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We present simulation scenarios of private and public transportation modes of transportation being replaced by autonomous mobility-on-demand (AMoD) system in Edinburgh, UK, based on realistic temporal and spatial demand patterns. Edinburgh is interesting to simulate because it is selected to be one of the top 50 congested cities despite the traditional layout of the city with a low population density. For each mode of transportation, we test five different operational policies (from the literature) for different fleet sizes. We examine the results both in terms of system expenditures and profits, as well as service quality. Additionally, we evaluate Pareto solutions that combine policies' performance to give a trade-off between wait times and empty miles. In comparison with private cars, the Autonomous Vehicles (AV) resulted in 86% reduction in the number of cars on the road. In addition, the use of the AVs per day is at least two to seven times higher than the UK private car usage. A reduction in the number of vehicles will have a positive influence on the congestion. When compared with the public mode, AMoD vehicles require 62% less time and 60% less distance than buses for the same trips. [ABSTRACT FROM AUTHOR]

This review article offers an in-depth analysis of cooperative motion planning and control in aerial-ground autonomous systems, emphasizing their methods and applications. It explores the integration of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), focusing on their synchronized planning and control mechanisms that enable efficient task execution in various settings, such as disaster response, environmental monitoring, and urban surveillance. The article addresses the motion planning strategies, and control mechanisms, while also highlighting the challenges and future trends in this domain. It serves as a comprehensive resource, shedding light on both the potentials and limitations of these systems, thereby providing valuable insights for researchers and practitioners in the field of autonomous systems. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a sparse semantic map building method and an outdoor relocalization strategy based on this map. Most existing semantic mapping approaches focus on improving semantic understanding of single frames and retain a large amount of environmental data. Instead, we don't want to locate the UGV precisely, but use the imprecise environmental information to determine the general position of UGV in a large-scale environment like human beings. For this purpose, we divide the environment into environment nodes according to the result of scene understanding. The semantic map of the outdoor environment is obtained by generating topological relations between the environment nodes. In the semantic map, only the information of the nodes is saved, so that the storage space can be kept at a very small level with the increasing size of environment. When the UGV receives a new local semantic map, we evaluate the similarity between local map and global map to determine the possible position of the UGV according to the categories of the left and right nodes and the distance between the current position and the nodes. In order to validate the proposed approach, experiments have been conducted in a large-scale outdoor environment with a real UGV. Depending on the semantic map, the UGV can redefine its position from different starting points. [ABSTRACT FROM AUTHOR]

Unmanned ground vehicles (UGVs) have been utilized in many civilian fields in addition to the traditional military field. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. Many applications require the UGV to autonomously navigate static environments while taking into consideration obstacle avoidance. Autonomous path planning is one of the key challenges and issues related to UGVs. Generally, robotic path planning is an optimization search problem that comes in different forms. Some of its forms have been solved by different classical algorithms such as A*, but these algorithms are computationally inefficient. In contrast, the emerging nature-inspired algorithms outperform the classical ones since the computational overhead is reduced. Nature-inspired algorithms are among the most common heuristic algorithms. This paper proposes a near-optimal algorithm to find a feasible path for UGV in a static environment. The performance of the proposed algorithm was compared with other well-established algorithms in path planning literature such as A* using a simulator developed for this purpose. The simulator tests three performance measures path length, farness from obstacles, and running time. The simulator results revealed that the length of the path generated by this algorithm is near-optimal, however, the generated path is kept far from obstacles. [ABSTRACT FROM AUTHOR]

