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An underactuated nonholonomic Dubins-vehicle-like robot with a lower-limited turning radius travels with a constant speed in a plane, which hosts unknown complex objects. In its local frame, the robot has sensorial access only to the part of the scene that is within a finite zone of visibility and in direct line of sight. It is required to approach and then circumnavigate the objects, with maintaining a given distance to the currently nearest of them, so that the ideal targeted path is the equidistant curve of the set of the objects. The focus is on the case where this curve cannot be perfectly traced due to excessive contortions and singularities. So the objective is restated as that of automatically finding, approaching and repeatedly tracing an approximation of the equidistant curve that is the best among those trackable by the robot with respecting safety concerns. Though pre-computation of this approximation is a hard problem of computational geometry, we show that autonomously seeking and tracking this approximation can be performed on-the-fly at a miserable computational cost via implementation of the proposed navigation law. This law is hybrid and operates in only a few discrete modes; within any mode, it is reactive, i.e., it directly converts the current observation into the current control. Nonlocal convergence of this law is justified by mathematically rigorous results and is confirmed by computer simulations and real-world experiments. • Underactuated nonholonomic Dubins-vehicle-like robot with a lower-limited turning radius. • Circumnavigation of unknown objects, with desirably maintaining a given distance to them. • Equidistant curve that is untrackable due to excessive contortion and singularities. • On-the-fly generation of the optimal trackable approximation at a miserable computational cost. • Computationally cheap (reactive) controller that simultaneously generates the path and ensures its tracking. [ABSTRACT FROM AUTHOR]

The article presents a novel idea to construct a smooth navigation function for a wheeled robot based on grid-based search, that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predictive control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot. • Navigation function created from the grid-based search enables real-time replanning. • Continuous navigation function is obtained by interpolating discrete grid-based costs. • MPC-based navigation takes velocity and acceleration constraints into account. • MPC with the control sequence generation scheme allows fast on-line computation. • The navigation approach guarantees closed-loop convergence for unicycle-type robots. [ABSTRACT FROM AUTHOR]

We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state , taking into account the robot actuation capabilities in addition to the robot–obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation. • Robot navigation among obstacles at high-speed using Model Predictive Control. • Dynamics-aware collision avoidance enhances robot safety. • Dynamics-aware collision avoidance outperforms its distance-based counterpart. • Integration of velocity fields aids the robot in circumventing obstacles. [ABSTRACT FROM AUTHOR]

Swarm robotics involves the study of the behavior of a set of robots in carrying out collective tasks, such as alignment, navigation and formation. During cooperative tasks execution, the communication among robots can contribute to successfully performing tasks through an efficient messages exchange. This paper proposes a communication strategy for swarm robots, the so-called Double-Wave Swarm, aiming at alignment and navigation tasks. The Double-Wave Swarm is an improvement of a prior Wave Swarm communication approach that uses the concept of wave propagation for message exchange between neighbors. Double-Wave Swarm provides better communication network connectivity when compared to the former approach. Experiments with the robot simulator CoppeliaSim (V-REP) validate the proposed communication and highlight its efficiency and robustness while running alignment and navigation tasks. Also, the Double-Wave Swarm proved to be superior during swarm formation navigation into environments with obstacles in different scenarios if compared with Wave Swarm. • DWS is an improvement on an earlier WS communication approach. • DWS increases the connectivity between robots when compared to WS. • Sharing information through DWS helps in locating and orienting swarm robots. • DWS is a robust and effective approach to in formation navigation of multi-robots. [ABSTRACT FROM AUTHOR]

