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The World Health Organization (WHO) reported that there are 285 million visuallyimpaired people worldwide. Among these individuals, there are 39 million who are totally blind. There have been several systems designed to support visually-impaired people and to improve the quality of their lives. Unfortunately, most of these systems are limited in their capabilities. In this paper, we present a comparative survey of the wearable and portable assistive devices for visuallyimpaired people in order to show the progress in assistive technology for this group of people. Thus, the contribution of this literature survey is to discuss in detail the most significant devices that are presented in the literature to assist this population and highlight the improvements, advantages, disadvantages, and accuracy. Our aim is to address and present most of the issues of these systems to pave the way for other researchers to design devices that ensure safety and independent mobility to visually-impaired people.

Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles (MAVs). More than 110 papers from 23 high-impact computer science journals, which were published over the past 20 years, were reviewed. The techniques were divided into monocular and stereo. The former uses a single camera, while the latter makes use of images taken by two synchronised cameras. Monocular obstacle detection methods are discussed in appearance-based, motion-based, depth-based, and expansion-based categories. Monocular obstacle detection approaches have simple, fast, and straightforward computations. Thus, they are more suited for robots like MAVs and compact UAVs, which usually are small and have limited processing power. On the other hand, stereo-based methods use pair(s) of synchronised cameras to generate a real-time 3D map from the surrounding objects to locate the obstacles. Stereo-based approaches have been classified into Inverse Perspective Mapping (IPM)-based and disparity histogram-based methods. Whether aerial or terrestrial, disparity histogram-based methods suffer from common problems: computational complexity, sensitivity to illumination changes, and the need for accurate camera calibration, especially when implemented on small robots. In addition, until recently, both monocular and stereo methods relied on conventional image processing techniques and, thus, did not meet the requirements of real-time applications. Therefore, deep learning networks have been the centre of focus in recent years to develop fast and reliable obstacle detection solutions. However, we observed that despite significant progress, deep learning techniques also face difficulties in complex and unknown environments where objects of varying types and shapes are present. The review suggests that detecting narrow and small, moving obstacles and fast obstacle detection are the most challenging problem to focus on in future studies.

With the steady progress of China’s agricultural modernization, the demand for agricultural machinery for production is widely growing. Agricultural unmanned aerial vehicles (UAVs) are becoming a new force in the field of precision agricultural aviation in China. In those agricultural areas where ground-based machinery have difficulties in executing farming operations, agricultural UAVs have shown obvious advantages. With the development of precision agricultural aviation technology, one of the inevitable trends is to realize autonomous identification of obstacles and real-time obstacle avoidance (OA) for agricultural UAVs. However, the complex farmland environment and changing obstacles both increase the complexity of OA research. The objective of this paper is to introduce the development of agricultural UAV OA technology in China. It classifies the farmland obstacles in two ways and puts forward the OA zones and related avoidance tactics, which helps to improve the safety of aviation operations. This paper presents a comparative analysis of domestic applications of agricultural UAV OA technology, features, hotspot and future research directions. The agricultural UAV OA technology of China is still at an early development stage and many barriers still need to be overcome.

This paper presents a new approach to guaranteeing collision avoidance with respect to moving obstacles that have constrained dynamics but move unpredictably. Velocity Obstacles have been used previously to plan trajectories that avoid collisions with obstacles under the assumption that the trajectories of the objects are either known or can be accurately predicted ahead of time. However, for real systems this predicted trajectory will typically only be accurate over short time-horizons. To achieve safety over longer time periods, this paper instead considers the set of all reachable points by an obstacle assuming that the dynamics fit the unicycle model, which has known constant forward speed and a maximum turn rate (sometimes called the Dubins car model). This paper extends the Velocity Obstacle formulation by using reachability sets in place of a single “known” trajectory to find matching constraints in velocity space, called Velocity Obstacle Sets . The Velocity Obstacle Set for each obstacle is equivalent to the union of all velocity obstacles corresponding to any dynamically feasible future trajectory, given the obstacle’s current state. This region remains bounded as the time horizon is increased to infinity, and by choosing control inputs that lie outside of these Velocity Obstacle Sets, it is guaranteed that the host agent can always actively avoid collisions with the obstacles, even without knowing their exact future trajectories. Furthermore it is proven that, subject to certain initial conditions, an iterative planner under these constraints guarantees safety for all time. Such an iterative planner is implemented and demonstrated in simulation.

