A potential field approach for reactive navigation of autonomous sailboats

Navigation techniques for autonomous sailboats are faced with two inherent difficulties. The uncontrollable and partially unpredictable nature of thrust forces on one hand and the complex kinematics of sailboats on the other hand. This paper proposes a new reactive navigation approach, based on artificial potential fields, that addresses these two problems simultaneously. Environment and specific sailboat navigation constraints are represented by a local potential built around the vehicle location. Changes of wind direction and detected obstacles affect this periodically updated potential, which guarantees the real-time computation of a feasible heading for the boat. Numerical simulations are presented and validate the proposed algorithm under various wind conditions. Field trials eventually illustrate the efficiency of this navigation technique using a reduced-scale autonomous sailboat prototype.     

Inertial navigation sensor integrated motion analysis for autonomous vehicle navigation

Many types of existing vehicles contain an inertial navigation system (INS) that can be utilized to greatly improve the performance of motion analysis techniques and make them useful for practical military and civilian applications. This article presents the results obtained with a maximally passive system of obstacle detection for ground-based vehicles and rotorcraft. Automatic detection of these obstacles and the necessary guidance and control actions triggered by such detection will facilitate autonomous vehicle navigation. Our approach to obstacle detection employs motion analysis of imagery collected by a passive sensor during vehicle travel to generate range measurements to world points within the field of view of the sensor. The approach makes use of INS data and scene analysis results to improve interest point selection, the matching of the interest points, and the subsequent motion-based range computations, tracking, and obstacle detection. In this article, we concentrate on the results obtained using lab and outdoor imagery. The range measurements that are made by INS integrated motion analysis are compared to a limited amount of ground truth that is available. © 1992 John Wiley & Sons, Inc.     

A hybrid approach for autonomous navigation of mobile robots in partially-known environments

We propose a hybrid approach specifically adapted to deal with the autonomous-navigation problem of a mobile robot which is subjected to perform an emergency task in a partially-known environment. Such a navigation problem requires a method that is able to yield a fast execution time, under constraints on the capacity of the robot and on known/unknown obstacles, and that is sufficiently flexible to deal with errors in the known parts of the environment (unexpected obstacles). Our proposal includes an off-line task-independent preprocessing phase, which is applied just once for a given robot in a given environment. Its purpose is to build, within the known zones, a roadmap of near-time-optimal reference trajectories. The actual execution of the task is an online process that combines reactive navigation with trajectory tracking and that includes smooth transitions between these two modes of navigation. Controllers used are fuzzy-inference systems. Both simulation and experimental results are presented to test the performance of the proposed hybrid approach. Obtained results demonstrate the ability of our proposal to handle unexpected obstacles and to accomplish navigation tasks in relatively complex environments. The results also show that, thanks to its time-optimal-trajectory planning, our proposal is well adapted to emergency tasks as it is able to achieve shorter execution times, compared to other waypoint-navigation methods that rely on optimal-path planning.     

Reinforcement learning-based group navigation approach for multiple autonomous robotic systems

In several complex applications, the use of multiple autonomous robotic systems (ARS) becomes necessary to achieve different tasks, such as foraging and transport of heavy and large objects, with less cost and more efficiency. They have to achieve a high level of flexibility, adaptability and efficiency in real environments. In this paper, a reinforcement learning (RL)-based group navigation approach for multiple ARS is suggested. Indeed, the robots must have the ability to form geometric figures and navigate without collisions while maintaining the formation. Thus, each robot must learn how to take its place in the formation, and avoid obstacles and other ARS from its interaction with the environment. This approach must provide ARS with the capability to acquire the group navigation approach among several ARS from elementary behaviors by learning with trialand-error search. Then, simulation results display the ability of the suggested approach to provide ARS with capability to navigate in a group formation in dynamic environments. With its cooperative behavior, this approach makes ARS able to work together to successfully fulfill the desired task.     

