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The collaborative visual perception of multiple Unmanned Aerial Vehicles (UAVs) has increasingly become a research hotspot. Compared to a single UAV equipped with a short-baseline stereo camera, multi-UAV collaborative vision offers a wide…

机器人学 · 计算机科学 2024-12-12 Zhaoying Wang , Wei Dong

Small-sized unmanned aerial vehicles (UAVs) have been widely investigated for use in a variety of applications such as remote sensing and aerial surveying. Direct three-dimensional (3D) mapping using a small-sized UAV equipped with a laser…

机器人学 · 计算机科学 2024-08-13 Taro Suzuki , Daichi Inoue , Yoshiharu Amano

This paper develops a robust extended Kalman filter to estimate the rotor angles and the rotor speeds of synchronous generators of a multimachine power system. Using a batch-mode regression form, the filter processes together predicted…

系统与控制 · 电气工程与系统科学 2021-04-06 Marcos Netto , Junbo Zhao , Lamine Mili

This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and…

机器人学 · 计算机科学 2022-05-05 Eduardo Gallo , Antonio Barrientos

Fault tolerance is achieved through multiply redundant hardware systems in large civil aircraft. This means of achieving fault tolerance is infeasible for small compact unmanned aerial vehicles. In this paper we apply a fault tolerant…

最优化与控制 · 数学 2016-10-12 Rudaba Khan , Paul Williams , Paul Riseborough , Asha Rao , Robin Hill

Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise that invalidate the stationary covariance assumptions of classical Kalman filters. The Sage-Husa Kalman Filter (SHKF)…

信号处理 · 电气工程与系统科学 2026-05-19 Kenan Majewski , Marcin Żugaj

Inertial motion capture systems widely use low-cost IMUs to obtain the orientation of human body segments, but these sensors alone are unable to estimate link positions. Therefore, this research used a SLAM method in conjunction with…

机器人学 · 计算机科学 2024-02-16 Mohammad Mahdi Azarbeik , Hamidreza Razavi , Kaveh Merat , Hassan Salarieh

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this…

信号处理 · 电气工程与系统科学 2023-04-12 Mengwei Sun , Mike E. Davies , Ian K. Proudler , James R. Hopgood

This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust…

Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and communities. In this context, the detection and localization of UAVs will be critical for maintaining safe and secure…

信号处理 · 电气工程与系统科学 2021-09-06 Udita Bhattacherjee , Ender Ozturk , Ozgur Ozdemir , Ismail Guvenc , Mihail L. Sichitiu , Huaiyu Dai

In the field of sensor fusion and state estimation for object detection and localization, ensuring accurate tracking in dynamic environments poses significant challenges. Traditional methods like the Kalman Filter (KF) often fail when…

机器人学 · 计算机科学 2024-10-15 Khaled Gabr , Mohamed Abdelkader , Imen Jarraya , Abdullah AlMusalami , Anis Koubaa

This letter proposes a reactive navigation strategy for recovering the altitude, translational velocity and orientation of Micro Aerial Vehicles. The main contribution lies in the direct and tight fusion of Inertial Measurement Unit (IMU)…

机器人学 · 计算机科学 2020-01-16 Shangkun Zhong , Pakpong Chirarattananon

Autonomous landing systems for Micro Aerial Vehicles (MAV) have been proposed using various combinations of GPS-based, vision, and fiducial tag-based schemes. Landing is a critical activity that a MAV performs and poor resolution of GPS,…

机器人学 · 计算机科学 2021-10-01 Pranay Mathur , Yash Jangir , Neena Goveas

Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracking of adversarial unmanned aerial vehicles (UAVs) is especially challenging due to agile…

机器学习 · 计算机科学 2026-05-01 Akhil Gupta , Erhan Guven

We propose a new position control strategy for VTOL-UAVs using IMU and GPS measurements. Since there is no sensor that measures the attitude, our approach does not rely on the knowledge (or reconstruction) of the system orientation as…

最优化与控制 · 数学 2012-03-22 Andrew Roberts , Abdelhamid Tayebi

Autonomous proximity operations, such as active debris removal and on-orbit servicing, require high-fidelity relative navigation solutions that remain robust in the presence of parametric uncertainty. Standard estimation frameworks…

机器人学 · 计算机科学 2026-03-31 Batu Candan , Simone Servadio

The robustness and accuracy of a vision system for motion estimation of a tumbling target satellite are enhanced by an adaptive Kalman filter. This allows a vision-guided robot to complete the grasping of the target even if occlusion occurs…

机器人学 · 计算机科学 2022-11-08 Farhad Aghili

It's a practical approach using the ground-aerial collaborative system to enhance the localization robustness of flying robots in cluttered environments, especially when visual sensors degrade. Conventional approaches estimate the flying…

机器人学 · 计算机科学 2025-12-19 Sijia Chen , Wei Dong

Indoor tracking and pose estimation, i.e., determining the position and orientation of a moving target, are increasingly important due to their numerous applications. While Inertial Navigation Systems (INS) provide high update rates, their…

机器人学 · 计算机科学 2024-09-04 Mohammed H. AlSharif , Mohanad Ahmed , Mohamed Siala , Tareq Y. Al-Naffouri

For intelligent quadcopter UAVs, a robust and reliable autonomous planning system is crucial. Most current trajectory planning methods for UAVs are suitable for static environments but struggle to handle dynamic obstacles, which can pose…

机器人学 · 计算机科学 2023-12-29 Jiageng Zhong , Ming Li , Yinliang Chen , Zihang Wei , Fan Yang , Haoran Shen