Robots In Outdoor Environments Research Articles

In situ skid estimation for mobile robots in outdoor environments

AbstractSkidding is a surface hazard for mobile robots' navigation and traction control systems when operating in outdoor environments and uneven terrains due to the wheel–terrain interaction. It could lead to large trajectory tracking errors, losing the robot's controllability, and mission failure occurring. Despite research in this field, the development of a real‐world feasible in situ skid estimation system with the capability of operating in harsh and unforeseen environments using low‐cost/power and ease of integrating sensors is still an open problem in terramechanics. This paper presents a novel velocity‐based definition for skidding that enables a real‐world feasible estimation for the mobile robot's undesired skidding at the vehicle‐level in outdoor environments. The proposed technique estimates the undesired skidding using a combination of two proprioceptive sensors (e.g., inertial measurement unit and wheel encoder) and deep learning. The practicality of a velocity‐based definition and the performance of the proposed undesired skid estimation technique are evaluated experimentally in various outdoor terrains. The results show that the proposed technique performs with less than 11.79 mm/s mean absolute error and estimates the direction of undesired skidding with approximately 98% accuracy.

Motion Tracking of Four-Wheeled Mobile Robots in Outdoor Environments Using Bayes’ Filters

Motion Tracking of Four-Wheeled Mobile Robots in Outdoor Environments Using Bayes’ Filters

In situ slip estimation for mobile robots in outdoor environments

AbstractAccounting for wheel–terrain interaction is crucial for navigation and traction control of mobile robots in outdoor environments and rough terrains. Wheel slip is one of the surface hazards that needs to be detected to mitigate against the risk of losing the robot's controllability or mission failure occurring. The open problems in the Terramechanics field addressed are (1) the need for in situ wheel‐slippage estimation in harsh environments using low‐cost/power and easy to integrate sensors, and (2) removing the need for prior information of the soil, which is not always available. This paper presents a novel slip estimation method that utilizes only two proprioceptive sensors (IMU and wheel encoder) to estimate the wheel slip using deep learning methods. It is experimentally shown to be real‐world feasible in outdoor, uneven terrains without prior soil information assumptions. Comparison with previously used machine learning algorithms for continuous and discrete slip estimation problems show more than 9% and 14% improvement in estimation performance, respectively.

Biomimetic navigation system using a polarization sensor and a binocular camera.

With the vigorous development of vision techniques, simultaneous localization and mapping (SLAM) has shown the capability of navigating autonomous robots in global-navigation-satellite-system-denied environments. However, the long-term robust navigation of lightweight autonomous robots in outdoor environments with complex interferences, such as illumination change, dynamic objects, and electromagnetic interference, is still a great challenge. In this paper, a polarization sensor-aided SLAM (POL-SLAM) that can provide absolute heading constraints for pure SLAM is proposed. POL-SLAM is a lightweight, tightly coupled system consisting of a polarization sensor and binocular camera. By means of an initialization that uses a polarization sensor, an absolute heading angle for the entire map is designed. Additionally, an algorithm to eliminate mismatching points using the matching point vector is proposed. The objective function of bundle adjustment is then deduced according to the re-projection error and polarization sensor. The vehicle test shows that the yaw and trajectory accuracies of POL-SLAM are significantly improved compared to pure SLAM. The yaw and trajectory accuracies are increased by 43.1% and 36.6%, respectively. These results indicate that the proposed POL-SLAM can improve the reliability and robustness of pure SLAM and can be used in lightweight autonomous robots in outdoor environments.