Adaptive navigation for autonomous robots

In many robotic exploration missions, robots have to learn specific policies that allow them to: (i) select high level goals (e.g., identify specific destinations), (ii) navigate (reach those destinations), (iii) and adapt to their environment (e.g., modify their behavior based on changing environmental conditions). Furthermore, those policies must be robust to signal noise or unexpected situations, scalable to more complex environments, and account for the physical limitations of the robots (e.g., limited battery power and computational power).In this paper we evaluate reactive and learning navigation algorithms for exploration robots that must avoid obstacles and reach specific destinations in limited time and with limited observations. Our results show that neuro-evolutionary algorithms with well-designed evaluation functions can produce up to 50% better performance than reactive algorithms in complex domains where the robot’s goals are to select paths that lead to seek specific destinations while avoiding obstacles, particularly when facing significant sensor and actuator signal noise.     

A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.     

A high integrity navigation architecture for outdoor autonomous vehicles

This paper describes the design of a high integrity navigation system for use in large autonomous mobile vehicles. A frequency domain model of sensor contributions to navigation system performance is used to study the performance of a conventional navigation loop. On the basis of this, a new navigation system structure is introduced which is capable of detecting faults in any combination of navigation sensors. A decentralised architecture is also presented for the fusion of information from different asynchronous sources. An example implementation of these principles is described which employs a twin GPS/inertial navigation system and a millimeter wave radar/encoder navigation loop.     

A new approach to vision-based unsupervised learning of unexplored indoor environment for autonomous land vehicle navigation

A vision-based approach to unsupervised learning of the indoor environment for autonomous land vehicle (ALV) navigation is proposed. The ALV may, without human's involvement, self-navigate systematically in an unexplored closed environment, collect the information of the environment features, and then build a top-view map of the environment for later planned navigation or other applications. The learning system consists of three subsystems: a feature location subsystem, a model management subsystem, and an environment exploration subsystem. The feature location subsystem processes input images, and calculates the locations of the local features and the ALV by model matching techniques. To facilitate feature collection, two laser markers are mounted on the vehicle which project laser light on the corridor walls to form easily detectable line and corner features. The model management subsystem attaches the local model into a global one by merging matched corner pairs as well as line segment pairs. The environment exploration subsystem guides the ALV to explore the entire navigation environment by using the information of the learned model and the current ALV location. The guidance scheme is based on the use of a pushdown transducer derived from automata theory. A prototype learning system was implemented on a real vehicle, and simulations and experimental results in real environments show the feasibility of the proposed approach.     

Parallelized egocentric fields for autonomous navigation

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent’s local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent’s steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations.     

Traversability analysis for autonomous driving in complex environment: A LiDAR-based terrain modeling approach

For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR-based terrain modeling approach, which could output stable, complete, and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multiframe information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial-temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real-time and outperforms state-of-the-art approaches.     

A micro-rover navigation and control system for autonomous planetary exploration

This paper describes an end-to-end control system for autonomous navigation of a small vehicle at a remote place, e.g. in space for planetary exploration. Due to a realistic background of this study the proposed method has to deal with limited knowledge about the environment as well as limited system resources and operational boundary conditions, especially a very large time delay in the communication between the ground control station and the space segment. To overcome these constraints the remote system has to act in a very autonomous way. Ground support minimizes the computational load of the remote system. High-level information interchange reduces the communication bandwidth requirements.     

A new autonomous celestial navigation method for the lunar rover

A secure and autonomous navigation system is needed for the lunar rover in future lunar missions in case of emergencies. Celestial navigation is a very attractive solution for long distance navigation on the Moon without the need of ground navigation aids. It only uses star altitudes, which are measured by a high accuracy star sensor and inertial measurement unit (IMU) to estimate the position of the rover. The navigational accuracy of this method depends largely on the accuracy of measurements, so the measurement errors have a great impact on the navigational performance. A new autonomous celestial navigation method for the lunar rover is presented in this paper, which uses the augmented state unscented particle filter (ASUPF) to deal with the systematic error and random error in the measurements. The validity and feasibility of this new method is tested and examined by the hardware-in-loop test. A position estimation error within 60 m is obtained. Compared to the conventional method, this method shows better navigation performance and higher adaptability to these measurement errors.     

