scholarly journals An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 950 ◽  
Author(s):  
Jianfeng Liu ◽  
Jiexin Pu ◽  
Lifan Sun ◽  
Zishu He
Keyword(s):  
Mobile Robots ◽  
Difficult Problem ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Problem Of Time ◽  
Positioning Errors ◽  
Multipath Effects ◽  
Effective System ◽  
Adaptive Adjustment

The key to successful positioning of autonomous mobile robots in complicated indoor environments lies in the strong anti-interference of the positioning system and accurate measurements from sensors. Inertial navigation systems (INS) are widely used for indoor mobile robots because they are not susceptible to external interferences and work properly, but the positioning errors may be accumulated over time. Thus ultra wideband (UWB) is usually adopted to compensate the accumulated errors due to its high ranging precision. Unfortunately, UWB is easily affected by the multipath effects and non-line-of-sight (NLOS) factor in complex indoor environments, which may degrade the positioning performance. To solve above problems, this paper proposes an effective system framework of INS/UWB integrated positioning for autonomous indoor mobile robots, in which our modeling approach is simple to implement and a Sage–Husa fuzzy adaptive filter (SHFAF) is proposed. Due to the favorable property (i.e., self-adaptive adjustment) of SHFAF, the difficult problem of time-varying noise in complex indoor environments is considered and solved explicitly. Moreover, outliers can be detected and corrected by the proposed sliding window estimation with fading coefficients. This facilitates the positioning performance improvement for indoor mobile robots. The benefits of what we propose are illustrated by not only simulations but more importantly experimental results.

scholarly journals Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 346
Author(s):  
Zhenjie Ma ◽  
Wenjun Zhang ◽  
Ke Shi
Keyword(s):  
Deep Learning ◽  
Indoor Positioning ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Job Allocation ◽  
The Real ◽  
Positioning Errors ◽  
Wireless Signal ◽  
Movement Distance ◽  
Status Recognition

As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved can be estimated from these data traces. These estimates are very useful in many applications such as workload statistics and optimized job allocation in the field of logistics. However, due to the uncertainties of the wireless signal and corresponding positioning errors, accurately estimating movement distance still faces challenges. To address this issue, this paper proposes a movement status recognition-based distance estimating method to improve the accuracy. We divide the positioning traces into segments and use an encoder–decoder deep learning-based model to determine the motion status of each segment. Then, the distances of these segments are calculated by different distance estimating methods based on their movement statuses. The experiments on the real positioning traces demonstrate the proposed method can precisely identify the movement status and significantly improve the distance estimating accuracy.

scholarly journals Integrated IMU with Faster R-CNN Aided Visual Measurements from IP Cameras for Indoor Positioning

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3134 ◽  
Author(s):  
Lin Zhang ◽  
Taoyun Zhou ◽  
Baowang Lian
Keyword(s):  
Indoor Positioning ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Positioning Errors ◽  
Measurement Units ◽  
Stationary Obstacles ◽  
Indoor Scenarios ◽  
Visual Measurements

Considering the radio-based indoor positioning system pertaining to signal degradation due to the environmental factors, and rising popularity of IP (Internet Protocol) cameras in cities, a novel fusion of inertial measurement units (IMUs) with external IP cameras to determine the positions of moving users in indoor environments is presented. This approach uses a fine-tuned Faster R-CNN (Region Convolutional Neural Network) to detect users in images captured by cameras, and acquires visual measurements including ranges and angles of users with respect to the cameras based on the proposed monocular vision relatively measuring (MVRM) method. The results are determined by integrating the positions predicted by each user’s inertial measurement unit (IMU) and visual measurements using an EKF (Extended Kalman Filter). The results experimentally show that the ranging accuracy is affected by both the detected bounding box’s by Faster R-CNN height errors and diverse measuring distances, however, the heading accuracy is solely interfered with bounding box’s horizontal biases. The indoor obstacles including stationary obstacles and a pedestrian in our tests more significantly decrease the accuracy of ranging than that of heading, and the effect of a pedestrian on the heading errors is greater than stationary obstacles on that. We implemented a positioning test for a single user and an external camera in five indoor scenarios to evaluate the performance. The robust fused IMU/MVRM solution significantly decreases the positioning errors and shows better performance in dense multipath scenarios compared with the pure MVRM solution and ultra-wideband (UWB) solution.

