Visual attitude stabilization of a unmanned helicopter in unknown environments with an embedded single-board computer

In this paper, we propose an autonomous navigation for Unmanned Ground Vehicles (UGVs) by a path selection method based on local features of terrain in unknown outdoor rough terrain environment. The correlation between a local terrain feature obtained from a path and a value of the path obtained from the global path planning is extracted in advance. When UGV comes to a branch while it is running, the value of path is estimated using the correlation with local terrain feature. Thereby, UGV navigation is performed by path selection under the criterion of shortest time in unknown outdoor environment. We experimented on a simulator and confirmed that the proposed method can select more effective paths in comparison with a simple path selection method.

This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner.1 Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments.

The second cause of disability in Mexico is visual impairment; 26% population is blind. Even though blind people maintain a lower quality’s life than sighted people, there is not enough social interest to develop comprehensive solutions that improve it. Although many voice-activated emerging technologies use artificial intelligence to get human-machine communication through intelligent virtual assistants, such as Alexa, Siri, Google Home, and Cortana, among others, in reality, there have not been developed specific tools just for blind people that help them to improve their independent behavior because they always depend on other people in doing their daily tasks, more even when they need to move around unknown environments for them. This document reports the development of an autonomous mobility system to be operated by blind people. By audio reconstruction, it is possible to dictate, to the blind pedestrian, in real-time, the presence of traffic lights, crosswalks, and information about their current location. This system employs real-time computer vision tools, artificial intelligence, audio playback systems, and location systems, and it improves the independent behavior of blind people because they could move through unknown environments without the assistance of any sighted person, giving them greater independence and consequently increasing their life’s quality.

In unknown environments, it is highly important to acquire efficiently the map of the unknown environments, which can be realized with swarm robot systems. However, swarm robots suffer from the lack of sensor data due to inevitably limited resources. Then, the individual maps of swarm robots degenerate as time goes, and thus the collective map of merging the individual maps may be inaccurate. This paper presents a feature-based map merging system with mesh networks to acquire individual maps efficiently and merge them accurately. The proposed system was tested with two small robots equipped with relatively low-cost sensors in unknown indoor environments. The experimental results showed that the proposed system acquired successfully individual maps and merged them accurately.

This paper presents a methodology for the automatic selection of heuristic scanning positions in unknown indoor environments. The surveying is carried out by a robotic system following a stop-and-go procedure. Starting with a random scan position in the room, the point cloud is discretized in voxels and they are submitted to a two-step classification and are labelled as occupied, occluded, empty, window, door, or exterior based on a visibility analysis. The main objective of the methodology is to obtain a complete point cloud of the indoor space and accordingly, the next best position is the scan position minimizing occluded voxels. Because the method locates doors and windows, the room can be delimited and the scan can continue for adjacent rooms. This approach has been tested in a real case study, in which three scans were developed.

Mobile robots have been increasingly applied in many different scenarios, such as space exploration and search and rescue, where the robots are required to travel over uneven terrain while outdoors. This book provides a new framework and the related algorithms for designing autonomous mobile robotic systems in such unknown outdoor environments.

Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary points that the robot must traverse before reaching the goal. Our approach makes use of a global planner (to find the next best waypoint at any instant), a local planner (to plan the path to a specific waypoint) and an adaptive Model Predictive Control strategy (for robust system control and faster maneuvers). We evaluate our algorithm on a set of randomly generated obstacle maps, intermediate waypoints and start-goal pairs, with results indicating significant reduction in computational costs, with high accuracies and robust control.

Robot odor source localization in indoor environments based on gradient adaptive extremum seeking search

This paper proposes a method for collaborative exploration adopting multiple UAVs in an unknown GPS-denied indoor environment. The core of this method is to use the Tracking-D*Lite algorithm to track moving targets in unknown terrain, combined with the Wall-Around algorithm based on the Bug algorithm to navigate the UAV in the unknown indoor environment. The method adopts the advantages of the above two algorithms, where the UAV applies the Wall-Around algorithm to fly around the wall and utilizes the Tracking-D*Lite algorithm to achieve collaboration among UAVs. This method is simulated and visualized by using Gazebo, and the results show that it can effectively take the advantages of multiple UAVs to explore the unknown indoor environments. Moreover, the method can also draw the boundary-contour map of the entire environment at last. Once extended to the real world, this method can be applied to dangerous buildings after earthquakes, hazardous gas factories, underground mines, or other search and rescue scenarios.

