Showing papers on "Obstacle published in 2021"

Rewriting results sections in the language of evidence

Abstract: Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the 'good practices' that exist in other fields.

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

Abstract: In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.

Analysis of barriers to implement drone logistics

Abstract: Companies are adopting innovative methods for responsiveness and efficiency in the logistics sector The implementation of drones in logistics sector is a move in this direction Potential obstacle

Finite Distribution Estimation-Based Dynamic Window Approach to Reliable Obstacle Avoidance of Mobile Robot

Abstract: This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA.

A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

Abstract: A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.

Deep Learning-based Monocular Obstacle Avoidance for Unmanned Aerial Vehicle Navigation in Tree Plantations

Abstract: In recent years, Unmanned Aerial Vehicles (UAVs) are widely utilized in precision agriculture, such as tree plantations. Due to limited intelligence, these UAVs can only operate at high altitudes, leading to the use of expensive and heavy sensors for obtaining important health information of the plants. To fly at low altitudes, these UAVs must possess the capability of obstacle avoidance to prevent crashes. However, most current obstacle avoidance systems with active sensors are not applicable to small aerial vehicles due to the cost, weight, and power consumption constraints. To this end, this paper presents a novel approach to the autonomous navigation of a small UAV in tree plantations only using a single camera. As the monocular vision does not provide depth information, a machine learning model, Faster Region-based Convolutional Neural Network (Faster R-CNN), was trained for the tree trunk detection. A control strategy was implemented to avoid the collision with trees. The detection model uses image heights of detected trees to indicate their distances from the UAV and image widths between trees to find the widest obstacle-free space. The control strategy allows the UAV to navigate until any approaching obstacle is detected and to turn to the safest area before continuing its flight. This paper demonstrates the feasibility and performance of the proposed algorithms by carrying out 11 flight tests in real tree plantation environments at two different locations, one of which is a new place. All the successful results indicate that the proposed method is accurate and robust for autonomous navigation in tree plantations.

WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

Abstract: A reliable obstacle detection system is crucial for autonomous surface vehicles (ASVs) to realize fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks, such as poor detection for small objects, low estimation accuracy caused by water surface reflection, and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoder–decoder structured deep semantic segmentation network, which is a water obstacle detection network based on image segmentation (WODIS), to solve the abovementioned problems. The first design feature of WODIS utilizes the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea–sky line areas, an attention refine module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a feature fusion module (FFM) is introduced to help concatenate the multidimensional high-level features in the decoder network. The WODIS is tested and cross-validated using four different types of maritime datasets with the results demonstrating that the mean intersection over union (mIoU) of WODIS can achieve superior segmentation effects for sea-level obstacles to values as high as 91.3%.

Multi-obstacle path planning and optimization for mobile robot

Abstract: In the past few decades, many results have been achieved in the research of mobile robot path planning, and they have been applied in simple scenarios, such as factory AGV, bank guide robot. However, path planning in highly dense and complex scenarios has become an important challenge for applications. Robots face dense map and complex obstacles and hardly find out an optimal path within a reasonable period, such as unmanned vehicles in freight ports and rescue robots in earthquake environment. Therefore, a multi-obstacle path planning and optimization method is proposed. In order to simplify complex environmental obstacles, the obstacles will be divided into basis obstacles and extension obstacles. Firstly, the basis obstacles and their contour point sets are determined according to the starting point and goal point. Furthermore, the basis obstacles are optimized by convex hulls, and then the corresponding basis point set is obtained. Secondly, the extension obstacles are determined by the basis point set, starting point and goal point, and then the corresponding extension point set is generated. After that, a path planner is designed by the multi-objective D* Lite algorithm for distance and smoothness in order to get reasonable and optimized path in a complex environment. Moreover, the path is smoothed by cubic bezier curves to fit the kinematic model of the robot. Finally, The proposed method conduct comparative experiments with other algorithms to verify its accuracy and computational efficiency of planning in complex environments.

Autonomous Quadrotor Navigation With Vision Based Obstacle Avoidance and Path Planning

Abstract: Due to the high demands on military and commercial applications, the development of UAVs (unmanned aerial vehicles) has become increasingly important in recent years. In this paper, we present a vision guided autonomous navigation approach for quadrotor UAVs. A map-based offline path planning technique is developed to generate an initial path, followed by the waypoints of the trajectory for flight guidance. During the navigation, an onboard camera is utilized to acquire a sequence of monocular images for environment perception. A vision-based obstacle detection technique using optical flow is proposed for collision avoidance. The optical flow field constructed from the image sequence is used to provide the depth cues for the incoming obstacle detection. A single-board computer is adopted as a control platform, and the proposed algorithms are implemented for online and real-time processing. Several experiments are carried out in the outdoor environment for obstacles avoidance and visual guidance. The results have demonstrated the feasibility of our proposed method for path planning and autonomous navigation.

