Alan F. T. Winfield

Distributed Sensing and Data Collection Via Broken Ad Hoc Wireless Connected Networks of Mobile Robots

This paper reports on ongoing work to develop ad hoc wireless networking for application in distributed mobile robotics. The paper discusses a mission scenario in which a number of mobile robots are required to autonomously disperse into a physically bounded region, take sensor readings at predetermined time intervals, and then communicate the sense data back to a single collection point. There is assumed to be no overall command and control structure. The robots are assumed to be equipped with low-power short-range wireless network interfaces, which only allow direct communication with near neighbours, and overall an ad hoc (multi-hop) wireless networking scheme is employed. Furthermore, it is assumed that there are insufficient robots to allow full wireless connectivity through the region so that, at any instant, the robots actually appear as a set of disconnected sub-nets (i.e. a broken ad hoc network). The paper proposes a mechanism that exploits the mobility of the robots to overcome the lack of overall wireless network connectivity, resulting in a sub-optimal but robust scheme for garnering sense data. The paper presents simulation results that show how the arithmetic mean data collection delay varies with the number of robots deployed. The paper concludes that in mission scenarios that do not require data collection in real-time and where occasional data loss (erasure) is acceptable, then the proposed data collection mechanism is feasible, robust and economical in terms of the number of robots required.

Interactive robots in experimental biology

Interactive robots have the potential to revolutionise the study of social behaviour because they provide several methodological advances. In interactions with live animals, the behaviour of robots can be standardised, morphology and behaviour can be decoupled (so that different morphologies and behavioural strategies can be combined), behaviour can be manipulated in complex interaction sequences and models of behaviour can be embodied by the robot and thereby be tested. Furthermore, robots can be used as demonstrators in experiments on social learning. As we discuss here, the opportunities that robots create for new experimental approaches have far-reaching consequences for research in fields such as mate choice, cooperation, social learning, personality studies and collective behaviour.

Towards Energy Optimization: Emergent Task Allocation in a Swarm of Foraging Robots

This article presents a simple adaptation mechanism to automatically adjust the ratio of foragers to resters (division of labor) in a swarm of foraging robots and hence maximize the net energy income to the swarm. Three adaptation rules are introduced based on local sensing and communications. Individual robots use internal cues (successful food retrieval), environmental cues (collisions with team-mates while searching for food) and social cues (team-mate success in food retrieval) to dynamically vary the time spent foraging or resting. Simulation results show that the swarm demonstrates successful adaptive emergent division of labor and robustness to environmental change (in food source density), and we observe that robots need to cooperate more when food is scarce. Furthermore, the adaptation mechanism is able to guide the swarm towards energy optimization despite the limited sensing and communication abilities of the individual robots and the simple social interaction rules. The swarm also exhibits the capacity to collectively perceive environmental changes; a capacity that can only be observed at a group level and cannot be deduced from individual robots.

Environment-driven Distributed Evolutionary Adaptation in a Population of Autonomous Robotic Agents

This article is concerned with a fixed-size population of autonomous agents facing unknown, possibly changing, environments. The motivation is to design an embodied evolutionary algorithm that can cope with the implicit fitness function hidden in the environment so as to provide adaptation in the long run at the level of population. The proposed algorithm, termed mEDEA, is shown to be both efficient in unknown environments and robust to abrupt and unpredicted changes in the environment. The emergence of consensus towards specific behavioural strategies is examined, with a particular focus on algorithmic stability. Finally, a real-world implementation of the algorithm is described with a population of 20 real-world e-puck robots.

The application of wireless local area network technology to the control of mobile robots

This paper describes a communications and control infrastructure for distributed mobile robotics, which makes use of wireless local area network (WLAN) technology and Internet Protocols (IPs). The use of commercial off-the-shelf (COTS) hardware and software components, and protocols, results in a powerful platform for conducting experiments into collective or co-operative robotics. Standard Transmission Control Protocol/Internet Protocol (TCP/IP) compatible applications programming interfaces (APIs) allow for rapid and straightforward development of applications software. Further, the message bandwidth available from WLAN interfaces (1‐2 Mbits/s) facilitates multi-robot experiments requiring high data rates, for instance in robot vision or navigation. The infrastructure described is equally applicable to teleoperated mobile robots. q 2000 Elsevier Science B.V. All rights reserved.