The Robot Operating System 2 (ROS 2) is rapidly impacting the intelligent machines sector — on space missions, large agriculture equipment, multi-robot fleets, and more. Its success derives from its focused design and improved capabilities targeting product-grade and modern robotic systems. Following ROS 2's example, the mobile robotics ecosystem has been fully redesigned based on the transformed needs of modern robots and is experiencing active development not seen since its inception. This paper comes from the desks of the key ROS Navigation maintainers to review and analyze the state of the art of robotics navigation in ROS 2. This includes new systems without parallel in ROS 1 or other similar mobile robotics frameworks. We discuss current research products and historically robust methods that provide differing behaviors and support for most every robot type. This survey consists of overviews, comparisons, and expert insights organized by the fundamental problems in the field. Some of these implementations have yet to be described in literature and many have not been benchmarked relative to others. We end by providing a glimpse into the future of the ROS 2 mobile robotics ecosystem. • ROS 2's Nav2 has a number of new algorithms and capabilities suitable for a large number of applications. • This survey analyzes ROS 2 algorithm's capabilities and benchmarks performance. • Explains systems provided to build robust autonomous navigation systems in dynamic environments. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new approach to view-based localization and navigation in outdoor environments, which are indispensable functions for mobile robots. Several approaches have been proposed for autonomous navigation. GPS-based systems are widely used especially in the case of automobiles, however, they can be unreliable or non-operational near tall buildings. Localization with a precise 3D digital map of the target environment also enables mobile robots equipped with range sensors to estimate accurate poses, but maintaining a large-scale outdoor map is often costly. We have therefore developed a novel view-based localization method SeqSLAM++ by extending the conventional SeqSLAM in order not only to robustly estimate the robot position comparing image sequences but also to cope with changes in a robot's heading and speed as well as view changes using wide-angle images and a Markov localization scheme. According to the direction to move provided by the SeqSLAM++, the local-level path planner navigates the robot by setting subgoals repeatedly considering the structure of the surrounding environment using a 3D LiDAR. The entire navigation system has been implemented in the ROS framework, and the effectiveness and accuracy of the proposed method was evaluated through off-line/on-line navigation experiments. [ABSTRACT FROM AUTHOR]

Abstract Despite the comprehensive development in the field of navigation algorithms for mobile robots, the research on performance metrics and evaluation procedures for making standardized quantitative comparison between different algorithms has gained attention only recently. This work attempts to contribute with such effort by introducing a performance metric for the assessment of collision avoidance algorithms for mobile robots. The proposed metric comprehensively evaluates the actions taken by the objects and their consequences, in a given scenario of any given collision avoidance algorithm, based on the concept of probability of collision. The contribution of the paper encompasses the definition of the metric, the methodology to estimate the metric, and the framework to apply the metric for any given scenario. Experiments and numerical simulations are conducted to validate and demonstrate the effectiveness of the proposed metric in performance evaluation and comparison among different collision avoidance algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent image-based robotic systems use predicted future state images to control robots. Therefore, the prediction accuracy of the future state image affects the performance of the robot. To predict images, most previous studies assume that the camera captures the entire scene and that the environment is static. However, in real robot applications, these assumptions do not always hold. For example, if a camera is attached to a mobile robot, its view changes from time to time. In this study, we analyzed the relationship between the performance of the image prediction model and the robot's behavior, controlled by an image-based navigation algorithm. Through mobile robot navigation experiments using front-faced and omni-directional cameras, we discussed the capabilities of the image prediction models and demonstrated their performance when applied to the image-based navigation algorithm. Moreover, to adapt to the dynamic changes in the environment, we studied the effectiveness of directing the camera to the ceiling. We showed that robust navigation can be achieved without using images from cameras directed toward the front or the floor, because these views can be disturbed by moving objects in a dynamic environment. • Empirical evaluation and comparison of image prediction models on a mobile robot. • Action-conditioned LSTM-based prediction model performs well across camera types. • Omni-directional camera is effective for image prediction models. • Omni-directional camera is effective for image-based mobile robot navigation tasks. [ABSTRACT FROM AUTHOR]

Despite the increasing popularity of the cloud robotics paradigm, the literature on the field still lacks comprehensive analysis on several aspects of the technology. Therefore, the adoption of common standards and frameworks is fundamental for developing the field and allowing practical works to be reproduced and compared. This work presents a ROS-based open framework for robot-cloud communication, easing the integration of robotics and remote cloud platforms, and discusses the implementation of the overall cloud robotics stack over open source software and commercial off-the-shelf devices. Additionally, we present two practical implementations in which most of the computation is carried out remotely and perform a series of experiments to demonstrate our technique. Our results indicate that task times can be reduced up to 15% when using remote cloud platforms even under 150 ms average communication latency over the public Internet while observing figures as low as 2% on throughput loss in sensor data transmission. In general, such results point to the feasibility of the presented approach in different classes of applications, even under non-ideal network and cloud settings. • Introduce an open framework based on ROS for robot-cloud communication. • Our framework requires no external network configuration for use over the Internet. • Propose a repeatable methodology for implementing cloud robotics. • Our work is validated considering different applications and cloud platforms setups. • Cloud computation can outperform embedded computation even in complex networks. [ABSTRACT FROM AUTHOR]