Two potential fields fused adaptive path planning system for autonomous vehicle under different velocities.

Abstract: Path planning is a basic function for autonomous vehicle (AV). However, it is difficult to adapt to different velocities and different types of obstacles including dynamic obstacle and static obstacle (such as road boundary) for AV. To solve the problem of path planning under different velocities and different types of obstacles, a two potential fields fused adaptive path planning system (TPFF-APPS) which includes two parts, a potential field fusion controller and an adaptive weight assignment unit, is presented in this work. In the potential field fusion controller, a novel potential velocity field is built by velocity information and fused with a traditional artificial potential field for adapting various velocities. The adaptive weight assignment unit is designed to distribute adaptively the weights of two potential fields for adapting different types of obstacles at the same time, including road boundary and dynamic obstacles. The simulation is carried on the Carsim-Matlab co-simulation platform, and the simulation results indicate that the proposed TPFF-APPS has excellent performance for path planning adapting to different velocities and different types of obstacles.

Double phase implicit obstacle problems with convection and multivalued mixed boundary value conditions

Abstract: In this paper we consider a mixed boundary value problem with a nonhomogeneous, nonlinear differential operator (called double phase operator), a nonlinear convection term (a reaction term depending on the gradient), three multivalued terms and an implicit obstacle constraint. Under very general assumptions on the data, we prove that the solution set of such implicit obstacle problem is nonempty (so there is at least one solution) and weakly compact. The proof of our main result uses the Kakutani-Ky Fan fixed point theorem for multivalued operators along with the theory of nonsmooth analysis and variational methods for pseudomonotone operators.

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras.

Abstract: Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

On obstacle avoidance path planning in unknown 3D environments: A fluid-based framework.

Abstract: Compared with preprocessed obstacle environments, unknown environments are more challenging for path planning. In unknown environments, an agent can make decisions only by relying on the obstacle information detected by its onboard sensors. However, when facing non-convex obstacles, this limited detection information can easily trap the agent in a local optimum. In this paper, a nature-inspired methodology called Interfered Fluid Dynamic System (IFDS) is extended to anti-local-optimum obstacle avoidance in unknown 3D environments for the first time and a novel fluid-based path planning framework is proposed. First, the detection region of the agent is discretized. Then, spherical virtual obstacles (SVOs) located at detected discrete points are generated and memorized. Thus, obstacle avoidance in unknown environments is transformed into the avoidance of known SVOs. Next, the currently generated and memorized SVOs are input to the core of the framework, the IFDS algorithm, to produce repulsive effects, and the corresponding 3D avoidance path is resolved. On this basis, to address local optimum in cases with non-convex obstacles, and considering compatibility with the IFDS, the direction coefficient and sink-heading angular rate adjustment strategies, which belong to the same system as the IFDS, are introduced to modify the IFDS in this framework. Finally, receding horizon control is introduced to improve the obstacle avoidance performance. Simulations show that the proposed framework can enable the agent to autonomously jump out of the 3D non-convex obstacle environments with typical features of the local optimum, including wall-like and cave-like obstacles, and safely reach the destination.

Rail Transit Obstacle Detection Based on Improved CNN

Abstract: With the continuous development of rail transit fully automatic operation, the urgent need to improve train operation safety makes obstacle detection become the research focus. In this work, a flexible and efficient multiscale one-stage object detector FE-YOLO was proposed for image obstacle detection. The feature extraction network is composed of attention module, downsampling module, residual block, spatial pyramid pooling (SPP) module, and so on. A repeatable bidirectional cross-scale path aggregation module was designed as the core of the feature fusion network. The dataset RT2021 of rail transit obstacles was constructed based on the real scene. The mean of average precision (mAP), detection time, iteration time, parameters, and anti-interference ability were used to compare FE-YOLO with other classic object detectors. The results showed that FE-YOLO has the best comprehensive performance. The mAP can reach 92.57%, and the single-frame detection time on the onboard embedded device is up to 0.0989 s. The ablation experiment verified the effectiveness of each module of FE-YOLO and has the best generalization performance on the PASCAL VOC dataset. Finally, a rail transit obstacle detection system was developed, and multiple sensors were used to improve the detection accuracy. Experiments showed that the detection system can work normally in different environments.

