Toolleeo's links
This video demonstrates the concept of series-elastic actuators (SEAs) and how they are used in rehabilitation robotics.
Python sample codes for robotics algorithms.
Filtering, SLAM, path planning, kinematics.
This is the abstract of the paper published on this painting UAV.
This paper describes a system for autonomous spray painting using a UAV, suitable for industrial applications. The work is motivated by the potential for such a system to achieve accurate and fast painting results. The PaintCopter is a quadrotor that has been custom fitted with an arm plus a spray gun on a pan-tilt mechanism. To enable long deployment times for industrial painting tasks, power and paint are delivered by lines from an external unit. The ability to paint planar surfaces such as walls in single color is a basic requirement for a spray painting system. But this work addresses more sophisticated operation that subsumes the basic task, including painting on 3D structure, and painting of a desired texture appearance. System operation consists of (a) an offline component to capture a 3D model of the target surface, (b) an offline component to design the painted surface appearance, and generate the associated robotic painting commands, (c) a live system that carries out the spray painting. Experimental results demonstrate autonomous spray painting by the UAV, doing area fill and versatile line painting on a 3D surface.
Donkey Car trained with Double Deep Q Learning (DDQN) in Unity Simulator.
A unique method in the world to learn ROS online.
Entering and mastering ROS in only 5 days.
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.
TeraRanger One is the most advanced distance sensor for robotics.
Its performance and lightweight design enable robotics applications that were previously impossible with slower sonar sensors or big heavy lasers. The sensor is fully eye-safe, pre-calibrated and ROS compatible. Simply attach it to your robot and you’re ready to go!
The sensor comes in two case designs but can also be customized in shape and colour for special applications. Please contact us for personalized solutions and configurations.
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" - 4개의 조종면을 사용한 단일로터 방식의 구형 비행체 Spherical flight vehicle with a single rotor which can do...
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.