This paper proposes a new pose estimation algorithm in the framework of robotic navigation problems. The algorithm gives the mobile robot (MR) pose on the basis of the difference between the MR desired pose and the MR current pose. In this regard the MR sensor readings and the readings of a virtual sensor are employed. The algorithm is advantageous in comparison with other pose estimation algorithms including those based on classical filter approaches because of the small computation time. Simulation and real-world experimental results are included to illustrate the effectiveness of the pose estimation algorithm and its potential for integration in MR control structures and algorithms. [ABSTRACT FROM AUTHOR]

Vector field SLAM is a framework for localizing a mobile robot in an unknown environment by learning the spatial distribution of continuous signals such as those emitted by WiFi or active beacons. In our previous work we showed that this approach is capable of keeping a robot localized in small to medium sized areas, e.g. in a living room, where four continuous signals of an active beacon are measured (Gutmann et al., 2012). In this article we extend the method to larger environments up to the size of a complete home by deploying more signal sources for covering the expanded area. We first analyze the complexity of vector field SLAM with respect to area size and number of signals and then describe an approximation that divides the localization map into decoupled sub-maps to keep memory and run-time requirements low. We also describe a method for re-localizing the robot in a vector field previously mapped. This enables a robot to resume its navigation after it has been kidnapped or paused and resumed. The re-localization method is evaluated in a standard test environment and shows an average position accuracy of 10 to 35 cm with a localization success rate of 96 to 99%. Additional experimental results from running the system in houses of up to 125 m2 demonstrate the performance of our approach. The presented methods are suitable for commercial low-cost products including robots for autonomous and systematic floor cleaning. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a generic hybrid monitoring approach, which allows the detection of inconsistencies in the navigation of autonomous mobile robots using online-generated models. A mission on the context of the navigation corresponds to an autonomous navigation from a start to an end mission point. The operator defines this mission by selecting a final goal point. Based on this selection the monitoring models for the current mission must be generated online. The originalities of this work are (i) the association of classic state estimation based on a particle filter with a special class of Petri net in order to deliver an estimation of the next robot state (position) as well as the environment state (graph nodes) and to use both pieces of information to distinguish between external noise influences, internal component faults and global behaviour inconsistency (ii) the integration of the geometrical and the logical environment representation into the monitor model (iii) the online generation of the corresponding monitoring model for the present mission trajectory while the system is running. The model takes simultaneously into account the uncertainty of the robot and of the environment through a unified modelling of both. To show the feasibility of the approach we apply it to an intelligent wheelchair (IWC) as a special type of autonomous mobile robot. [Copyright &y& Elsevier]

Abstract: This paper proposes a solution to the problem of mobile robotic localization using visual indoor image sequences with a biologically inspired spatio-temporal neural network approach. The system contains three major subsystems: a feature extraction module, a scene quantization module and a spatio-temporal long-term memory (LTM) module. During learning, the scene quantization module clusters the visual images set into scene tokens. A -Iteration Fast Learning Artificial Neural Network (KFLANN) is employed as the core unit of the quantization module. The KFLANN network is driven by intrinsic statistics of the data stream and therefore does not require the number of clusters to be predefined. In addition, the KFLANN performance is less sensitive to data presentation ordering compared to popular clustering methods such as -means, and can therefore produce a consistent number of stable centroids. Using scene tokens, the topological structure of the environment can be composed into sequences of tokens. These sequences are then learnt and stored in memory units in an LTM architecture, which is able to continuously and robustly recognize the visual input stream. The design of memory units addresses two critical problems in spatio-temporal learning, namely error tolerance and memory forgetting. The primary objective of this work is to explore the synergy between the strength of KFLANN and LTM models to address the visual topological localization problem. We demonstrate the efficiency and efficacy of the proposed framework on the challenging COsy Localization Dataset. [Copyright &y& Elsevier]

