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In this paper, we consider multi-pursuer single-superior-evader pursuit-evasion differential games where the evader has a speed that is similar to or higher than the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work. The proposed algorithm uses the well-known Apollonius circle mechanism to define the capture region of the learning pursuer based on its location and the location of the superior evader. The proposed algorithm uses the Apollonius circle with a developed formation control approach in the tuning mechanism of the fuzzy logic controller (FLC) of the learning pursuer so that one or some of the learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism used by the proposed algorithm also makes the Apollonius circles of each two adjacent pursuers intersect or be at least tangent to each other so that the capture of the superior evader can occur. The proposed algorithm is a decentralized algorithm as no communication among the pursuers is required. The only information the proposed algorithm requires is the position and the speed of the superior evader. The proposed algorithm is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games. The simulation results show the effectiveness of the proposed algorithm.

A Learning Invader for the “Guarding a Territory” Game
A Reinforcement Learning Problem

This paper explores the use of a learning algorithm in the “guarding a territory” game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case the guard is superior (faster) to the invader. We will also consider using models with non-holonomic constraints. A control system is designed and optimized for the invader to play the game and reach Nash Equilibrium. The paper shows how the learning system is able to adapt itself. The system’s performance is evaluated through different simulations and compared to the Nash Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.

Multi-robot systems promise to provide efficient solutions for an ever increasing variety of different applications, such as exploration of hostile environments, disaster recovery, construction, and nano-scale medicine.

Each of these applications is characterized by several aspects, such as environment dynamics (e.g. orbit, water flow, wind, rough terrain, bloodstream), robot types (e.g., mechatronics, sensors, actuators), and communication means (e.g., wifi, vision, stigmergy, etc.).

Physics-based simulation plays a fundamental role in the study of solutions for this wide set of applications.

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Nice robot for the everyone from Sharp.
Several interesting applications for the everyday life depicted in this video.

Spherical Flight Vehicle with single rotor using 4 control surface

The credit card sized, open-spec Udoo Neo SBC features Freescale's Cortex-M4-enhanced i.MX6 SoloX, plus Arduino compatibility, WiFi, Bluetooth, and sensors.

MM7150

Welcome to the Freescale Cup! The following pages are designed to introduce students to the concepts of robotics and the components of the Freescale Cup Car. Programming microcontrollers is challenging, and the content here is developed to facilitate the process of becoming a successful embedded programmer.

FSRs are sensors that allow you to detect physical pressure, squeezing and weight. They are simple to use and low cost. This sensor is a Interlink model 406 FSR with a 38mm square sensing region. Note that this sensor can't detect where on the square you pressed (for that, check out their ribbon soft pots or capacitive touch pad).

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

I sensori a riflessione della serie LR-T rappresentano una sintesi perfetta fra tecnologia innovativa e robustezza funzionale.

Discover online classes taught by top instructors and industry experts. Take courses at your own pace.

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Vision-Doctor.co.uk - Basics, knowledge, know-how, online tools and solutions for industrial machine vision applications - Lars Fermum

The tangent bug algorithm is actually the imroved version of Bug1 and Bug2 algorithms. Unlike these methods, tangent bug algorithm depends on the existence of a range sensor that is mounted on the point robot in the map. By only investigating the output ot this range sensor, and including the knowledge of the robot's current pose and goal's pose, the robot plans actions to reach to the goal.

This web page consists of information about the tangent bug algorithm and its implementation.

This course is a seminar-style hands-on survey of approaches to control and learning in single and multi-robot systems. We will read original seminal papers that track the development of the field and overview the different state-of-the-art approaches to mobile robot control, including reactive, hybrid, and behavior-based based systems. The discussion will focus on the issues of resolving the fundamental conflict between thinking and acting, i.e., high-level deliberation and real-time control. Different approaches and robot control architectures for addressing this issue will be covered and discussed. In the second part of the course we will discuss scaling up robot control to multi-robot systems and swarms of robots. The control architectures discussed in the first part of the semester will be revisited in the context of scaling up to distributed systems. Finally, we will address adaptation and learning in single and multi-robot systems, and deal with the many challenges those problems present. Several other relevant topics will be covered at least briefly, including biological inspirations for robot control and philosophical foundations. All topics will be illustrated with implemented systems and demonstrated with videos.