Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation problem of robotics is safe area search and map building. In this report, a global collision-free path planning algorithm for ground mobile robots in dynamic environments is presented firstly. Considering the advantages of sensor network, the presented path planning algorithm is developed to a sensor network based navigation algorithm for ground mobile robots. The 2D range finder sensor network is used in the presented method to detect static and dynamic obstacles. The sensor network can guide each ground mobile robot in the detected safe area to the target. Furthermore, the presented navigation algorithm is extended into 3D environments. With the measurements of the sensor network, any flying robot in the workspace is navigated by the presented algorithm from the initial position to the target. Moreover, in this report, another navigation problem, safe area search and map building for ground mobile robot, is studied and two algorithms are presented. In the first presented method, we consider a ground mobile robot equipped with a 2D range finder sensor searching a bounded 2D area without any collision and building a complete 2D map of the area. Furthermore, the first presented map building algorithm is extended to another algorithm for 3D map building

Indoor localization of a mobile robot using sensor fusion : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics with Honours at Massey University, Wellington, New Zealand

Reliable indoor navigation of mobile robots has been a popular research topic in recent years. GPS systems used for outdoor mobile robot navigation can not be used indoor (warehouse, hospital or other buildings) because it requires an unobstructed view of the sky. Therefore a specially designed indoor localization system for mobile robot is needed. This project aims to develop a reliable position and heading angle estimator for real time indoor localization of mobile robots. Two different techniques have been developed and each consisted of three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of these three sensor modules is achieved by applying the real time Kalman filter which provides filtered and reliable information of a mobile robot's current location and orientation relative to its environment. Extensive experimental results are provided to demonstrate its improvement over conventional methods like dead reckoning. In addition, a control strategy is developed to control the mobile robot to move along the planned trajectory. The techniques developed in this project have potentials for the application for mobile robots in medical service, health care, surveillances, search and rescue in indoor environments

Occupancy Map Construction for Indoor Robot Navigation

Robot mobile navigation is a hard task that requires, essentially, avoiding static and dynamic objects. This chapter presents a strategy for constructing an occupancy map by proposing a probabilistic model of an ultrasonic sensor, during robot indoor navigation. A local map is initially constructed using the ultrasonic sensor mounted in the front of the robot. This map provides the position of the nearest obstacles in the scene useful for achieving the reactive navigation. The encoders allow computing the robot location in the initial local map. A first path for robot navigation based on the initial local map is estimated using the potential field strategy. As soon as the robot starts its trajectory in real indoor environments with obstacles, the sensor continuously detects and updates the occupancy map by the logsig strategy. A Gaussian function is used for modelling the ultrasonic sensor with the aim of reaching higher precision of the distance measured for each obstacle in the scene. Experiments on detecting, mapping and avoiding obstacles are performed using the mobile robotic platform DaNI 2.0 and the VxWorks system. The resulted occupancy grid is analysed and discussed at the end of this document

A Neural Network Strategy Applied in Autonomous Mobile Localization

In this article, a new approach to the problem of indoor navigation based on ultrasonic sensors is presented, where artificial neural networks (ANN) are used to estimate the position and orientation of a mobile robot.
This approach proposes the use of three Radial Basis Function (RBF) Networks, where environment maps from an ultrasonic sensor and maps synthetically generated are used to estimate the robot localization.
The mobile robot is mainly characterized by its real time
operation based on the Matlab/Simulink environment, where the
whole necessary tasks for an autonomous navigation are done in a hierarchical and easy reprogramming way. 
Finally, practical results of real time navigation related to robot localization in a known indoor environment are shown

Logical Control for Mobile Robots

In this work we present a distributed sensor-based control strategy for mobile robot navigation. We investigate a server-client model, where the clients are executing their tasks in parallel. The logical sensor approach is used as a hybrid framework to model and implement the sensory system for control of the mobile robot. The framework allows for a hierarchical data representation scheme, where sensory data and uncertainty is modeled and used at different levels, depending on the nature of the requested control command

Wavefront Propagation and Fuzzy Based Autonomous Navigation

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments

A genetic algorithm for mobile robot localization using ultrasonic sensors

A mobile robot requires the perception of its local environment for position estimation. Ultrasonic range data provide a robust description of the local environment for navigation. This article presents an ultrasonic sensor localization system for autonomous mobile robot navigation in an indoor semi-structured environment. The proposed algorithm is based upon an iterative non-linear filter, which utilizes matches between observed geometric beacons and an a-priori map of beacon locations, to correct the position and orientation of the vehicle. A non-linear filter based on a genetic algorithm as an emerging optimization method to search for optimal positions is described. The resulting self-localization module has been integrated successfully in a more complex navigation system. Experiments demonstrate the effectiveness of the proposed method in real world applications.Publicad

Mapless LiDAR Navigation Control of Wheeled Mobile Robots Based on Deep Imitation Learning

[[abstract]]This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.[[notice]]補正完