Abstract: We present a sliding mode based strategy for a unicycle-like robot navigation and guidance. The proposed navigation law is applied to the problems of patrolling the border of a moving and deforming domain and reaching a target through a dynamic environment cluttered with moving obstacles. Mathematically rigorous analysis of the proposed approach is provided. The convergence and performance of the algorithm is demonstrated via experiments with real robots and extensive computer simulation. [Copyright &y& Elsevier]

Abstract: The paper focuses on the navigation subsystem of a mobile robot which operates in human environments to carry out different tasks, such as transporting waste in hospitals or escorting people in exhibitions. The paper describes a hybrid approach (Roaming Trails), which integrates a priori knowledge of the environment with local perceptions in order to carry out the assigned tasks efficiently and safely: that is, by guaranteeing that the robot can never be trapped in deadlocks even when operating within a partially unknown dynamic environment. The article includes a discussion about the properties of the approach, as well as experimental results recorded during real-world experiments. [Copyright &y& Elsevier]

Abstract: This paper describes a method of robustly modeling road boundaries on-line for autonomous navigation. Since sensory evidence for road boundaries might change from place to place, we cannot depend on a single cue but have to use multiple sensory features. It is also necessary to cope with various road shapes and road type changes. These requirements are naturally met in the proposed particle filter-based method, which makes use of multiple features with the corresponding likelihood functions and keeps multiple road hypotheses as particles. The proposed method has been successfully applied to various road scenes with cameras and a laser range finder. To show that the proposed method is applicable to other sensors, preliminary results of using stereo instead of the laser range finder are also described. [Copyright &y& Elsevier]

Abstract: Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability. [Copyright &y& Elsevier]

Abstract: For a mobile robot to operate autonomously in real-world environments, it must have an effective control system and a navigation system capable of providing robust localization, path planning and path execution. In this paper we describe work investigating synergies between mapping and control systems. We have integrated development of a control system for navigating mobile robots and a robot SLAM system. The control system is hybrid in nature and tightly coupled with the SLAM system; it uses a combination of high and low level deliberative and reactive control processes to perform obstacle avoidance, exploration, global navigation and recharging, and draws upon the map learning and localization capabilities of the SLAM system. The effectiveness of this hybrid, multi-level approach was evaluated in the context of a delivery robot scenario. Over a period of two weeks the robot performed 1143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), travelled a total distance of more than 40 km, and recharged autonomously a total of 23 times. In this paper we describe the combined control and SLAM system and discuss insights gained from its successful application in a real-world context. [Copyright &y& Elsevier]

Abstract: We consider the problems of a wheeled mobile robot navigation and guidance towards an unknown stationary or maneuvering target using range-only measurements. We propose and study several methods for navigation and guidance termed Equiangular Navigation Guidance (ENG) laws. We give mathematically rigorous analysis of the proposed guidance laws. The performance is confirmed with computer simulations and experiments with ActivMedia Pioneer 3-DX wheeled robots. [Copyright &y& Elsevier]

Abstract: This paper presents empirical results of the effect of the global position information on the performance of the modified local navigation algorithm (MLNA) for unknown world exploration. The results show that global position information enables the algorithm to maintain 100% success rate irrespective of initial robot position, movement speed, and environment complexity. Most mobile robot systems accrue an odometry error while moving, and hence need to use external sensors to recalibrate their position on an ongoing basis. We deal with position calibration to compensate the odometry error using the global position information provided by the Teleworkbench, which is a teleoperated platform and test bed for managing experiments using mini-robots. In this paper we demonstrate how we incorporate the global position information during and after the experiments. [Copyright &y& Elsevier]