A Novel Lidar-Assisted Monocular Visual SLAM Framework for Mobile Robots in Outdoor Environments

In this paper, a novel 3D lidar-assisted monocular visual SLAM (LAMV-SLAM) framework is proposed for mobile robots in outdoor environments. LAMV-SLAM can run in real-time without a GPU and build a dense map with real scale. An online photometric calibration thread is integrated into LAMV-SLAM to eliminate the photometric disturbances in images. The tracking thread combines the lidar and vision data to estimate and refine the frame-to-frame transformation. In this thread, the depth fusion algorithm is proposed to provide accurate depth values for the extracted visual features by combining the lidar points, and a novel two-stage optimization method is proposed to utilize the fused lidar-vision data to estimate the camera transformation with the real scale. A parallel mapping thread generates new map points based on depth-filter and lidar-vision data fusion. A loop closing thread further reduces the accumulative errors of the system. To verify the accuracy and efficiency of the system, we evaluated the proposed pipeline on the KITTI odometry benchmark, and our LAMV-SLAM achieves a 0.81% of relative position drift while running at over 3X real-time speed. To verify the robustness of the system in challenging environments, experiments were carried out on the NCLT and NuScenes datasets. Moreover, real-world experiments were applied to our mobile robot platform to show the practicability and validity of the proposed approach.

A coaxial quadrotor flying robot: Design, analysis and control implementation

In this paper, the analysis, design and fabrication of an Octorotor Miniature Aerial Vehicle (MAV) are presented. This rotorcraft setup can act as an autonomous robot in outdoor environments to carry and move high payloads, and can thus also be used in object manipulation tasks. Within this paper, the system design, dynamic modeling, control system design, and its implementation are elaborated. The design procedure for this MAV includes two mechanics and electronics sub systems, which are thoroughly discussed. The mechanical design covers the aerodynamics analysis, body components, and propulsion system implementation. The electronic design includes hardware selection and control system design for the presented aerial robot. Since the MAV flies in outdoor environments, it additionally has to cope with windy conditions. Hence, the stabilization control scheme is considered along the lines of usual PID controllers. The attitude controller, as well as the inner and outer control loops, are fully studied, and a Fuzzy-PID controller is developed to improve the performance. Finally, considering maneuvers in two case studies, simulation and implementation results are studied and compared.

Coordinated Movement of Multiple Robots in Outdoor Environment with Obstacles Pages

This article mainly focuses on the proposal of the program that coordinates the movement of multiple robots in outdoor environment. The emphasis of the proposed algorithm is on collision free movement. Simulation with four robots that are controlled in an environment with obstacles is the result of these experiments. The formation of robots consists of a leader and three following robots, which are trying to copy leader¢‚¬„¢s movement. Robots use GPS, laser rangefinder, as well as bumpers sensors for the environment scanning. GPS is used as positioning sensor and other sensors are used for reactive navigation. Besides that, simulation uses virtual reality from Microsoft Robotics Studio 4 (MRS). In addition, programming language used for this simulation is called Visual Programming Language. Advantage of MRS usage is that the final code can be simply transferred into real robots.

Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.

Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments

Working environment of mobile robots has gradually expanded from indoor structured scenes to outdoor scenes such as wild areas in recent years. The expansion of application scene, change of sensors and the diversity of working tasks bring greater challenges and higher demands to active localization for mobile robots. The efficiency and stability of traditional localization strategies in wild environments are significantly reduced. On the basis of considering features of the environment and the robot motion curved surface, this paper proposes a curve-localizability-SVM active localization algorithm. Firstly, we present a curve-localizability-index based on 3D observation model, and then based on this index, a curve-localizability-SVM path planning strategy and an improved active localization method are proposed. Obtained by setting the constraint space and objective function of the planning algorithm, where curve-localizability is the main constraint, the path helps improve the convergence speed and stability in complex environments of the active localization algorithm. Helped by SVM, the path is smoother and safer for large robots. The algorithm was tested by comparative experiments and analysis in real environment and robot platform, which verified the improvement of efficiency and stability of the new strategy.

