This paper presents a systematic design of an active disturbance rejection control (ADRC) system for unmanned tracked vehicles (UTVs) in leader-follow formation. Two ADRC controllers are designed for the lateral and the longitudinal channels of the UTV based on control errors in the cross-track and the along-track directions. Through simulations, the proposed ADRC approach is first shown to outperform the conventional PI/PID controllers in scenarios involving sudden changes in the leader motion dynamics, slippage disturbances, and measurement noise. Then, a comprehensive experimental validation of the proposed leader–follower control is performed using a laboratory UTV equipped with a camera and laser sensors (to enable the calculation of error signals). In order to provide more effective interaction between the human (leader) and the UTV (follower) during the leader–follower task, a camera-based subsystem for human pose recognition is developed and deployed. Finally, the experimental results obtained outdoors demonstrate that the proposed ADRC-based leader–follower UTV control system achieves high tracking capabilities, robustness against slippage disturbances, and adaptability to changing environmental conditions. 

Active disturbance rejection control for unmanned tracked vehicles in leader-follower scenarios: discrete-time implementation and field test validation

Recently, person-following robots have been increasingly used in many real-world applications, and they require robust and accurate person identification for tracking. Recent works proposed to use re-identification metrics for identification of the target person; however, these metrics suffer due to poor generalization, and due to impostors in nonlinear multi-modal world. This work learns a domain generic person re-identification to resolve real-world challenges and to identify the target person undergoing appearance changes when moving across different indoor and outdoor environments or domains. Our generic metric takes advantage of novel attention mechanism to learn deep cross-representations to address pose, viewpoint, and illumination variations, as well as jointly tackling impostors and style variations the target person randomly undergoes in various indoor and outdoor domains; thus, our generic metric attains higher recognition accuracy of target person identification in complex multi-modal open-set world, and attains 80.73% and 64.44% Rank-1 identification in multi-modal close-set PRID and VIPeR domains, respectively.

/pdf/lightweight-multimodal-domain-generic-person-2qvnc0ii.pdf

Lightweight Multimodal Domain Generic Person Reidentification Metric for Person-Following Robots

This article presents a gait monitoring method for the home-based rehabilitation of Parkinson’s disease using a following robot. We use robot-based 2-D light detection and ranging (LiDAR) to collect environmental point clouds. The point clouds of the monitored person’s legs are identified from the static environment by an intersection of union (IOU) matching algorithm and the legs are continuously tracked with the Kalman filter (KF). The spatiotemporal information of gait is subsequently determined by legs segmentation. During this process, the robot follows behind the person along the trajectory just walked by the person, ensuring that both legs are always in the robot’s view. For Parkinson’s abnormal gait during rehabilitation, we propose a quantitative indicator to assess the patient’s recovery status. The experimental results demonstrate that the proposed method can effectively monitor and score the abnormality of Parkinson’s gait. In terms of gait detection, it achieves an F1 detection score of 0.983 and a root mean square error (RMSE) within 0.050 m while significantly extending the monitoring radius to 10 m of the room. This makes the method practical for home rehabilitation applications.

Quantitative Measurement of Parkinson’s Disease Gait Based on the Rehabilitation Monitoring Robot

Abstract Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics. 

Fusion of Time-of-Flight Based Sensors with Monocular Cameras for a Robotic Person Follower

With the progress of science and technology and the development of economy, more and more robots have entered the family and become good helpers and partners in people’s daily life. As a good partner of human beings, the robot is endowed with humanized emotion and decision-making core, and has harmonious and natural social interaction ability. Among them, the socially concomitant navigation function is a very important social function. For indoor mobile robot to navigate socially with its human companions, a Single-Object Tracking deepsort(SOT deepsort)algorithm is proposed, and a fast, stable and simple socially concomitant navigation system is proposed. In order to make the robot more socialized, questionnaires are designed to investigate the comfortable distance of the robot in the process of following its companion. Endow the robot appropriate behavior when traveling with human companions, so that the robot can play the role of accompanying the pedestrian and complete the task of following the companion. Finally, the proposed system is tested on Fabo mobile robot. The experimental results show that the following accuracy of this method is high and meets the real-time requirements. 