Abstract: Localization is a fundamental operation for the navigation of mobile robots. The standard localization algorithms fuse external measurements of the environment with the odometric evolution of the robot pose to obtain its optimal estimation. In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time. The presented method is based on an Extended Kalman Filter (EKF) with a state-vector composed of the external angular measurements. This algorithm keeps track of the angles between actual measurements from robot odometric information. This continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion. The article reports experimental results that show the localization accuracy obtained by means of the presented approach. These results are compared to the ones obtained applying the EKF algorithm with the standard pose state-vector. For the experiments, an omnidirectional robotic platform with omnidirectional wheels is used. [Copyright &y& Elsevier]

Abstract: Reactive-based approaches are widely used in autonomous navigation. However, in complex unknown environments, pure reactive-based navigation still poses a few challenges since it can be easily trapped by a local minimum and may produce some extra manoeuvres. This paper presents the design of a reactive-based approach for navigation in complex and unknown environments called sub goal seeking, in which depth point maps of the environment are analysed to extract free spaces around the robot. These spaces are then evaluated the one that is most likely to lead to the final goal is chosen as a sub goal. The robot then drives towards these sub goals, instead of the final goal until it is visible. By analysing the environmental structure, dead-ends within robot sensory range are able to be detected thus reducing the chance of being trapped and also reducing unnecessary manoeuvres. This paper also evaluates the performance of the sub goal seeking approach using three criteria, goal achievable ability, safety and maneuvering through extensive simulation and real mobile robot experiments. [Copyright &y& Elsevier]

This paper proposes a geometrical feature detection system which is to be used with conventional 2D laser range finders. It consists of three main modules: data acquisition and pre-processing, segmentation and landmark extraction and characterisation. The novelty of this system is a new approach for laser data segmentation based on an adaptive curvature estimation. Contrary to other works, this approach divides the laser scan into line and curve segments. Then, these items are used to directly extract several types of landmarks associated with real and virtual features of the environment (corners, center of tree-like objects, line segments and edges). For each landmark, characterisation provides not only the parameter vector, but also complete statistical information, suitable to be used in a localization and mapping algorithm. Experimental results show that the proposed approach is efficient to detect landmarks for structured and semi-structured environments. [Copyright &y& Elsevier]

We present a new control law for the problem of docking a wheeled robot to a target at a certain location with a desired heading. Recent research into insect navigation has inspired a solution which uses only one video camera. The control law is of the “behavioral” type in that all control actions are based on immediate visual information. Docking success under certain conditions is proved mathematically and simulation studies show the control law to be robust to camera intrinsic parameter errors. Experiments were performed for verification of the control law. [Copyright &y& Elsevier]

In this paper, we present a method that uses panoramic images to perform long-range navigation as a succession of short-range homing steps along a route specified by appearances of the environment of the robot along the route. Our method is different from others in that it does not extract any features from the images and only performs simple image processing operations. The method does only make weak assumptions about the surroundings of the robot, assumptions that are discussed. Furthermore, the method uses a technique borrowed from computer graphics to simulate the effect in the images of short translations of the robot to compute local motion parameters. Finally, the proposed method shows that it is possible to perform navigation without explicitly knowing where the destination is nor where the robot currently is. Results in our Lab are presented that show the performance of the proposed system. [Copyright &y& Elsevier]

SAURON (Surveillance AUtonomous Robots Over Network) is a research project focused on the use of autonomous patrolling robots for surveillance purposes. This paper presents the structure of SAURON localization module AMIRoLoS (Active Marker Internet-based Robot Localization System). It provides on demand the robot position in indoor and outdoor environments using active markers and commercial off-the-shelf web-cams. The system has a client-server architecture, and is now part of a message-oriented middleware that is under development at the University of Brescia. The functionality of the localization module has been demonstrated with real-world experiments. [Copyright &y& Elsevier]

Abstract: In this paper, we study coordinated motion in a swarm robotic system, called a swarm-bot. A swarm-bot is a self-assembling and self-organising artifact, composed of a swarm of s-bots, mobile robots with the ability to connect to and disconnect from each other. The swarm-bot concept is particularly suited for tasks that require all-terrain navigation abilities, such as space exploration or rescue in collapsed buildings. As a first step toward the development of more complex control strategies, we investigate the case in which a swarm-bot has to explore an arena while avoiding falling into holes. In such a scenario, individual s-bots have sensory–motor limitations that prevent them navigating efficiently. These limitations can be overcome if the s-bots are made to cooperate. In particular, we exploit the s-bots’ ability to physically connect to each other. In order to synthesise the s-bots’ controller, we rely on artificial evolution, which we show to be a powerful tool for the production of simple and effective solutions to the hole avoidance task. [Copyright &y& Elsevier]

