In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

https://journals.sagepub.com/doi/pdf/10.5772/57313

A Survey and Analysis of Multi-Robot Coordination

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

This paper presents a method of realizing artificial neural networks (ANNs) hardware implementation using field programmable analog arrays (FPAAs). A simplified realization for neurons with piecewise linear activation functions is used to reduce the complexity of the neural network architecture. A feedforward neural network is implemented using multi-chip FPAAs. Anadigm's commercially available AN221E04 FPAA chips are adopted as the platform for simulation and experiments. The FPAA based ANN classifies two groups of data with zero error at a speed of 6.0 million connections per second (MCPS). The result is more than 1400 times faster than software implementation. The ANN architecture is also expandable to perform more complicated tasks by incorporating more FPAA chips into the implementation. The programmability of the FPAA makes rapid prototyping possible.

Implementation of Artificial Neural Network for Real Time Applications Using Field Programmable Analog Arrays

Dedicated hardware implementations of artificial neural networks promise to provide faster, lower-power operation when compared to software implementations executing on microprocessors, but rarely do these implementations have the flexibility to adapt and train online under dynamic conditions. A typical design process for artificial neural networks involves offline training using software simulations and synthesis and hardware implementation of the obtained network offline. This paper presents a design of block-based neural networks (BbNNs) on FPGAs capable of dynamic adaptation and online training. Specifically the network structure and the internal parameters, the two pieces of the multiparametric evolution of the BbNNs, can be adapted intrinsically, in-field under the control of the training algorithm. This ability enables deployment of the platform in dynamic environments, thereby significantly expanding the range of target applications, deployment lifetimes, and system reliability. The potential and functionality of the platform are demonstrated using several case studies.

/pdf/evolvable-block-based-neural-network-design-for-applications-2v3520et4x.pdf

Evolvable block-based neural network design for applications in dynamic environments

In this article we focus on the essential requirements of a robot for semantically dealing with cooperative problem solving in a natural setting such as discovery of exit from a labyrinth. A very common application area for such robots are used as set of multi-agents at junctions in Traffic Management and Information Systems (TMIS) to manage the traffic and interact with ambulance or fire fighters to traverse the shortest possible path [18]. In order to present the above mentioned goal, a new modular architecture for design and implementation of cooperative labyrinth discovery robots (CLDRs) is presented. The labyrinth data structure (Maze Set) is represented in notation 3 format useful for semantic logic data processing purposes. This facilitates semantic web services on each CLDR (agent). Each agent is an autonomous system which acts based on its sensors and information retrieved from other agents. Our aim is creating robotic agents based on semantic web services and implementing autonomous semantic agents (ASAs). Numerous domains are likely application areas for multiple ASAs (MASAs), such as systems for distributed control, decision support, and financial market projection. Initial application area will be "Cooperative Labyrinth Discovery" where MASA robots cooperatively inspect an uncharted maze to discover its exits. Hence, context awareness, semantic understanding, near area communication, and friend/foe distinction make up upper value sets loaded upon "automaton" nature of a robotic ASA. We are aiming at effecting coordination and cooperation among MASAs towards succeeding a shared goal through semantic web theory and techniques.

Human-Robot Interactive Communication Using Semantic Web Tech. in Design and Implementation of Collaboratively Working Robots

Most commonly, neural networks models or algorithms are simulated and implemented by computer programming in neural networks theory research. But in many practical applications, it is necessary to consider essential issues such as hardware implementation. Specific application of neural networks hardware has the advantages of high speed, small in size, good performance and low cost. Thus, the implementation of high performance neural networks hardware is the final target in some actual applications. In this paper, a hardware structure of single perceptron that serves as the basic nerve cell and its implementation method with FPGA is introduced. It is based on VLSI implementation approach for the standard neural networks. The method proposed is a primary discussion and research for the hardware implementation of artificial neural networks.

The hardware structure design of perceptron with FPGA implementation

Autonomous robot soccer system consists of a multi-agent system that need to cooperate in unknown, dynamic, and competitive environment. Thus, it is a crucial issue how to realize effective and real time communications among soccer robots. There are three problems need to solve about the communication, they are dialog management, communication language, and communication protocol. Thereinto, communication language is a intermedium to transport information and exchange knowledge among agents. In this paper, we introduce the communication mechanism of autonomous robot soccer system, and its practical and reliable communication language based on KQML (Knowledge Query and Manipulation Language), which is designed for cooperative problem solving and knowledge sharing of the multi-robot system. It is shown that the communication mechanism is effective, efficient robust.

Design and realization of communication mechanism of autonomous robot soccer based on multi-agent system

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The hardware structure design of perceptron with FPGA implementation

Design and realization of communication mechanism of autonomous robot soccer based on multi-agent system