Dual-Master/Single-Slave Haptic Teleoperation System for Semiautonomous Bilateral Control of Hexapod Robot Subject to Deformable Rough Terrain

The increasing application requirements of multilegged walking robots in outdoor environments pose new challenges regarding the design of their teleoperation systems. Some of these challenges arise from the multiple degrees of freedom of the telerobotic system and nonpassive exogenous disturbance. Herein, a novel control system based on a dual-master/single-slave bilateral haptic teleoperation framework using a semiautonomous strategy for hexapod robots walking on deformable rough terrains is proposed. In this teleoperation system, the body velocities and postures of the hexapod robot are determined according to the positions of two haptic master robots. The proposed teleoperator includes a time-domain passivity control approach to compensate for the system’s potential nonpassivity induced by the contact slippage between the foot and the ground. Furthermore, a posture-level bilateral controller is designed to overcome the unpredictable posture vibration. Information about the velocity loss and posture error is displayed to the human operator in the form of haptic force. In the underlying controller of the slave robot, a foot-force optimization algorithm is developed to improve the local autonomy of the teleoperation system. Furthermore, the stability of the system is demonstrated by its passivity. Experimental results indicate that the proposed controllers can provide a stable and transparent bilateral haptic teleoperation system for a hexapod robot under environmental perturbations.

Real-Time Lidar-based Localization of Mobile Ground Robot

Paper discusses modern methods for localization of mobile ground robot in outdoor environment. Special attention is payed to lidar-based methods which allow to do precise simultaneous localization and mapping (SLAM) independently to lighting conditions. Modular approach to lidar-based localization is proposed. It consists of three modules: map reconstructor, map merger and corrector, localization module. As map reconstructor we use 3D Lidar-based Graph SLAM, state-of-the-art method LOAM and its modification with point cloud segmentation techniques A-LeGO-LOAM. As map merger and corrector we apply the technique of interactive map correction for 3D Graph SLAM. For localization module we compare different real-time approaches: NDT scan matching and Localization with scan batch processing. They do not allow to localize the robot with an unknown initial position. To overcome this problem, a stage of global Monte Carlo localization is proposed, which allows to generate a switching condition and a rough initial pose. Matrix of localization convergence speed is introduced to study influence of possible inaccuracies of initial pose estimation to the convergence of real-time localization approach. An important emphasis in the paper is made to the evaluation of the localization quality with relative and absolute metrics. Experimental results are obtained for own data from mobile ground robot ClearPath Husky equipped with Velodyne HDL-32E lidar. Proposed approach to robot localization achieves real-time performance for different hardware platforms in particular single-board computer Nvidia Jetson Xavier. It demonstrates possibility for integration of studied modular localization approach to onboard control systems of autonomous vehicles.

Accurate real-time visual SLAM combining building models and GPS for mobile robot

This paper presents a novel 7 DOF (i.e., orientation, translation, and scale) visual simultaneous localization and mapping (vSLAM) system for mobile robots in outdoor environments. In the front end of this vSLAM system, a fast initialization method is designed for different vSLAM backbones, which upgrades the accuracy of trajectory and reconstruction of vSLAM with an absolute scale computed from depth maps generated by building blocks. In the back end of this vSLAM, we propose a nonlinear optimization mechanism throughout which multimodal data are combined for more robust optimization. The modality of building blocks in optimization can improve the tracking accuracy and the scale estimation. By integrating the pose estimated from visual information and the position received through GPS, the optimization further alleviates the drift. The experimental results prove that the proposed method is extremely suitable for outer AR application for outdoor environments, because our method has superior initialization performance, runs in real time, and achieves real scale, higher accuracy, and robustness.