Abstract: The localization capability of a mobile robot is central to basic navigation and map building tasks. We describe a probabilistic environment model which facilitates global localization scheme by means of location recognition. In the exploration stage the environment is partitioned into locations, each characterized by a set of scale-invariant keypoints. The descriptors associated with these keypoints can be robustly matched despite changes in contrast, scale and viewpoint. We demonstrate the efficacy of these features for location recognition, where given a new view the most likely location from which this view came from is determined. The misclassifications due to dynamic changes in the environment or inherent appearance ambiguities are overcome by exploiting location neighborhood relationships captured by a Hidden Markov Model. We report the recognition performance of this approach in an indoor environment consisting of eighteen locations and discuss the suitability of this approach for a more general class of recognition problems. Once the most likely location has been determined we demonstrate how to robustly compute the relative pose between the representative view and the current view. [Copyright &y& Elsevier]

This paper presents the theoretical development of a complete navigation problem of an autonomous mobile robot. The situation for which the vehicle tries to reach the endpoint is treated using a fuzzy logic controller. The problem of extracting the optimized IF–THEN rule base is solved using an evolutionary algorithm. A new approach based on fuzzy concepts is presented in this paper to avoid any collision with the surrounding environment when this latter becomes relatively complex. Simulation results show that the designed fuzzy controller achieves effectively any movement control of the vehicle from its current position to its end motion and without any collision. [Copyright &y& Elsevier]

Titan is a mobile robot built for outdoor navigation research. In this paper, we report on research into navigation along concrete paths through a park using the edge of the path as a continuous landmark. Titan measures its location relative to the path edge with a continuous transmission frequency modulated (CTFM) ultrasonic sensor and steers to follow a trajectory relative to the edge. When it loses the edge, it commences a recovery strategy. [Copyright &y& Elsevier]

Robust navigation for mobile robots over long distances requires an accurate method for tracking the robot position in the environment. Promising techniques for position estimation by determining the camera ego-motion from monocular or stereo sequences have been previously described. However, long-distance navigation requires both a high level of robustness and a low rate of error growth. In this paper, we describe a methodology for long-distance rover navigation that meets these goals using robust estimation of ego-motion. The basic method is a maximum-likelihood ego-motion algorithm that models the error in stereo matching as a normal distribution elongated along the (parallel) camera viewing axes. Several mechanisms are described for improving navigation robustness in the context of this methodology. In addition, we show that a system based on only camera ego-motion estimates will accumulate errors with super-linear growth in the distance traveled, owing to increasing orientation errors. When an absolute orientation sensor is incorporated, the error growth can be reduced to a linear function of the distance traveled. We have tested these techniques using both extensive simulation and hundreds of real rover images and have achieved a low, linear rate of error growth. This method has been implemented to run on-board a prototype Mars rover. [Copyright &y& Elsevier]

A mobile robot should be designed to navigate with collision avoidance capability in the real world, flexibly coping with the changing environment. In this paper, a novel limit-cycle navigation method is proposed for a fast mobile robot using the limit-cycle characteristics of a 2nd-order nonlinear function. It can be applied to the robot operating in a dynamically changing environment, such as in a robot soccer system. By adjusting the radius of the motion circle and the direction of obstacle avoidance, the navigation method proposed enables a robot to maneuver smoothly towards any desired destination. Simulations and real experiments using a robot soccer system demonstrate the merits and practical applicability of the proposed method. [Copyright &y& Elsevier]

Portable mobile robots, in the size class of 20 kg or less, could be extremely valuable as autonomous reconnaissance platforms in urban hostage situations and disaster relief. We have developed a prototype urban robot on a novel chassis with articulated tracks that enable stair climbing and scrambling over rubble. Autonomous navigation capabilities of the robot include stereo vision-based obstacle avoidance, visual serving to user-designated goals, and autonomous vision-guided stair climbing, The system was demonstrated in an urban reconnaissance mission scenario at Fort Sam Houston in October 1999. A two-axis scanning laser range-finder has been developed and integrated into the robot for indoor mapping and position estimation. This paper describes the robot, its performance in field trials, and some of the technical challenges that remain to enable fieldable urban reconnaissance robots. [ABSTRACT FROM AUTHOR]