Real-time hierarchical POMDPs for autonomous robot navigation

This paper proposes a new hierarchical formulation of POMDPs for autonomous robot navigation that can be solved in real-time, and is memory efficient. It will be referred to in this paper as the Robot Navigation–Hierarchical POMDP (RN-HPOMDP). The RN-HPOMDP is utilized as a unified framework for autonomous robot navigation in dynamic environments. As such, it is used for localization, planning and local obstacle avoidance. Hence, the RN-HPOMDP decides at each time step the actions the robot should execute, without the intervention of any other external module for obstacle avoidance or localization. Our approach employs state space and action space hierarchy, and can effectively model large environments at a fine resolution. Finally, the notion of the reference POMDP is introduced. The latter holds all the information regarding motion and sensor uncertainty, which makes the proposed hierarchical structure memory efficient and enables fast learning. The RN-HPOMDP has been experimentally validated in real dynamic environments.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.     

A quantized approach for occupancy grids for autonomous vehicles: Q-Trees

Map Representation of the Real World is an essential step in robotics for using the mathematical methods in an abstract way. In addition to grid-based and topological studies on this area, this paper uses both approaches representing the environment with independent graph trees for all axes. We are proposing a methodology that allows us to substitute point clouds on grids in an efficient way and can be easily adapted to multi-system approaches even in real time with low-level computational systems.     

非合作航天器自主相对导航研究综述

非合作航天器自主相对导航作为与非合作航天器实现空间交会对接过程中的关键技术,是在轨服务技术的重点发展方向之一,其研究具有重要的理论价值与工程意义.针对在轨服务任务对于自主相对精确导航的需求,本文对发展非合作航天器自主相对导航技术的必要性进行了阐述.首先总结了非合作航天器自主相对导航技术的内涵与研究现状;随后分析梳理了非合作航天器自主相对导航过程涉及到的光学敏感器、位姿测量、导航滤波器以及地面实验等关键技术.最后根据研究现状和关键技术的分析指出了非合作航天器自主相对导航目前存在的主要问题并给出后续发展的建议.     

Seafloor map generation for autonomous underwater vehicle navigation

Elevation map generation is an essential component of any autonomous underwater vehicle designed to navigate close to the seafloor because elevation maps are used for obstacle avoidance, path planning and self localization. We present an algorithm for the reconstruction of elevation maps of the seafloor from side-scan sonar backscatter images and sparse bathymetric points co-registered within the image. Given the trajectory for the underwater vehicle, the reconstruction is corrected for the attitude of the side-scan sonar during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system using vehicle attitude and performing the reconstruction again. We demonstrate the effectiveness of our algorithm on synthetic and real data sets. Our algorithm is shown to decrease the average elevation error when compared to real bathymetry from 4.6 meters for the initial surface estimate to 1.6 meters for the final surface estimate from a survey taken of the Juan de Fuca Ridge.     

Appearance-based navigation and homing for autonomous mobile robot

In this paper, we develop an algorithm for navigating a mobile robot using the visual potential. The visual potential is computed from an image sequence and optical flow computed from successive images captured by a camera mounted on the robot, that is, the visual potential for navigation is computed from appearances of the workspace observed as an image sequence. The direction to the destination is provided at the initial position of the robot. The robot dynamically selects a local pathway to the destination without collision with obstacles and without any knowledge of the robot workspace. Furthermore, the guidance algorithm to destination allows the mobile robot to return from the destination to the initial position. We present the experimental results of navigation and homing in synthetic and real environments.     

IRA-WDS: A GIS-based risk analysis tool for water distribution systems

This paper presents the development of a new software tool IRA-WDS. This GIS-based software predicts the risks associated with contaminated water entering water distribution systems from surrounding foul water bodies such as sewers, drains and ditches. Intermittent water distribution systems are common in developing countries and these systems are prone to contamination when empty. During the non-supply hours contaminants from pollution sources such as sewers, open drains and surface water bodies enter into the water distribution pipes through leaks and cracks. Currently there are no tools available to help engineers identify the risks associated with contaminant intrusion into intermittent water distribution systems. Hence it is anticipated that IRA-WDS will find wide application in developing countries. The paper summarises the details of the mathematical models that form the basis of IRA-WDS. It also describes the software architecture, the main modules, and the integration with GIS using a tight coupling approach. A powerful GUI has been developed that enables data for the models to be retrieved from the spatial databases and the outputs to be converted into tables and thematic maps. This is achieved seamlessly through DLL calling functions within the GIS. This paper demonstrates the application of the software to a real case study in India. The outputs from IRA-WDS are risk maps showing the risk of contaminant intrusion into various parts of the water distribution system. The outputs also give an understanding of the main factors that contribute to the risk.