Discrete Multi-Valued Particle Swarm Optimization

Discrete optimization is a difficult task common to many different areas in modern research. This type of optimization refers to problems where solution elements can assume one of several discrete values. The most basic form of discrete optimization is binary optimization, where all solution elements can be either 0 or 1, while the more general form is problems that have solution elements which can assume $n$ different unordered values, where $n$ could be any integer greater than 1. While Genetic Algorithms (GA) are inherently able to handle these problems, there has been no adaption of Particle Swarm Optimization able to solve the general case

The Cost of Reality: Effects of Real-World Factors on Multi-Robot Search

Designing algorithms for multi-robot systems can be a complex and difficult process: the cost of such systems can be very high, collecting experimental data can be time consuming, and individual robots may malfunction, invalidating experiments. These constraints make it very tempting to work using high-level abstractions of the robots and their environment. While these high-level models can be useful for initial design, it is important to verify techniques in more realistic scenarios that include real-world effects that may have been ignored in the abstractions. In this paper, we take a simple, coordinated, multi-robot search algorithm and illustrate problems that it encounters in environments which incorporate real-world factors, such as probabilistic target detection and positional noise. We compare the performance to that of several simple randomized approaches, which are better able to deal with these constraints

Distributed Adaptation in Multi-Robot Search using Particle Swarm Optimization

We present an adaptive strategy for a group of robots engaged in the localization of multiple targets. The robotic search algorithm is inspired by chemotaxis behavior in bacteria, and the algorithmic parameters are updated using a distributed implementation of the Particle Swarm Optimization technique. We explore the efficacy of the adaptation, the impact of using local fitness measurements to improve global fitness, and the effect of different particle neighborhood sizes on performance. The robustness of the approach in non-static environments is tested in a time-varying scenario

Relative Localization and Communication Module for Small-Scale Multi-Robot Systems

We characterize and improve an existing infrared relative localization/communication module used to find range and bearing between robots in small-scale multi-robot systems. Modifications to the algorithms of the original system are suggested which offer better performance. A mathematical model which accurately describes the system is presented and allows us to predict the performance of modules with augmented sensorial capabilities. Finally, the usefulness of the module is demonstrated in a multi-robot self-localization task using both a realistic robotic simulator and real robots, and the performance is analyzed

Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Recently, techniques have been adopted for multi-robot search from the Particle Swarm Optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by Particle Swarm Optimization. Additionally, we exploit this inspiration by modifying the Particle Swarm Optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range

An Exploration of Online Parallel Learning in Heterogeneous Multi-Robot Swarms

Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers online in an automated fashion. In multi-robot systems, robots learning in parallel can share information to dramatically increase the evolutionary rate. However, manufacturing variations in robotic sensors may result in perceptual differences between robots, which could impact the learning process. In this paper, we explore how varying sensor offsets and scaling factors affects parallel swarm-robotic learning of obstacle avoidance behavior using both Genetic Algorithms and Particle Swarm Optimization. We also observe the diversity of robotic controllers throughout the learning process using two different metrics in an attempt to better understand the evolutionary process

Stoke-on-Trent Opportunity Area Programme Improving Outcomes for Early Years ‘Understanding the World – Area of Learning’

This is a Stoke-on-Trent Opportunity Area funded project. The Opportunity Area Programme seeks to improve social mobility for children and young people, to break the link between social background and destination. Stoke-on-Trent is one of 12 areas selected for additional support from the DfE, working through a partnership of local leaders. The project relates to improving outcomes in the Early Years Foundation Stage (EYFS) to give children the best possible start in life and learning. Data shows that only 71% of pupils achieve or exceed the expected standard against the Understanding the World Area of Learning which incorporates three Early Learning Goals (ELGs). Children in the most deprived wards are least likely to achieve the standard

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“Understanding the World”: a pilot study of effective practice and provision in early years settings

This small-scale study explored effective practice and provision in early years settings to support children’s learning. The research was funded by an Opportunity Area programme, a government policy for dealing with social mobility through education. The research investigated the experiences and perceptions of early years practitioners in relation to the Understanding the World area of learning which incorporates three of the seventeen Early Learning Goals contained within the Early Years Foundation Stage. These Early Learning Goals are; ‘People and Communities’, ‘The World’ and ‘Technology’. The study was conducted within the city of Stoke-on-Trent in England where official published data reveals only 71% of pupils achieve or exceed the expected standard against the Understanding the World area of learning where the national average is 83%. The research adopted a mixed methods approach comprising an online survey and semi-structured interviews with practitioners working with children in private and maintained day nurseries and primary school reception classes where good and outstanding results are achieved for the city’s disadvantaged children. The findings of the study include the identification of best practice examples along with features of effective provision. Barriers to children’s progression and attainment of these specific Early Learning Goals were also ascertained. The implications for practice and further research are presented

Communication in a Swarm of Miniature Robots: The e-Puck as an Educational Tool for Swarm Robotics

Swarm intelligence, and swarm robotics in particular, are reaching a point where leveraging the potential of communication within an artificial systempromises to uncover newand varied directions for interesting research without compromising the key properties of swarmintelligent systems such as self-organization, scalability, and robustness. However, the physical constraints of using radios in a robotic swarm are hardly obvious, and the intuitive models often used for describing such systems do not always capture them with adequate accuracy. In order to demonstrate this effectively in the classroom, certain tools can be used, including simulation and real robots. Most instructors currently focus on simulation, as it requires significantly less investment of time, money, and maintenance—but to really understand the differences between simulation and reality, it is also necessary to work with the real platforms from time to time. To our knowledge, our coursemay be the only one in the world where individual students are consistently afforded the opportunity to work with a networked multi-robot system on a tabletop. The e-Puck,1 a low-cost small-scale mobile robotic platform designed for educational use, allows us bringing real robotic hardware into the classroom in numbers sufficient to demonstrate and teach swarm-robotic concepts.We present here a custom module for local radio communication as a stackable extension board for the e-Puck, enabling information exchange between robots and also with any other IEEE 802.15.4-compatible devices. Transmission power can be modified in software to yield effective communication ranges as small as fifteen centimeters. This intentionally small range allows us to demonstrate interesting collective behavior based on local information and control in a limited amount of physical space, where ordinary radios would typically result in a completely connected network. Here we show the use of this module facilitating a collective decision among a group of 10 robots