scholarly journals Robust TOA-Based UAS Navigation under Model Mismatch in GNSS-Denied Harsh Environments

2020 ◽  
Vol 12 (18) ◽  
pp. 2928
Author(s):  
Jan Mortier ◽  
Gaël Pagès ◽  
Jordi Vilà-Valls
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Systems ◽  
Real Life ◽  
Transportation Systems ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
Navigation Systems ◽  
Satellite Systems

Global Navigation Satellite Systems (GNSS) is the technology of choice for outdoor positioning purposes but has many limitations when used in safety-critical applications such Intelligent Transportation Systems (ITS) and Unmanned Autonomous Systems (UAS). Namely, its performance clearly degrades in harsh propagation conditions and is not reliable due to possible attacks or interference. Moreover, GNSS signals may not be available in the so-called GNSS-denied environments, such as deep urban canyons or indoors, and standard GNSS architectures do not provide the precision needed in ITS. Among the different alternatives, cellular signals (LTE/5G) may provide coverage in constrained urban environments and Ultra-Wideband (UWB) ranging is a promising solution to achieve high positioning accuracy. The key points impacting any time-of-arrival (TOA)-based navigation system are (i) the transmitters’ geometry, (ii) a perfectly known transmitters’ position, and (iii) the environment. In this contribution, we analyze the performance loss of alternative TOA-based navigation systems in real-life applications where we may have both transmitters’ position mismatch, harsh propagation environments, and GNSS-denied conditions. In addition, we propose new robust filtering methods able to cope with both effects up to a certain extent. Illustrative results in realistic scenarios are provided to support the discussion and show the performance improvement brought by the new methodologies with respect to the state-of-the-art.

UWB Positioning Systsem Design: Selection of Modulation and Multiple Access Schemes

2007 ◽  
Vol 61 (1) ◽  
pp. 45-62 ◽  
Author(s):  
Hui Yu ◽  
Enrique Aguado ◽  
Gary Brodin ◽  
John Cooper ◽  
David Walsh ◽  
...  
Keyword(s):  
Multiple Access ◽  
High Performance ◽  
Ultra Wideband ◽  
Good Time ◽  
Modulation Scheme ◽  
Positioning System ◽  
Indoor Environments ◽  
Signal Design ◽  
Positioning Systems ◽  
Design Selection

In densely-populated cities or indoor environments, limited visibility to satellites and severe multipath effects significantly affect the accuracy and reliability of satellite-based positioning systems. To meet the needs of “seamless navigation” in these challenging environments an advanced terrestrial positioning system is under development. This system is based upon Ultra-Wideband (UWB) technology, which is a promising candidate for this application due to good time domain resolution and immunity to multipath. This paper presents a detailed analysis of two key aspects of the UWB signal design that will allow it to be used as the basis of such a high performance positioning system: the modulation scheme and the multiple access technique. These two aspects are evaluated in terms of spectral efficiency and synchronisation performance over multipath channels. Thus this paper identifies optimal modulation and multiple access techniques for a long range, high performance terrestrial positioning system using UWB.

Constructing Floor Plan through Smoke Using Ultra Wideband Radar

2021 ◽  
Vol 5 (4) ◽  
pp. 1-29
Author(s):  
Weiyan Chen ◽  
Fusang Zhang ◽  
Tao Gu ◽  
Kexing Zhou ◽  
Zixuan Huo ◽  
...  
Keyword(s):  
Spatial Information ◽  
Low Cost ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Floor Plan ◽  
Construction Methods ◽  
Median Error ◽  
Indoor Scenarios

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

scholarly journals The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

2020 ◽  
Vol 69 ◽  
pp. 471-500
Author(s):  
Shih-Yun Lo ◽  
Shiqi Zhang ◽  
Peter Stone
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Large Scale ◽  
Accurate Estimate ◽  
Indoor Environments ◽  
Task Planning ◽  
Trade Offs ◽  
High Level ◽  
Task Level

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

scholarly journals Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 954
Author(s):  
Abhijeet Ravankar ◽  
Ankit A. Ravankar ◽  
Arpit Rawankar ◽  
Yohei Hoshino
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Robot Navigation ◽  
Indoor Environments ◽  
Safe Distance ◽  
Field Robots ◽  
Safe Navigation

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

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