Highlights • Focus on trade-off between mobility and latency in unmanned ground vehicles (UGVs). • Focus on a path following task. • Novel empirical trade-off relationships for large, high speed, teleoperated UGVs. • A benchmark for the U.S. Army's evaluations of future UGV technologies. Abstract As the penetration levels of unmanned ground vehicles (UGVs) in military applications increase, there is a growing need to evaluate their mobility across different latencies and various modes of operation ranging from pure teleoperation to full autonomy. State-of-the-art tools to evaluate mobility of ground vehicles do not address this need due to their not accounting for UGV technologies and the associated latencies. Although the trade-off between latency and performance has been thoroughly studied in the telerobotics literature and the results may qualitatively shed light onto the UGV domain, as well, a quantitative generalization is not possible due to the differences in context. Recognizing this gap, this paper presents a functional relationship between mobility and latency in high-speed, teleoperated UGVs under the context of path following. Specifically, data from human-in-the-loop simulations performed in this paper are combined with data from prior studies to span three vehicle types, three courses, and teleoperation latencies ranging from 0 s to 1 s. This combination yields for the first time a diverse data set for the context of path following in high speed, teleoperated UGVs. Based on this data set, empirical relationships are derived to quantify the trade-off between latency versus average speed and lane keeping error. These relationships can be used to establish a benchmark to evaluate the performance of autonomy-enabled UGV systems. [ABSTRACT FROM AUTHOR]

Reliable knowledge of the vehicle heading plays a significant role in the autonomous navigation of agricultural Unmanned Ground Vehicles (UGVs), especially in the context of unstructured outdoor environments such as rural and forestry scenarios. However, achieving this information with an acceptable degree of confidence is a non-trivial task and still an open field of research. Expensive solutions are available on the market, but they often discourage most farmers due to the large investments needed for the startup. This paper introduces a novel algorithmic solution for reliable evaluation of the absolute vehicle heading, grounded on adaptive Kalman filtering with input evaluation via linear regression analysis. The proposed approach provides a functional and affordable solution to the heading estimation problem that can be used in real-world applications. The system is validated through an extensive experimental campaign using an all-terrain tracked rover operating in agricultural settings, showing good accuracy compared to other approaches, such as a dual GPS method found in the literature. • Increasing the accuracy of GPS-based heading estimation. • Autonomous mobile robot navigation in unstructured and challenging environments. • Localization of unmanned ground vehicles in agricultural settings. • Adaptive Kalman filtering for GPS heading correction. [ABSTRACT FROM AUTHOR]

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 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]

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 agnostic robotic paradigm (ARP) represents a recent development as the use of robots becomes more common, and there is a need for agnostic robots to cope with rich artificial objects environments. All parties and stakeholders need to seize the imminent opportunity and act on ushering in the revolutionary changes of contemporary robotic and facility control solutions. The scalability and effectiveness of robotic enterprise solutions depend primarily on the availability of operational information, robotic solutions, and their information infrastructure. However, different functions and software of robotics and facilities are being launched in the market. Therefore, this paper investigates the implementation of the emerging ARP for the Industrial Internet of Things (IIoT) and resource synchronisation flexible robotic and facility control system to address this challenge. We propose an Artificial Intelligence (AI) edge intelligence and IIoT-based agnostic robotic architecture for resource synchronisation and sharing in manufacturing and robotic mobile fulfillment systems (RMFS). We adopted simultaneous localisation and mapping (SLAM) as one of the edge intelligence, provided the simulation results, and tested with multiple parameters under different conflicts. Our research suggests that purposely developing an ARP for flexible robotic and facility control system via IIoT assisted with AI-edge intelligence are a good solution for both operational and management level under a cloud platform. [ABSTRACT FROM AUTHOR]

Abstract: Motion planning for unmanned ground vehicles (UGV) constitutes a domain of research where several disciplines meet, ranging from artificial intelligence and machine learning to robot perception and computer vision. In view of the plurality of related applications such as planetary exploration, search and rescue, agriculture, mining and off-road exploration, the aim of the present survey is to review the field of 3D terrain traversability analysis that is employed at a preceding stage as a means to effectively and efficiently guide the task of motion planning. We identify that in the epicenter of all related methodologies, 3D terrain information is used which is acquired from LIDAR, stereo range data, color or other sensory data and occasionally combined with static or dynamic vehicle models expressing the interaction of the vehicle with the terrain. By taxonomizing the various directions that have been explored in terrain perception and analysis, this review takes a step toward agglomerating the dispersed contributions from individual domains by elaborating on a number of key similarities as well as differences, in order to stimulate research in addressing the open challenges and inspire future developments. [Copyright &y& Elsevier]

