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The paper describes the case study of the mobile robot Andabata navigating on natural terrain at low speeds with fuzzy elevation maps (FEMs). To this end, leveled three-dimensional (3D) point clouds of the surroundings are obtained by synchronizing ranges obtained from a 360 ∘ field of view 3D laser scanner with odometric and inertial measurements of the vehicle. Then, filtered point clouds are employed to produce FEMs and their corresponding fuzzy reliability masks (FRMs). Finally, each local FEM and its FRM are processed to choose the best motion direction to reach distant goal points through traversable areas. All these tasks have been implemented using ROS (Robot Operating System) nodes distributed among the cores of the onboard processor. Experimental results of Andabata during non-stop navigation on an urban park are presented.

In this study, autonomous navigation of an Unmanned Ground Vehicle moving in an unknown environment is realized with Genetic Algorithms using Sectoral Grid Based Axising Method. For the algorithm, three dimensional environment is reduced to two dimensional numerical matrix. Terrain structure as well as obstacles are mapped one to oneon to the matrix. Coding is based upon modularity for integrating real sensing and localization inputs into the matrix including error rates. Mathematical formulization is build over Cartesian Coordinate System for computational simplicity. Simulations are made in intense obstacle and severe terrain conditions. The algorithm’s performance is tested and the results are discussed.

In this study, the development process, terminology and categories of robot and robotic technology is surveyed and operational requirements of Unmanned Ground Vehicles are presented. Anticipation of military concepts and doctrines about engineering and improvement necessities for the Unmanned Ground Vehicles which has a great importance in future combat systems are evaluated in general and advantages of these very critical systems in todays asymmetric operational conditions are identified. Expected final product parameters from producers and technology companies in unit level are compiled in detail under functional requirements title.

Localization capabilities are necessary for autonomous robots that need to keep track of their position with respect to a surrounding environment. A pursuit robot is an autonomous robot that tracks and pursues a moving target, requiring accurate localization relative to the target’s position and obstacles in the local environment. Small unmanned ground vehicles (SUGVs) equipped with a monocular camera and wheel encoders could act as effective pursuit robots, but the noisy 2D target position and size estimates from the monocular camera will in turn lead to overly noisy 3D target pose estimates. One possible approach to relative localization for pursuit robots is, rather than simply tracking and estimating a relative robot–target position in each frame, joint localization , in which the purser and target are both localized with respect to a common reference frame. In this paper, we propose a novel method for joint localization of a pursuit robot and arbitrary target. The proposed method fuses the pursuit robot’s kinematics and the target’s dynamics in a joint state space model. We show that predicting and correcting pursuer and target trajectories simultaneously produces improved results compared to standard filters for estimating relative target trajectories in a 3D coordinate system. For visual tracking, we also introduce an adaptive histogram matching threshold for suspending tracking when the target is lost in a cluttered environment. When tracking is suspended, rather than traversing the entire image to search for a reappearance of the target, we only search the part of the image segmented by histogram backprojection and correctly reinitialize the tracker. The experimental results show that the joint localization method outperforms standard localization methods and that the visual tracker for pursuit robot can deal effectively with target occlusions. [ABSTRACT FROM AUTHOR]

Military vehicle convoys are logistical supply chains that are heavily relied upon and are very often operated in severe terrain conditions. As it is shown in this article, the convoy mobility analytical estimation becomes a rather difficult task since the track, and thus tire–terrain interaction, is formed by multiple vehicles of a convoy driving over the same track. Furthermore, the distribution of power between the drive wheels of each convoy vehicle is a key factor in convoy mobility enhancement. A vehicle can be immobilized due to inappropriate combination of a power supply to a wheel and its poor tire–terrain properties. This leads to mobility loss of several or all the vehicles of the convoy. To the contrary, power wheel distributions concerted with wheel–terrain conditions improve mobility of a wheel, vehicle and the entire convoy. This article crystallizes the main factors that impact convoy mobility, discusses the factors at a descriptive level of vehicle dynamics control and then results in a convoy on-line mobility estimation and control strategy. [ABSTRACT FROM AUTHOR]

This work addresses the problem of localizing a ground target observed by multiple heterogeneous unmanned vehicles. Specifically, the case of a team of unmanned aerial and ground vehicles is analyzed. Effective collaboration between unmanned aerial and ground vehicles can utilize the strengths of both platforms while mediating their individual weaknesses. In this research, a probabilistic framework is proposed to improve target localization accuracy by utilizing the epipolar geometry and inter-robot localization measurements. A virtual simulation environment is implemented to fully test the proposed methods. Hardware experiments are carried out to validate the proposed methods. [ABSTRACT FROM PUBLISHER]

Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations. [ABSTRACT FROM AUTHOR]

Outdoor scene understanding plays a key role for unmanned ground vehicles (UGVs) to navigate in complex urban environments. This paper presents a novel 3D scene-understanding framework for UGVs to handle uncertain and changing lighting conditions outdoors. A 2D bearing angle (BA) image is deployed to perform scene understanding so that the computational burden in the process of segmentation and classification of the 3D laser point cloud can be reduced. An improved super-pixel algorithm is used for fast 3D scene segmentation, and then the Gentle–Adaboost algorithm is utilized to perform super-pixel patch classification using the texture features of the Gray Level Co-occurrence Matrix. All false classification results in the uncertain super-pixel patches of BA images are transformed back to raw 3D laser points and a re-classification is conducted to refine the 3D scene understanding for UGVs. The results from a real laser dataset taken from a large-scale campus environment show the validity and robust performance of the proposed approach, in comparison with the results from Korea Advanced Institute of Science and Technology dataset. [ABSTRACT FROM PUBLISHER]

