Mobile Robot Indoor Autonomous Navigation with Position Estimation Using RF Signal Triangulation

In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle theta) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It’s a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass and the result of triangulation Cartesian estimative, are fused in a Kalman filter in the way to perform optimal estimation and correct robot pose. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments.

Deep Reinforcement Learning Based Mobile Robot Navigation:A Review

Navigation is a fundamental problem of mobile robots,for which Deep Reinforcement Learning(DRL)has received significant attention because of its strong representation and experience learning abilities.There is a growing trend of applying DRL to mobile robot navigation.In this paper,we review DRL methods and DRL-based navigation frameworks.Then we systematically compare and analyze the relationship and differences between four typical application scenarios:local obstacle avoidance,indoor navigation,multi-robot navigation,and social navigation.Next,we describe the development of DRL-based navigation.Last,we discuss the challenges and some possible solutions regarding DRL-based navigation.

Navigation of Non-holonomic Mobile Robot Using Neuro-fuzzy Logic with Integrated Safe Boundary Algorithm

In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.

ARCHITECTURE AND ITS IMPLEMENTATION FOR ROBOTS TO NAVIGATE IN UNKNOWN INDOOR ENVIRONMENTS

It is discussed with the design and implementation of an architecture for a mobile robot to navigate in dynamic and anknown indoor environments. The architecture is based on the framework of Open Robot Control Software at KTH （OROCOS@KTH）, which is also discussed and evaluated to navigate indoor efficiently, a new algorithm named door-like-exit detection is proposed which employs 2D feature oft. door and extracts key points of pathway from the raw data of a laser scanner. As a hybrid architecture, it is decomposed into several basic components which can be classified as either deliberative or reactive. Each component can concurrently execute and communicate with another. It is expansible and transferable and its components are reusable.

Communication-based positioning systems:past,present and prospects

This paper reviews positioning systems in the context of communication systems. First, the basic positioning technique is described for location based ser- vice （LBS） in mobile communication systems. Then the high integrity global posi- tioning system （iGPS） is introduced in terms of aspects of what it is and how the low Earth orbit （LEO） Iridium telecommunication satellites enhance the global posi- tioning system （GPS）. Emphasis is on the Chinese Area Positioning System （CAPS） which is mainly based on commercial geostationary （GEO） communication satellites, including decommissioned GEO and inclined geosynchronous communication satel- lites. Characterized by its low cost, high flexibility, wide-area coverage and ample frequency resources, a distinctive feature of CAPS is that its navigation messages are generated on the ground, then uploaded to and forwarded by the communication satellites. Fundamental principles and key technologies applied in the construction of CAPS are presented in detail from the CAPS validation phase to its experimental system setup. A prospective view of CAPS has concluded it to be a seamless, high ac- curacy, large capacity navigation and communication system which can be achieved by expanding it world wide and enhancing it with LEO satellites and mobile base stations. Hence, this system is a potential candidate for the next generation of radio navigation after GPS.