Unmanned Ground Vehicle Modelling in Gazebo/ROS-Based Environments

The fusion of different technologies is the base of the fourth industrial revolution. Companies are encouraged to integrate new tools in their production processes in order to improve working conditions and increase productivity and production quality. The integration between information, communication technologies and industrial automation can create highly flexible production models for products and services that can be customized through real-time interactions between consumer, production and machinery throughout the production process. The future of production, therefore, depends on increasingly intelligent machinery through the use of digital systems. The key elements for future integrated devices are intelligent systems and machines, based on human–machine interaction and information sharing. To do so, the implementation of shared languages that allow different systems to dialogue in a simple way is necessary. In this perspective, the use of advanced prototyping tools like Open-Source programming systems, the development of more detailed multibody models through the use of CAD software and the use of self-learning techniques will allow for developing a new class of machines capable of revolutionizing our companies. The purpose of this paper is to present a waypoint navigation activity of a custom Wheeled Mobile Robot (WMR) in an available simulated 3D indoor environment by using the Gazebo simulator. Gazebo was developed in 2002 at the University of Southern California. The idea was to create a high-fidelity simulator that gave the possibility to simulate robots in outdoor environments under various conditions. In particular, we wanted to test the high-performance physics Open Dynamics Engine (ODE) and the sensors feature present in Gazebo for prototype development activities. This choice was made for the possibility of emulating not only the system under analysis, but also the world in which the robot will operate. Furthermore, the integration tools available with Solidworks and Matlab-Simulink, well known commercial platforms of modelling and robotics control respectively, are also explored.

Caracterización de un Sistema GPS RTK de Bajo Costo

En las últimas décadas la comunidad robótica ha adoptado cada vez más el uso de sensores GPS-RTK para estimar de forma precisa la localización de robots móviles en ambientes exteriores. En este trabajo se presenta la caracterización de un sistema GPS-RTK de bajo costo. A partir del montaje de las unidades base y móvil se adquirieron datos de posición corregidos en tiempo real por el mismo sistema RTK bajo diferentes disposiciones físicas, velocidades de actualización y condiciones ambientales presentándose posteriormente un estudio estadístico de los resultados obtenidos.

Sensor-Fusion Based Navigation for Mobile Robot in Outdoor Environment

Autonomous navigation of the vehicles or robots is very challenging and useful task used by many scientists and researchers these days. By keeping this fact in mind, an algorithm for autonomous navigation of mobile robot in outdoor environment is proposed. This navigation track consists of colored border containing obstacles and some unplanned surfaces along with some specific points for GPS (Global Positioning System) alarms. The main goal is to avoid colored border and obstacles. For the vision problem webcam is used. First the colored border is detected by using OpenCV library by following HSI (Hue Saturation Intensity)technique. The Canny edge algorithm is used to find both edges of the border, then for detecting straight lines on both sides of track, Hough transformation is used. Finally, the closest border line is detected and its center point is calculated for which the mobile robot has to steer to avoid it. Second step is to avoid the obstacles, which is done by LRF (Laser Range Finder), first the range of LRF is defined, because not all obstacles have to be avoided, only those obstacles are detected and avoided which are in specified range as defined before, and finally GPS receiver is used to make alarms at some specific points. As a result, a successful navigation of the mobile robot in the outdoor environment is implemented.

SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition

Place recognition, or loop closure detection, is an essential component to address the problem of visual simultaneous localization and mapping (SLAM). Long-term navigation of robots in outdoor environments introduces new challenges to enable life-long SLAM, including the strong appearance change resulting from vegetation, weather, and illumination variations across various times of the day, different days, months, or even seasons. In this paper, we propose a new shared representative appearance learning (SRAL) approach to address long-term visual place recognition. Different from previous methods using a single feature modality or a concatenation of multiple features, our SRAL method autonomously learns representative features that are shared in all scene scenarios, and then fuses the features together to represent the long-term appearance of environments observed by a robot during life-long navigation. By formulating SRAL as a regularized optimization problem, we use structured sparsity-inducing norms to model interrelationships of feature modalities. In addition, an optimization algorithm is developed to efficiently solve the formulated optimization problem, which holds a theoretical convergence guarantee. Extensive empirical study was performed to evaluate the SRAL method using large-scale benchmark datasets, including St Lucia, CMU-VL, and Nordland datasets. Experimental results have shown that our SRAL method obtains superior performance for life-long place recognition using individual images, outperforms previous single image-based methods, and is capable of estimating the importance of feature modalities.