Indoor Mobile Robot Socially Concomitant Navigation System

This paper proposes a person-following method based on monocular vision, which allows quadruped robots to track a target person in both indoor and outdoor environments with different illumination conditions. Our method is composed of a person detector, a Kalman filter (KF) tracker, and a re-identification module. To be more specific, the person detector uses a human pose estimation method to detect persons. The KF is applied to predict the position of the target person and update its state with detection results. A re-identification module is proposed to deal with distractions, where the Convolutional Channel Features (CCF) is used to extract appearance features and Online Boosting is used to distinguish the target person from others. Especially, we design a target recapture mechanism based on the Recurrent Neural Network (RNN). Combining motion information with appearance features, the system can accurately re-identify the target person. Without extra customized markers, our method can track the target person steadily in real-time only using a monocular camera. Experiments results can validate the robustness and effectiveness of the proposed method. 

A person-following method based on monocular camera for quadruped robots

This article presents an online light detection and ranging (LiDAR)-only person-following (LoPF) framework, which can be applied in both indoor and outdoor environments with only sparse LiDAR point cloud data as input. The framework consists of two cascade backbone procedures. The former is pedestrian detection, which is developed for classifying a set of human clusters from noisy and sparse LiDAR point clouds. An originally designed voxel feature is integrated for depicting the 3-D information of each potential cluster. A U-shaped convolutional neural network (CNN) is proposed to achieve human cluster detection. Then the latter procedure, pedestrian tracking, is developed to follow the target. Specifically, to achieve long-term person-following, an unscented Kalman filter (UKF), a position filter, and a target reidentification module are combined. The reidentification module contains a specifically designed support vector machine (SVM) target classifier. The developed framework is verified by extensive real-world experiments in outdoor environments using mobile robots. Compared with the state of the art, our method is better in target identification and tracking, realizing better performance in the person-following.

LoPF: An Online LiDAR-Only Person-Following Framework

Autonomous exploration is the prerequisite for reli-able robot navigation in unknown environments via providing the necessary information. The goal is to build a precise environment map while at the same time locating the robot robustly. To achieve this goal, this paper develops an information map that jointly provides the information of mapping and localization uncertainty. It consists of a localization uncertainty map and a model uncertainty map. The former evaluates the landmarks around the robot and the latter indicates the map completeness based on information theory. The information map serves as the input for making better decisions of where-to-go to achieve higher localization and mapping performance. To improve the efficiency of information map prediction, we develop a learning-based map prediction framework, where a dataset is established autonomously. The effectiveness and efficiency of the developed framework are verified through experiments in simulation environments.

Information Map Prediction based on Learning Network for Reinforced Autonomous Exploration

This study focuses on localization and mapping for UGVs when they are deployed in environments with non-level roads. In these scenarios, the vehicles need to travel through flat but not necessarily level grounds, i.e., ascent or descent, which may cause drifts of the robot pose and distortion of the map. We develop a low-drift LiDAR odometry and mapping approach for the UGV with LiDAR as the only exteroceptive sensor. A factor-graph based pose optimization method is developed with a specifically designed factor named slope factor. This factor includes the slope information that is estimated from a real-time LiDAR data stream. The slope information is also used to enhance the loop-closure detection procedure. Moreover, an incremental pitch estimation mechanism is designed to achieve further pose estimation refinement. We demonstrate the effectiveness of the developed framework in real-world environments. The odometry drift is lower and the map is more precise than experiments with the state-of-the-arts. Notably, on the Kitti dataset, our method also exhibits convincing performance, demonstrating its strength in more general application scenarios.

Low-drift LiDAR-only Odometry and Mapping for UGVs in Environments with Non-level Roads

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiangyu Chen

Author Tools

Chat about Author

Papers

A person-following method based on monocular camera for quadruped robots

LoPF: An Online LiDAR-Only Person-Following Framework

Information Map Prediction based on Learning Network for Reinforced Autonomous Exploration

Low-drift LiDAR-only Odometry and Mapping for UGVs in Environments with Non-level Roads