1
|
Zhang J, Guo J, Chai H, Zhang Q, Li Y, Wang Z, Zhang Q. A Day/Night Leader-Following Method Based on Adaptive Federated Filter for Quadruped Robots. Biomimetics (Basel) 2023; 8:biomimetics8010020. [PMID: 36648806 PMCID: PMC9844425 DOI: 10.3390/biomimetics8010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
The quadruped robots have superior adaptability to complex terrains, compared with tracked and wheeled robots. Therefore, leader-following can help quadruped robots accomplish long-distance transportation tasks. However, long-term following has to face the change of day and night as well as the presence of interference. To solve this problem, we present a day/night leader-following method for quadruped robots toward robustness and fault-tolerant person following in complex environments. In this approach, we construct an Adaptive Federated Filter algorithm framework, which fuses the visual leader-following method and the LiDAR detection algorithm based on reflective intensity. Moreover, the framework uses the Kalman filter and adaptively adjusts the information sharing factor according to the light condition. In particular, the framework uses fault detection and multisensors information to stably achieve day/night leader-following. The approach is experimentally verified on the quadruped robot SDU-150 (Shandong University, Shandong, China). Extensive experiments reveal that robots can identify leaders stably and effectively indoors and outdoors with illumination variations and unknown interference day and night.
Collapse
Affiliation(s)
- Jialin Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250100, China
- Robotics Research Center, Shandong University, Jinan 250100, China
| | - Jiamin Guo
- School of Control Science and Engineering, Shandong University, Jinan 250100, China
- Robotics Research Center, Shandong University, Jinan 250100, China
| | - Hui Chai
- School of Control Science and Engineering, Shandong University, Jinan 250100, China
- Robotics Research Center, Shandong University, Jinan 250100, China
- Correspondence:
| | - Qin Zhang
- Robotics Research Center, Shandong University, Jinan 250100, China
- School of Electrical Engineering, University of Jinan, Jinan 250024, China
| | - Yibin Li
- School of Control Science and Engineering, Shandong University, Jinan 250100, China
- Robotics Research Center, Shandong University, Jinan 250100, China
| | - Zhiying Wang
- School of Control Science and Engineering, Shandong University, Jinan 250100, China
- Robotics Research Center, Shandong University, Jinan 250100, China
| | - Qifan Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250100, China
- Robotics Research Center, Shandong University, Jinan 250100, China
| |
Collapse
|
2
|
Mahlknecht F, Gehrig D, Nash J, Rockenbauer FM, Morrell B, Delaune J, Scaramuzza D. Exploring Event Camera-Based Odometry for Planetary Robots. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3187826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Florian Mahlknecht
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Daniel Gehrig
- Robotics and Perception Group, University of Zurich, Zurich, Switzerland
| | - Jeremy Nash
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | - Benjamin Morrell
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jeff Delaune
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Davide Scaramuzza
- Robotics and Perception Group, University of Zurich, Zurich, Switzerland
| |
Collapse
|
3
|
Peripheral-Free Calibration Method for Redundant IMUs Based on Array-Based Consumer-Grade MEMS Information Fusion. MICROMACHINES 2022; 13:mi13081214. [PMID: 36014135 PMCID: PMC9415369 DOI: 10.3390/mi13081214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/04/2022] [Accepted: 07/27/2022] [Indexed: 02/01/2023]
Abstract
The MEMS array-based inertial navigation module (M-IMU) reduces the measurement singularities of MEMS sensors by fusing multiple data processing to improve its navigation performance. However, there are still existing random and fixed errors in M-IMU navigation. The calibration method calibrates the fixed error parameters of M-IMU to further improve navigation accuracy. In this paper, we propose a low-cost and efficient calibration method to effectively estimate the fixed error parameters of M-IMU. Firstly, we manually rotate the M-IMU in multiple sets of different attitudes (stationary), then use the LM-calibration algorithm to optimize the cost function of the corresponding sensors in different intervals of the stationary-dynamic filter separation to obtain the fixed error parameters of MEMS, and finally, the global fixed error parameters of the M-IMU are calibrated by adaptive support fusion of the individual MEMS fixed error parameters based on the benchmark conversion. A comparison of the MEMS calibrated separately by the fusion-calibration algorithm and the LM-calibration algorithm verified that the calibrated MEMS array improved the measurement accuracy by about 10 db and reduced the dispersion of the output data by about 8 db compared to the individual MEMS in a multi-dimensional test environment, indicating the robustness and feasibility of the fusion calibration algorithm.
Collapse
|
4
|
Zhang L, Wisth D, Camurri M, Fallon M. Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3137910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
5
|
Lee J, Hanley D, Bretl T. Extrinsic Calibration of Multiple Inertial Sensors From Arbitrary Trajectories. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3143290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
6
|
Jung JH, Choe Y, Park CG. Photometric Visual-Inertial Navigation With Uncertainty-Aware Ensembles. IEEE T ROBOT 2022. [DOI: 10.1109/tro.2021.3139964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
7
|
An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM. SENSORS 2021; 21:s21082857. [PMID: 33921567 PMCID: PMC8073482 DOI: 10.3390/s21082857] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 11/16/2022]
Abstract
Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.
Collapse
|