Shigang Yue

Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method

Aggregation in swarm robotics is referred to as the gathering of spatially distributed robots into a single aggregate. Aggregation can be classified as cue-based or self-organized. In cue-based aggregation, there is a cue in the environment that points to the aggregation area, whereas in self-organized aggregation no cue is present. In this paper, we proposed a novel fuzzy-based method for cue-based aggregation based on the state-of-the-art BEECLUST algorithm. In particular, we proposed three different methods: naïve, that uses a deterministic decision-making mechanism; vector-averaging, using a vectorial summation of all perceived inputs; and fuzzy, that uses a fuzzy logic controller. We used different experiment settings: one-source and two-source environments with static and dynamic conditions to compare all the methods. We observed that the fuzzy method outperformed all the other methods and it is the most robust method against noise.

Colias: An Autonomous Micro Robot for Swarm Robotic Applications:

Robotic swarms that take inspiration from nature are becoming a fascinating topic for multi-robot researchers. The aim is to control a large number of simple robots in order to solve common complex tasks. Due to the hardware complexities and cost of robot platforms, current research in swarm robotics is mostly performed by simulation software. The simulation of large numbers of these robots in robotic swarm applications is extremely complex and often inaccurate due to the poor modelling of external conditions. In this paper, we present the design of a low-cost, open-platform, autonomous micro-robot (Colias) for robotic swarm applications. Colias employs a circular platform with a diameter of 4 cm. It has a maximum speed of 35 cm/s which enables it to be used in swarm scenarios very quickly over large arenas. Long-range infrared modules with an adjustable output power allow the robot to communicate with its direct neighbours at a range of 0.5 cm to 2 m. Colias has been designed as a complete platform with supporting software development tools for robotics education and research. It has been tested in both individual and swarm scenarios, and the observed results demonstrate its feasibility for use as a micro-sized mobile robot and as a low-cost platform for robot swarm applications.

Development of an autonomous micro robot for swarm robotics

Swarm robotic systems which are inspired from social behaviour of animals especially insects are becoming a fascinating topic for multi-robot researchers. Simulation software is mostly used for performing research in swarm robotics due the hardware complexities and cost of robot platforms. However, simulation of large numbers of these swarm robots is extremely complex and often inaccurate. In this paper we present the design of a low-cost, open-platform, autonomous micro robot (Colias) for swarm robotic applications. Colias uses a circular platform with a diameter of 4 cm. Long-range infrared modules with adjustable output power allow the robot to communicate with its direct neighbours. The robot has been tested in individual and swarm scenarios and the observed results demonstrate its feasibility to be used as a micro sized mobile robot as well as a low-cost platform for robot swarm applications.

Bio-Inspired Embedded Vision System for Autonomous Micro-Robots: The LGMD Case

In this paper, we present a new bio-inspired vision system embedded for micro-robots. The vision system takes inspiration from locusts in detecting fast approaching objects. Neurophysiological research suggested that locusts use a wide-field visual neuron called lobula giant movement detector (LGMD) to respond to imminent collisions. In this paper, we present the implementation of the selected neuron model by a low-cost ARM processor as part of a composite vision module. As the first embedded LGMD vision module fits to a micro-robot, the developed system performs all image acquisition and processing independently. The vision module is placed on top of a micro-robot to initiate obstacle avoidance behavior autonomously. Both simulation and real-world experiments were carried out to test the reliability and robustness of the vision system. The results of the experiments with different scenarios demonstrated the potential of the bio-inspired vision system as a low-cost embedded module for autonomous robots.

Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm

Aggregation is one of the most fundamental behaviors and has been studied in swarm robotic researches for more than two decades. Studies in biology have revealed that the environment is a preeminent factor, especially in cue-based aggregation. This can be defined as aggregation at a particular location which is a heat or a light source acting as a cue indicating an optimal zone. In swarm robotics, studies on cue-based aggregation mainly focused on different methods of aggregation and different parameters such as population size. Although of utmost importance, environmental effects on aggregation performance have not been studied systematically. In this paper, we study the effects of different environmental factors: size, texture and number of cues in a static setting, and moving cues in a dynamic setting using real robots. We used the aggregation time and size of the aggregate as the two metrics with which to measure aggregation performance. We performed real robot experiments with different population sizes and evaluated the performance of aggregation using the defined metrics. We also proposed a probabilistic aggregation model and predicted the aggregation performance accurately in most of the settings. The results of the experiments show that environmental conditions affect the aggregation performance considerably and have to be studied in depth.

Fuzzy-based aggregation with a mobile robot swarm

Aggregation is a widely observed phenomenon in social insects and animals such as cockroaches, honeybees and birds. From swarm robotics perspective [3], aggregation can be defined as gathering randomly distributed robots to form an aggregate. Honeybee aggregation is an example of cue-based aggregation method that was studied in [4]. In that study, micro robots were deployed in a gradually lighted environment to mimic the behavior of honeybees which aggregate around a zone that has the optimal temperature (BEECLUST). In our previous study [2], two modifications on BEECLUST --- dynamic velocity and comparative waiting time --- were applied to increase the performance of aggregation.

Comparison of Different Cue-Based Swarm Aggregation Strategies

In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naive with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naive method.

COSΦ: Artificial pheromone system for robotic swarms research

Pheromone-based communication is one of the most effective ways of communication widely observed in nature. It is particularly used by social insects such as bees, ants and termites; both for inter-agent and agent-swarm communications. Due to its effectiveness; artificial pheromones have been adopted in multi-robot and swarm robotic systems for more than a decade. Although, pheromone-based communication was implemented by different means like chemical (use of particular chemical compounds) or physical (RFID tags, light, sound) ways, none of them were able to replicate all the aspects of pheromones as seen in nature. In this paper, we propose a novel artificial pheromone system that is reliable, accurate and it uses off-the-shelf components only - LCD screen and low-cost USB camera. The system allows to simulate several pheromones and their interactions and to change parameters of the pheromones (diffusion, evaporation, etc.) on the fly allowing for controllable experiments. We tested the performance of the system using the Colias platform in single-robot and swarm scenarios. To allow the swarm robotics community to use the system for their research, we provide it as a freely available open-source package.

Bio-inspired collision detector with enhanced selectivity for ground robotic vision system

There are many ways of building collision-detecting systems. In this paper, we propose a novel collision selective visual neural network inspired by LGMD2 neurons in the juvenile locusts. Such collision-sensitive neuron matures early in the first-aged or even hatching locusts, and is only selective to detect looming dark objects against bright background in depth, represents swooping predators, a situation which is similar to ground robots or vehicles. However, little has been done on modeling LGMD2, let alone its potential applications in robotics and other vision-based areas. Compared to other collision detectors, our major contributions are first, enhancing the collision selectivity in a bio-inspired way, via constructing a computing efficient visual sensor, and realizing the revealed specific characteristic sofLGMD2. Second, we applied the neural network to help rearrange path navigation of an autonomous ground miniature robot in an arena. We also examined its neural properties through systematic experiments challenged against image streams from a visual sensor of the micro-robot.

Power-Law Distribution of Long-Term Experimental Data in Swarm Robotics

Bio-inspired aggregation is one of the most fundamental behaviours that has been studied in swarm robotic for more than two decades. Biology revealed that the environmental characteristics are very important factors in aggregation of social insects and other animals. In this paper, we study the effects of different environmental factors such as size and texture of aggregation cues using real robots. In addition, we propose a mathematical model to predict the behaviour of the aggregation during an experiment.