Abstract: Research on intelligent vehicles has been developed by more and more organizations, so the evaluation methods of intelligent driving abilities are of great importance. In this paper we first analyzed information processing of intelligent vehicles and established pattern recognition model of intelligent driving behavior based on prototype matching; second, we briefly introduced the basic theory and methods of fuzzy synthetic evaluation; finally, according to the competition “Future Challenge”, we focused on overtaking behavior and U-Turn behavior, which were also considered as the first level factors of the evaluation model, and structured the fuzzy synthetic evaluation model of intelligent driving behaviors. It can be used to assess the intelligent driving behaviors for unmanned ground vehicles for the next “Future Challenge” competitions, and also provide a strong basis for research on intelligent vehicle control in the future. [Copyright &y& Elsevier]

• A global path consists of a number of points that should be followed by an autonomous terrestrial or aerial unmanned vehicle in a certain environment. • We designed and trained a Deep Learning model able to predict the position of key waypoints in an aerial map of a target row-based field, that can be used to generate a path that covers the full extension of the field. • For training, we used a synthetic dataset randomly generated in order to be a representation as general as possible of a row-based culture. • We tested the proposed methodology with both unseen synthetic maps and manually labeled satellite images of vineyards and orchards. • Experimentations show that our approach can successfully generalize to a real-world context, leading to a global path generation with high field coverages. Agriculture 3.0 and 4.0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield. Row-based crops are the perfect settings to test and deploy smart machines capable of monitoring and manage the harvest. In this context, global path generation is essential either for ground or aerial vehicles, and it is the starting point for every type of mission plan. Nevertheless, little attention has been currently given to this problem by the research community and global path generation automation is still far to be solved. In order to generate a viable path for an autonomous machine, the presented research proposes a feature learning fully convolutional model capable of estimating waypoints given an occupancy grid map. In particular, we apply the proposed data-driven methodology to the specific case of row-based crops with the general objective to generate a global path able to cover the extension of the crop completely. Extensive experimentation with a custom made synthetic dataset and real satellite-derived images of different scenarios have proved the effectiveness of our methodology and demonstrated the feasibility of an end-to-end and completely autonomous global path planner. [ABSTRACT FROM AUTHOR]

In this paper, a disturbance-observer-based control strategy is proposed for longitudinal control and lateral control of unmanned ground vehicles. A composite nonlinear extended state observer is put forward to estimate the total disturbance. A longitudinal dead-zone model is developed with actual constraints and rapidity requirements for unmanned ground vehicles. The disturbance-observer-based feedback controller is presented to improve the robustness of the closed-loop system by incorporating the composite nonlinear extended state observer. Numerical and experimental results are given to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Local meteorological conditions including wind speed and turbulence significantly influence the dispersion of pollutant plumes, introducing severe difficulties in predicting its trajectory, potential evacuation sites, and ultimately containment efforts. Ongoing developments in estimating rapid contaminant dispersion include the combined use of local meteorological data along with plume-source localization and identification via autonomous data-driven mobile-sensing robotic/vehicular platforms. With a vast number of available environmental-sensing mobile platforms, contaminant dispersion scenarios, and source-finding algorithms, selection of the ideal configuration for autonomous source localization involves a great deal of opportunity alongside uncertainty. This paper aims to review the significant developments of unmanned ground-based mobile sensing network configurations and autonomous data acquisition strategies commonly used for the task of gaseous plume source localization. Unlabelled Image • Meteorogical variables obtained using mobile sensing increases the detection accuracy for plume tracking • Conducting polymer gas sensors are most efficient for autonomous robots to locate the source in the least amount of time. • Using Large-eddy-simulation within numerical weather prediction models increases the accuracy of the dispersion models significantly. • Optimal multi-sensor fusion algorithms are critical to accomplish the goal of autonomously localizing the pollutant source. • Bayesian, Optimization, and Hybrid source-finding methods are reviewed. [ABSTRACT FROM AUTHOR]