External force estimation and disturbance rejection for Micro Aerial Vehicles

Papadimitriou, Andreas; Jafari, Hedyeh; Mansouri, Sina Sharif; Nikolakopoulos, George

doi:10.1016/j.eswa.2022.116883

Cited by 15 publications

(5 citation statements)

References 23 publications

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“…Exploration algorithms like the frontier exploration algorithms [15], entropybased algorithms [16], and information-gain algorithms [17] provide a global planning strategy for the MAV, while an additional reactive control layer provides a local obstacle avoidance to prevent collisions with the environment. The most widely used reactive control layer is the artificial potential fields [18], while another approach that has received attention in the last years is the NMPC [19], which has been applied to perform real-time obstacle avoidance for MAVs [20] and disturbance rejection [21]. However, all of these methods consider fixed time steps, while in real-life applications and especially in networked enabled MAVs the feature of a time-varying path planning and a corresponding controller that can take into consideration time variations is vital for collision-free navigation.…”

Section: Background and Motivationmentioning

confidence: 99%

Multi-Stage NMPC for a MAV based Collision Free Navigation Under Varying Communication Delays

Papadimitriou

Jafari²,

Mansouri³

et al. 2023

J Intell Robot Syst

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Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to guarantee collision-free Micro Aerial Vehicles (MAVs) navigation. This study introduces a novel approach that considers different sampling times by a tree of discretization scenarios contrary to the existing typical multi-stage NMPC where system uncertainties are modeled by a tree of scenarios. Additionally, the proposed method considers adaptive weights for the multi-stage NMPC scenarios based on the probability of time delays in the communication link. As a result of the multi-stage NMPC, the obtained optimal control action is valid for multiple sampling times. Finally, the overall effectiveness of the proposed novel control framework is demonstrated in various tests and different simulation environments.

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Section: Background and Motivationmentioning

confidence: 99%

Multi-Stage NMPC for a MAV based Collision Free Navigation Under Varying Communication Delays

Papadimitriou

Jafari²,

Mansouri³

et al. 2023

J Intell Robot Syst

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“…Thus, vibrations induced by on-board motors or external disturbances caused by wind-gusts acting on the UAV can impact mission performance. Through the use of a gimbal mechanism and an external observer as in [43], external influences can be isolated and avoided. External uncertainties in localization is a primary concern in the field of robotics and can affect mission performance.…”

Section: Limitationsmentioning

confidence: 99%

Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach

Viswanathan,

Lindqvist,

Satpute

et al. 2023

J Intell Robot Syst

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This paper highlights the significance of maintaining and enhancing situational awareness in Urban Search and Rescue (USAR) missions. It focuses specifically on investigating the capabilities of Unmanned Aerial Vehicles (UAV) equipped with limited sensing capabilities and onboard computational resources to perform visual inspections of apriori unknown fractured and collapsed structures in unfamiliar environments. The proposed approach, referred to as First Look Inspect-Explore (FLIE), employs a flexible bifurcated behavior tree that leverages real-time RGB image and depth cloud data. By employing a recursive and reactive synthesis of safe view pose within the inspection module, FLIE incorporates a novel active visual guidance scheme for identifying previously inspected surfaces. Furthermore, the integration of a tiered hierarchical exploration module with the visual guidance system enables the UAV to navigate towards new and unexplored structures without relying on a map. This decoupling reduces memory overhead and computational effort by eliminating the need to plan based on an incrementally built, error-prone global map. The proposed autonomy is extensively evaluated through simulation and experimental verification under various scenarios and compared against state-of-art approaches, demonstrating its performance and effectiveness.

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“…KF based estimators may have slow convergence for rapid changes in state, and only consider one measurement for each estimation iteration. Nonlinear Moving Horizon Estimation (NMHE) methods are also getting more attention [17], [18], [19] for their ability to estimate complex nonlinear dynamic models, while they can handle inequality constraints. MHE method uses a moving time window to iteratively estimate the states of a nonlinear dynamic system, providing real-time updates as new measurements become available.…”

Section: A Background and Motivationmentioning

confidence: 99%

Semantic 3D Grid Maps for Autonomous Driving

Khoche

Wozniak

Duberg

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Data annotation in autonomous vehicles is a critical step in the development of Deep Neural Network (DNN) based models or the performance evaluation of the perception system. This often takes the form of adding 3D bounding boxes on time-sequential and registered series of point-sets captured from active sensors like Light Detection and Ranging (LiDAR) and Radio Detection and Ranging (RADAR). When annotating multiple active sensors, there is a need to motion compensate and translate the points to a consistent coordinate frame and timestamp respectively. However, highly dynamic objects pose a unique challenge, as they can appear at different timestamps in each sensor's data. Without knowing the speed of the objects, their position appears to be different in different sensor outputs. Thus, even after motion compensation, highly dynamic objects are not matched from multiple sensors in the same frame, and human annotators struggle to add unique bounding boxes that capture all objects. This article focuses on addressing this challenge, primarily within the context of Scania-collected datasets. The proposed solution takes a track of an annotated object as input and uses the Moving Horizon Estimation (MHE) to robustly estimate its speed. The estimated speed profile is utilized to correct the position of the annotated box and add boxes to object clusters missed by the original annotation.

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External force estimation and disturbance rejection for Micro Aerial Vehicles

Cited by 15 publications

References 23 publications

Multi-Stage NMPC for a MAV based Collision Free Navigation Under Varying Communication Delays

Multi-Stage NMPC for a MAV based Collision Free Navigation Under Varying Communication Delays

Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach

Semantic 3D Grid Maps for Autonomous Driving

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