Adaptive Obstacle Avoidance for a Class of Collaborative Robots

Abstract: In a human–robot collaboration scenario, operator safety is the main problem and must be guaranteed under all conditions. Collision avoidance control techniques are essential to improve operator safety and robot flexibility by preventing impacts that can occur between the robot and humans or with objects inadvertently left within the operational workspace. On this basis, collision avoidance algorithms for moving obstacles are presented in this paper: inspired by algorithms already developed by the authors for planar manipulators, algorithms are adapted for the 6-DOF collaborative manipulators by Universal Robots, and some new contributions are introduced. First, in this work, the safety region wrapping each link of the manipulator assumes a cylindrical shape whose radius varies according to the speed of the colliding obstacle, so that dynamical obstacles are avoided with increased safety regions in order to reduce the risk, whereas fixed obstacles allow us to use smaller safety regions, facilitating the motion of the robot. In addition, three different modalities for the collision avoidance control law are proposed, which differ in the type of motion admitted for the perturbation of the end-effector: the general mode allows for a 6-DOF perturbation, but restrictions can be imposed on the orientation part of the avoidance motion using 4-DOF or 3-DOF modes. In order to demonstrate the effectiveness of the control strategy, simulations with dynamic and fixed obstacles are presented and discussed. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system.

A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

Abstract: 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.

A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

Abstract: This paper provides a review of the literature on vision-based on-board obstacle detection and distance estimation in railways. Environment perception is crucial for autonomous detection of obstacles in a vehicle’s surroundings. The use of on-board sensors for road vehicles for this purpose is well established, and advances in Artificial Intelligence and sensing technologies have motivated significant research and development in obstacle detection in the automotive field. However, research and development on obstacle detection in railways has been less extensive. To the best of our knowledge, this is the first comprehensive review of on-board obstacle detection methods for railway applications. This paper reviews currently used sensors, with particular focus on vision sensors due to their dominant use in the field. It then discusses and categorizes the methods based on vision sensors into methods based on traditional Computer Vision and methods based on Artificial Intelligence.

Relay selection in millimeter wave D2D communications through obstacle learning

Abstract: There has been growing interest in device to device (D2D) millimeterwave (mmwave) communication, due to the promising high speeds and immense amounts of unused bandwidth available. However, mmwaves suffer from unusually high attenuation, through free space, and especially through obstacles. The accepted way to avoid such attenuation is to break up the transmission path into multiple short hops, such that there are no obstacles between nodes. We extend the possibility of using a global positioning system (GPS) based, location aware, centralized approach to the problem of relay selection. We propose a simple learning based approach to detect the presence of static as well as dynamic obstacles, without having access to any data regarding their location and sizes. We then use this knowledge to efficiently select an appropriate relay for a UE, lowering the chance of allocating an obstacle prone link. Our proposed algorithm works even for UEs inside vehicles. We also propose a smart way of checking whether a pair of UEs is likely to be blocked, in real time. Finally we compare our relay selection algorithm with an existing algorithm and show that there is a significant improvement in the quality of link allocation.

Real-Time Avoidance Strategy of Dynamic Obstacles via Half Model-Free Detection and Tracking With 2D Lidar for Mobile Robots

Abstract: Avoidance is a necessary capability for a mobile robot to perform tasks, such as delivering objects in household or industrial scenarios. The existing avoidance strategy based on timed elastic band local planner and cost-map provided by robotics operating system cannot realize the excellent performance when a robot and an obstacle both move. In this article, we present a real-time, simple, and reliable approach to detecting and tracking obstacles via a two-dimensional lidar in dynamic scenarios where the mobile robot and the obstacle are moving. Obstacles are represented by a set of points against their outlines and the information of obstacles is initialized and updated via the raw laser measurement. First, the obstacle is detected by three main steps: preprocessing, segmentation, and merging, classification of consequent measurements. Second, we use a hierarchical method to realize data associations for figuring out the corresponding matches among obstacles with the consecutive time. Last, after doing the data association, we need to estimate the motion of the dynamic obstacle for being tracked by the Kalman filter. Extensive experiments performed in the simulation and practical scenarios indicate that the proposed method enables a mobile robot to perform dynamic avoidances efficiently [Real-time Avoidance Strategy of Dynamic Obstacles via Half Model-free Detection and Tracking (T-MECH)].

Deep Learning in Lane Marking Detection: A Survey

Abstract: Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning

Abstract: Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D environments where the robot has to solely rely on a single monocular camera. In particular, we are interested in solving this problem without relying on localization, mapping, or planning techniques. Most of the existing work consider obstacle avoidance as two separate problems, namely obstacle detection, and control. Inspired by the recent advantages of deep reinforcement learning in Atari games and understanding highly complex situations in Go, we tackle the obstacle avoidance problem as a data-driven end-to-end deep learning approach. Our approach takes raw images as input and generates control commands as output. We show that discrete action spaces are outperforming continuous control commands in terms of expected average reward in maze-like environments. Furthermore, we show how to accelerate the learning and increase the robustness of the policy by incorporating predicted depth maps by a generative adversarial network.