This paper proposes a global path planning (GPP) system based on building information modeling (BIM) and physics engine for unmanned ground vehicles (UGVs) operations in indoor environments. Firstly, UGV configuration is integrated into BIM as a knowledge base of GPP system by customized IFC structure, with which a multi-layer map generation method is proposed with improved logic and efficiency. Secondly, a UGV-centric A* path planning algorithm is designed by considering UGV's properties, including mobility-based primitive expansion, geometry-based collision checking, mobility-based cost setting, and mobility-based analytical expansion. Finally, a reliable trajectory generation method is developed based on physics engine, followed with a novel spatiotemporal coordination method for efficient collision avoidance. The whole GPP system is validated in a representative university building floor and a common inspection UGV. It is demonstrated that UGV-integrated BIM improves the resources interoperability for the whole system, and the map components are clarified clearly for efficient generation and maintenance. Besides, the UGV-centric A* performs a 100% success rate in congested environments where traditional A* always fail. It can even reduce almost 50% of the trajectory time and steering jerk in open scenarios than traditional A*, but still with an acceptable computation speed (less than 0.6 s). Finally, trajectory coordination saves 50% of path traveling time compared with the general queue method. • BIM-based schema to integrate UGV's configuration and BIM models. • Multi-layered map generation method with improved logic and efficiency. • UGV-centric A* with a 100% success rate and promising efficiency. • Reliable trajectory generation method based on the physics engine. • Developed trajectory coordination method saves 50% time over basic queue method. [ABSTRACT FROM AUTHOR]

Path planning for multiple Unmanned Ground Vehicles (UGVs) is a critical problem for UGV autonomy and is increasingly attracting attention due to its wide applications. This paper presents a continuous ant colony-based multi-UGV path planner, which consists of UGV path planning and multi-UGV coordination. A continuous Ant Colony Optimisation with a Probability-based random-walk strategy and an Adaptive waypoints-repair method (ACOPA R) is proposed to optimise the path for each UGV. Collision avoidance among the UGVs for the multi-agent coordination problem is then resolved via a velocity shifting optimisation algorithm. In ACOPA R , exploration and exploitation are balanced using a probability-based random-walk strategy switching between a Brownian and a Cauchy motion to modify the construction process of new solutions. An adaptive waypoints-repair strategy and a re-initialisation strategy are designed to improve the algorithm's performance in finding feasible paths. A test suite of multi-UGV path planning with 12 cases is proposed to evaluate the search capability and scalability of the proposed ACOPA R compared to other algorithms. Experimental results validate the superiority of ACOPA R , especially when solving complex, high-dimensional problems. • A new ACOPA R is designed for optimising the path for each UGV. • A new random-walk strategy switching between Brownian and Cauchy motion is designed. • Adaptive waypoints-repair-strategy to improve search accuracy and scalability. • Multi-agent coordination is designed to avoid the collision among UGVs. • Experiments validate the superiority of ACOPA R , especially on complex problems. [ABSTRACT FROM AUTHOR]

This paper investigates techniques on improving navigation accuracy using multiple sensors mounted on a mobile platform and exploiting the inherent characteristic of a ground vehicle that does not move along the cross-track and off the ground, often termed nonholonomic constraints (NHC) for car-like vehicles that assume no slip or skid. The forward velocity of the vehicle is obtained using a wheel encoder. The 3D velocity vector becomes observable during the normal moving state of the vehicle by using NHC, which produces one virtual sensor. Another virtual sensor is the zero-velocity update (ZVU) condition of the vehicle; when the condition is true, the 3D velocity vector (which is zero) becomes observable. These observables were employed in an extended Kalman filter (EKF) update to limit the growth of inertial navigation system error. We designed an EKF for data fusion of inertial measurement units, global positioning systems (GPS), and motion constraints (i.e., NHC and ZVU). We analyzed the effects of utilizing these constraints on improving navigation accuracy in stationary and dynamic cases. Our proposed navigation suite provides reliable accuracy for unmanned ground vehicle applications in a GPS-denied environment (e.g., forest canopy and urban canyon). [ABSTRACT FROM AUTHOR]

An approach for coordination and control of 3D heterogeneous formations of unmanned aerial and ground vehicles under hawk-eye-like relative localization is presented in this paper. The core of the method lies in the use of visual top-view feedback from flying robots for the stabilization of the entire group in a leader–follower formation. We formulate a novel model predictive control-based methodology for guiding the formation. The method is employed to solve the trajectory planning and control of a virtual leader into a desired target region. In addition, the method is used for keeping the following vehicles in the desired shape of the group. The approach is designed to ensure direct visibility between aerial and ground vehicles, which is crucial for the formation stabilization using the hawk-eye-like approach. The presented system is verified in numerous experiments inspired by search-and-rescue applications, where the formation acts as a searching phalanx. In addition, stability and convergence analyses are provided to explicitly determine the limitations of the method in real-world applications. [ABSTRACT FROM AUTHOR]

Highlights: [•] Traffic operating in the absence of lanes is capable of larger traffic bandwidths. [•] Fast genetic algorithm with Bezier curve generates the trajectory of each vehicle. [•] Overtaking and vehicle following behaviours are studied for unorganized traffic. [•] These behaviours are fused as heuristics to make a fast coordination scheme. [•] Plans are adapted to overcome uncertainties as the vehicles move. [ABSTRACT FROM AUTHOR]

The article presents a study that examines skill development and workload among those piloting teleoperated unmanned ground vehicles (UGVs) through openings of different widths using the so-called cornering law. Two groups of novices operated UGVs using either a first-person or third-person-overhead view. In both cases the cornering law was successful in modeling how aperture width affected driving performance.