Six Pre-attached Robots Interacting via Physical Links
Proc. of the 2006 IEEE Int. Conf. on Robotics and Automation, IEEE (2006) 1317-1323 doi> | bibtex | pdf

 Adaptive all-terrain navigation
We study the problem of functional self-assembling. The task we consider requires a group of robots to navigate over an area of unknown terrain towards a target light source. If possible, the robots should navigate to the target independently. If, however, the terrain proves too difficult for a single robot, the group should self-assemble into a larger entity and collectively navigate to the target.

1. Self-Assembly Strategies in a Group of Autonomous Mobile Robots
Autonomous Robots, 28(4):439-455, 2010 doi> | bibtex | pdf
2. Self-assembly on demand in a group of physical autonomous mobile robots navigating rough terrain
Lecture Notes in Artificial Intelligence, 3630:272-281, 2005 doi> | bibtex | pdf
3. Performance Benefits of Self-Assembly in a Swarm-Bot
Proc. of the 2007 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IEEE (2007) 2381-2387 doi> | bibtex | pdf

 Pattern formation
This study is about pattern generation in robotic swarm. Using self-organized principles we can let robots form patterns such as chains, clusters and center-periphery. The study is carried out in a simple grid-based simulation environment. Additionally, a mathematical model is proposed.

1. Modeling Pattern Formation in a Swarm of Self-Assembling Robots.
Technical Report TR/IRIDIA/2002-12, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium (2002) pdf

 Negotiation of goal direction for cooperative transport
In this paper, we study the cooperative transport of a heavy object by a group of robots towards a goal. We investigate the case in which robots have partial and noisy knowledge of the goal direction and can not perceive the goal itself. The robots have to coordinate their motion to apply enough force on the object to move it. Furthermore, the robots should share knowledge in order to collectively improve their estimate of the goal direction and transport the object as fast and as accurately as possible towards the goal. We propose a bio-inspired mechanism of negotiation of direction that is fully distributed. Four different strategies are implemented and their performances are compared on a group of four real robots, varying the goal direction and the level of noise.

1. Negotiation of goal direction for cooperative transport
Lecture Notes in Computer Science, 4150:191-202, 2006 doi> | bibtex | pdf

 Evolving chess playing programs
EvoChess is a scientific experiment that uses Internet-connected computers for the evolution of chess playing programs. You can participate by running qoopy, an environment for distributed computing as done by EvoChess. The users can start their own evolution to develop a variety of chess programs. The better ones will survive and produce offspring, who inherit their successful behavior encoded in their genotype.

1. Evolving chess playing programs
Proc. of the Genetic and Evolutionary Computation Conf., Morgan Kaufmann (2002) 740-747 bibtex pdf

© 2001-2015 Roderich Groß, Department of Automatic Control and Systems Engineering (ACSE), The University of Sheffield