This work presents real time implementation algorithms of Simultaneous Localization and Map Building (SLAM) with emphasis to outdoor land vehicle applications in large environments. It presents the problematic of outdoors navigation in areas with combination of feature and featureless regions. The aspect of feature detection and validation is investigated to reliably detect the predominant features in the environment. Aided SLAM algorithms are presented that incorporate absolute information in a consistent manner. The SLAM implementation uses the compressed filter algorithm to maintain the map with a cost proportional to number of landmarks in the local area. The information gathered in the local area requires a full SLAM update when the vehicle leaves the local area. Algorithms to reduce the full update computational cost are also presented. Finally, experimental results obtained with a standard vehicle running in unstructured outdoor environment are presented. [ABSTRACT FROM AUTHOR]

Presents an automatic landmark selection algorithm that allows a mobile robot to select conspicuous landmarks from a continuous stream of sensory perceptions. Absence of pre-installed knowledge or human intervention during the selection process; Use of the algorithm to make mapping mechanisms more efficient and reliable.

This paper deals with an innovative autonomous bus navigation and parking system in a bus depot, in order to optimize their movements in a confined area. The kinematic model of the vehicle is defined. Considering its dimensions and weight as well as the centimetric accuracy required, a predictive controller is designed, based on its model linearized around the changing path curvature value, to perform accurate curved paths tracking with a limited tracking error guaranteed by the consideration of a constraint. This controller and additional sliding observers are designed according to the distance traveled, allowing maneuvers to be performed at any forward or backward speed with constant accuracy. In addition, these observers are not affected by path tracking errors. The implementation on an industrial vehicle, operated under realistic conditions, demonstrates the performance and robustness of this navigation system. • The autonomous navigation and parking for buses storage in a bus center is proposed. • The predictive steer control is expressed as a constrained linear quadratic problem. • The controller and sliding observers are designed according to the distance traveled. • The tracking of curved paths is accurately performed at forward or backward speed. • Results using an industrial vehicle validate the solution until 8 km/h. [ABSTRACT FROM AUTHOR]

Proposes a methodology for image-based navigation using a self-organized visual representation of the environment. Presentation of VISMOB system architecture for visually guided mobile robots; Self-organization of perceptual capabilities; Conclusion.

Focuses on the ARK mobile robot project, which has designed and implemented a series of mobile robots capable of navigating within industrial environments without relying on artificial landmarks or beacons. Roles of mobile robots; Tasks of the ARK robots; Its prototypes; ARK sensors; ARK software architecture; Discussion.

Addresses the problem of self-localisation in autonomous mobile robot navigation, the task of identifying places after previous exploration and map building by the robot. Presentation of a novel localisation system which accumulates both exteroceptive and proprioceptive sensory evidence; Procedure for evaluating different localisation systems on the same robot; Results of the experiment; Conclusions.

The collision-free navigation of a mobile robot in clutter environments is challenging. Global Positioning System (GPS) and adaptive neuro-fuzzy inference system (ANFIS) are well-known techniques widely used for navigation and control, respectively. This paper proposes a hybrid GPS-ANFIS based method for collision-free navigation of autonomous mobile robots. The GPS-based controller keeps the navigation direction of the robot toward the static or dynamic target. It uses the coordinates received from the two GPS modules on the edges of the longitudinal axis of the robot all together with the coordinates of the target to divert it from the current path making a certain angle towards the target. The performance of the proposed method in navigating a mobile robot in clutter environments and its effectiveness in comparison with the other collision-free navigation methods has been evaluated through simulations. The evaluation criteria are on the basis of the obstacle avoidance behavior and the length of the discovered collision-free path by the robot. The results have shown that our hybrid GPS-ANFIS method navigates the robot toward the goal via a shorter path while avoiding the obstacles. [ABSTRACT FROM AUTHOR]