Modeling and Optimization of Adaptive Foraging in Swarm Robotic Systems

Understanding the effect of individual parameters on the collective performance of swarm robotic systems in order to design and optimize individual robot behaviors is a significant challenge. In this paper we present a macroscopic probabilistic model of adaptive collective foraging in a swarm of robots, where each robot in the swarm is capable of adjusting its time threshold parameters following the rules described by Liu et al. 2007. The swarm adapts the ratio of foragers to resters (division of labor) in order to maximize the net swarm energy for a given food density. A probabilistic finite state machine (PFSM) and a number of difference equations are developed to describe collective foraging at a macroscopic level. To model adaptation we introduce the new concepts of the sub-PFSM and private/public time thresholds. The model has been validated extensively with simulation trials, and results show that the model achieves very good accuracy in predicting the group performance of the swarm. Finally, a real-coded genetic algorithm is used to explore the parameter spaces and optimize the parameters of the adaptation algorithm. Although this paper presents a macroscopic probabilistic model for adaptive foraging, we argue that the approach could be applied to any adaptive swarm system in which the heterogeneity of the system is coupled with its time parameters.

Special issue on swarm robotics

Swarm robotics is a new approach to the coordination of multi-robot systems. In contrast with traditional multi-robot systems which use centralised or hierarchical control and communication systems in order to coordinate robots’ behaviours, swarm robotics adopts a decentralised approach in which the desired collective behaviours emerge from the local interactions between robots and their environment. Such emergent or self-organised collective behaviours are inspired by, and in some cases modelled on, the swarm intelligence observed in social insects. The potential for swarm robotics is considerable. Any task in which physically distributed objects need to be explored, surveyed, collected, harvested, rescued, or assembled into structures is a potential real-world application for swarm robotics. The key advantage of the swarm robotics approach is robustness, which manifests itself in a number of ways. Firstly, because a swarm of robots consists of a number of relatively simple and typically homogeneous robots, which are not pre-assigned to specific roles or tasks within the swarm, then the swarm can self-organise or dynamically re-organise the way individual robots are deployed. Secondly, and for the same reasons, the swarm approach is highly tolerant to the failure of individual robots. Thirdly, the fact that control is completely decentralised means that there is no common-mode failure point or vulnerability in the swarm. Indeed, it could be said that the high level of robustness evident in robotic swarms comes for free in the sense that it is intrinsic to the swarm robotics approach, which contrasts with the high engineering cost of fault tolerance in conventional robotic systems. The realisation of the potential of swarm robotics requires the solution of a number of very challenging problems. Firstly, in algorithm design: swarm roboticists face the problem of designing both the physical morphology and behaviours of the individual robots such that when those robots interact with each other and their environment, the desired overall collective behaviours will emerge. At present there are no principled approaches to the design of low-level behaviours for a given desired collective behaviour. Secondly, in implementation

On Formal Specification of Emergent Behaviours in Swarm Robotic Systems

It is a characteristic of swarm robotics that specifying overall emergent swarm behaviours in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation we need such specifications. This paper explores the use of temporal logic to formally specify, and possibly also prove, the emergent behaviours of a robotic swarm. The paper makes use of a simplified wireless connected swarm as a case study with which to illustrate the approach. Such a formal approach could be an important step toward a disciplined design methodology for swarm robotics.

Modelling a wireless connected swarm of mobile robots

It is a characteristic of swarm robotics that modelling the overall swarm behaviour in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation such models would be critical for both overall validation of algorithm correctness and detailed parameter optimisation. We seek models with predictive power: models that allow us to determine the effect of modifying parameters in individual robots on the overall swarm behaviour. This paper presents results from a study to apply the probabilistic modelling approach to a class of wireless connected swarms operating in unbounded environments. The paper proposes a probabilistic finite state machine (PFSM) that describes the network connectivity and overall macroscopic behaviour of the swarm, then develops a novel robot-centric approach to the estimation of the state transition probabilities within the PFSM. Using measured data from simulation the paper then carefully validates the PFSM model step by step, allowing us to assess the accuracy and hence the utility of the model.

Open-hardware e-puck Linux extension board for experimental swarm robotics research

In this paper we describe the implementation of a Linux extension board for the e-puck educational mobile robot, designed to enhance the computation, memory and networking performance of the robot at very low cost. The extension board is based on a 32-bit ARM9 microprocessor and provides wireless network support. The ARM9 extension board runs in parallel with the dsPIC microprocessor on the e-puck motherboard with communication between the two via an SPI bus. The extension board is designed to handle computationally intensive image processing, wireless communication and high-level intelligent robot control algorithms, while the dsPIC handles low-level sensor interfacing, data processing and motor control. The extension board runs an embedded Linux operating system, along with a Debian-based port of the root file system stored in a Micro SD card. The extended e-puck robot platform requires minimal effort to integrate the well-known open-source robot control framework Player and, when placed within a TCP/IP networked infrastructure, provides a powerful and flexible platform for experimental swarm robotics research.