Simultaneous localization and mapping (SLAM) has been richly researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the wide deployment of Wifi wireless network. In this paper, we present a novel approach for crowd-sensing simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system makes use of the received signal strength (RSS) from surrounding Wifi access points (AP) and the motion tracking data from a smart phone (Tango as an example). These measurements are captured duration the walking of multiple users in unknown environments without map information and location of the AP. The experiments were done in a university building with dynamic environment and the results show that the proposed system is capable of estimating the tracks of a group of users with an accuracy of 1.74 meters when compared to the ground truth acquired from a point cloud-based SLAM.

We address in this paper the problem of the autonomous online learning of a sensory-motor task, demonstrated by an operator guiding the robot. For the last decade, we have developed a vision-based architecture for mobile robot navigation. Our bio-inspired model of the navigation has already proved to achieve sensory-motor tasks in real time both in unknown indoor and outdoor environments. We propose to bootstrap the underlying PerAc architecture in order to control the sensory-motor learning. The interaction leads the robot to autonomously build a precise sensory-motor dynamic approximating the behavior of the teacher. A real dialog based on actions imposed by the teacher and those proposed by the robot emerges, which catalyzes the learning of the robot. The architecture is finally tested in real indoor and outdoor environments.

A conditional shortest path is a collision-free path of shortest distance based on known information on an obstacle-scattered environment at a given time. This paper investigates the problem of finding a conditional L/sub 2/ shortest path through an unknown environment in which path planning is implemented "on the fly" as new obstacle information becomes available through external sensors. We propose a novel cell decomposition approach which calculates an L/sub 2/ distance transform through the use of a circular path-planning wave. The proposed method is based on a new data structure, called the framed-quadtree, which combines together the accuracy of grid-based path planning techniques with the efficiency of quadtree-based techniques, hence having the advantages of both. The heart of this method is a linear time algorithm for computing dynamic Voronoi diagrams.

This work presents a proposal for an Ambient Assisted Wheelchair (A2W). Such a vehicle could collect useful information provided by Intelligent Environments equipped with appropriate wireless services. Two different networks have been considered for this tasks: a standard Wi-Fi network that will allow a connection to Internet Services providing building accessibility information; and a specific sensor network able to provide indoor location. The Buildings Accessibility Information Service (BAIS) is aimed for providing route planning in unknown indoor environments. BAIS system is based on a set of XML description files that contain all the information needed to access public or private buildings: floor maps, accessibility information, available routes and calibration landmarks. Sensor Network (SN) is based on a set of wireless nodes equipped with ZigBee devices; RSSI (Received Signal Strength Indication) from fixed nodes are used to compute an approximate 3D location that reinforces vehicle positioning inside buildings. Both resources BAIS and SN (when installed) could provide valuable information to any kind of user inside unknown environments, although best performances will be achieved with A2W units.

We present a navigation system using multiple sensors for unknown and dynamic indoor environments. To achieve the robustness and flexibility of the mobile robot, we propose a new behavior-based architecture with three groups of clustered (reflexive, purposive, and adaptive) agents that realizes both efficiency in attaining the mission of the robot and robustness against the various kinds or failures that may occur in a dynamic environment. Basic behaviors required for navigation, such as, avoiding obstacles, moving towards free space, and following targets, are redundantly developed as agents and combined in the behavior-based system architecture.

In this article we face the problem of navigating a mobile robot on an unknown indoor environment. The parti-game approach is used for simultaneous learning of a world model, and learning a path from an initial location to a specified goal region. These two learning abilities may be seen as cooperating and enhancing each other in order to improve the overall system performance. It is shown that the constructed world model is general-purpose, in the sense that its usefulness is not restricted to be used on self-learning a particular path, but may be valuable for learning paths with different (start, goal) pairs. The robot uses its own infrared distance-sensors to perform obstacle detection while moving. Is also has the predefined ability of performing straight-line motions. Simulation results are presented that validate the effectiveness of the approach.