Estimation of Gas Source Location from Fluctuating Readings of Gas Sensors and Anemometer on Mobile Robot in Outdoor Environment

Here we report on a new method for robotic gas source localization. Research efforts are being made to develop mobile robots that can autonomously search for gas sources. The tasks envisaged for such robots include detecting gas leaks in industrial plants and exploring gas emissions in landfill sites. If a mobile robot is provided with gas sensors, it can detect the presence of a target gas drifting in the air. However, to find the location of the gas source is not a trivial task for the robot. Gas molecules released in the air are carried by the airflow and form a plume in the downwind direction. Since the airflow the robot encounters in gas source localization applications is almost always turbulent, the plume randomly meanders and comes to have a complicated patchy structure. Gas source localization in outdoor environments is especially challenging owing to the large fluctuations in the wind direction. In the method proposed in this series of work, a mobile robot equipped with gas sensors and an anemometer is used to measure the gas concentration and airflow direction at various locations in the area under consideration. The location of the gas source is estimated by applying a turbulent diffusion model of a gas plume to the sensor data collected by the mobile robot. Two different turbulent plume models were compared regarding the accuracy in the estimated gas source location. The results are presented to show that the proposed method can provide a map showing the area in which a gas source is likely to be found.

Estimation of Gas Source Location from Fluctuating Readings of Gas Sensors and Anemometer on Mobile Robot in Outdoor Environment

Here we report on a new method for robotic gas source localization. In the method proposed in this series of work, a mobile robot equipped with gas sensors and an anemometer is used to measure the gas concentration and airflow direction at various locations in the area under consideration. The location of the gas source is estimated by applying a turbulent diffusion model of a gas plume to the sensor data collected by the mobile robot. In the previous version of the method, the value of the turbulent diffusion coefficient in the model needs to be properly adjusted to obtain appropriate estimation results. Therefore, the model is replaced with the one that requires no empirical parameter. The estimation results are presented to show that the proposed method can provide a map showing the area in which a gas source is likely to be found.

Detection of traversable region around a mobile robot by computing terrain unevenness from the range data of a 3D laser scanner

Purpose – The purpose of this paper is to detect traversable regions surrounding a mobile robot by computing terrain unevenness using the range data obtained from a single 3D scan. Design/methodology/approach – The geometry of acquiring range data from a 3D scan is exploited to probe the terrain and extract traversable regions. Nature of terrain under each scan point is quantified in terms of an unevenness value, which is computed from the difference in range of scan point with respect to its neighbours. Both radial and transverse unevenness values are computed and compared with threshold values at every point to determine if the point belongs to a traversable region or an obstacle. A region growing algorithm spreads like a wavefront to join all traversable points into a traversable region. Findings – This simple method clearly distinguishes ground and obstacle points. The method works well even in presence of terrain slopes or when the robot experiences pitch and roll. Research limitations/implications – The method applies on single 3D scans and not on aggregated point cloud in general. Practical implications – The method has been tested on a mobile robot in outdoor environment in our research centre. Social implications – This method, along with advanced navigation schemes, can reduce human intervention in many mobile robot applications including unmanned ground vehicles. Originality/value – Range difference between scan points has been used earlier for obstacle detection, but no methodology has been developed around this concept. The authors propose a concrete method based on computation of radial and transverse unevenness at every point and detecting obstacle edges using range-dependent threshold values.

Robust Visual Robot Localization Across Seasons Using Network Flows

Image-based localization is an important problem in robotics and an integral part of visual mapping and navigation systems. An approach to robustly match images to previously recorded ones must be able to cope with seasonal changes especially when it is supposed to work reliably over long periods of time. In this paper, we present a novel approach to visual localization of mobile robots in outdoor environments, which is able to deal with substantial seasonal changes. We formulate image matching as a minimum cost flow problem in a data association graph to effectively exploit sequence information. This allows us to deal with non-matching image sequences that result from temporal occlusions or from visiting new places. We present extensive experimental evaluations under substantial seasonal changes. Our approach achieves accurate matching across seasons and outperforms existing state-of-the-art methods such as FABMAP2 and SeqSLAM.