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM arm.

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

Unmanned surface vehicle (USV) path planning is a crucial technology for achieving USV autonomous navigation. Under global path planning, dynamic local obstacle avoidance has become the primary focus for safe USV navigation. In this study, a USV autonomous dynamic obstacle avoidance method based on the enhanced velocity obstacle method is proposed in order to achieve path replanning. Through further analysis of obstacles, the obstacle geometric model set in the conventional velocity obstacle method was redefined. A special triangular obstacle geometric model was proposed to reconstruct the velocity obstacle region. The collision time was predicted by fitting the previously gathered data to the detected obstacle’s distance, azimuth, and other relevant data. Then, it is combined with the collision risk to determine when obstacle avoidance should begin and end. In order to ensure safe driving between path points, the international maritime collision avoidance rules (COLREGs) are incorporated to ensure the accuracy of obstacle avoidance. Finally, through numerical simulations of various collision scenarios, it was determined that, under the assumption of ensuring a safe encounter distance, the maximum change rates of USV heading angle are optimized by 17.54%, 58.16%, and 28.63% when crossing, head-on, and overtaking, respectively. The results indicate that, by optimizing the heading angle, the enhanced velocity obstacle method can avoid the risk of ship rollover caused by an excessive heading angle during high-speed movement and achieve more accurate obstacle avoidance action in the event of a safety encounter.

In this original and controversial book, historian and philosopher Reviel Netz explores the development of a controlling and pain-inducing technology—barbed wire. Surveying its development from 1874 to 1954, Netz describes its use to control cattle during the colonization of the American West and to control people in Nazi concentration camps and the Russian Gulag. Physical control over space was no longer symbolic after 1874. This is a history told from the perspective of its victims. With vivid examples of the interconnectedness of humans, animals, and the environment, this dramatic account of barbed wire presents modern history through the lens of motion being prevented. Drawing together the history of humans and animals, Netz delivers a compelling new perspective on the issues of colonialism, capitalism, warfare, globalization, violence, and suffering. Theoretically sophisticated but written with a broad readership in mind, Barbed Wire calls for nothing less than a reconsideration of modernity.

Under unknown environments, drones should always maintain vigilance to address potential threats. In fact, unknown obstacles suddenly moving and blocking the way could generate great flight safety risks. Besides conventional static and moving obstacles, addressing such unknown malicious obstacles is crucial for enhancing drone safety, yet relevant research is scarce. In this work, we propose a systematic planning framework for drones with switchable obstacle avoidance strategies based on risk estimation of unknown obstacles. When the risk value in the unknown environment is low, the drone adopts a global planning strategy. However, when encountering high-risk obstacles that move suddenly, the drone switches to a reactive obstacle avoidance strategy. Firstly, an online dynamic point cloud recognition method is employed to identify dynamic and static obstacles in unknown environments. Obstacle trajectories are then predicted based on historical positions, without the need for predefined motion models. A risk estimation function based on field theory is devised to assess the potential risk caused by static obstacles in unknown environments. To accommodate different obstacle threats, a gradient-based global path planning method is utilized to avoid conventional static and dynamic obstacles, while a reactive avoidance strategy is promptly activated to avoid high-risk malicious obstacles that move suddenly. Extensive simulations and real flight tests validate the efficacy of the proposed approach. The reaction time from detecting the sudden movement of a static obstacle to planning a safe trajectory is less than 3 ms .

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.