Knowledge Transfer Between Different UAVs for Trajectory Tracking

Chen, Zhu; Liang, Xiao; Zheng, Minghui

doi:10.1109/lra.2020.3004776

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…In Kentsch et al (2020) , the authors study and quantify issues related to the use of transfer learning approaches in their own UAV-acquired images in forestry applications. Chen, Liang & Zheng (2020) propose a learning algorithm that enables a quadrotor unmanned aerial vehicle to automatically improve its tracking performance by learning from the tracking errors made by other UAVs with different dynamics.…”

Section: Related Workmentioning

confidence: 99%

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation

Barreiro

Lara

Manrique

et al. 2023

PeerJ Computer Science

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In the field of artificial intelligence (AI) one of the main challenges today is to make the knowledge acquired when performing a certain task in a given scenario applicable to similar yet different tasks to be performed with a certain degree of precision in other environments. This idea of knowledge portability is of great use in Cyber-Physical Systems (CPS) that face important challenges in terms of reliability and autonomy. This article presents a CPS where unmanned vehicles (drones) are equipped with a reinforcement learning system so they may automatically learn to perform various navigation tasks in environments with physical obstacles. The implemented system is capable of isolating the agents’ knowledge and transferring it to other agents that do not have prior knowledge of their environment so they may successfully navigate environments with obstacles. A complete study has been performed to ascertain the degree to which the knowledge obtained by an agent in a scenario may be successfully transferred to other agents in order to perform tasks in other scenarios without prior knowledge of the same, obtaining positive results in terms of the success rate and learning time required to complete the task set in each case. In particular, those two indicators showed better results (higher success rate and lower learning time) with our proposal compared to the baseline in 47 out of the 60 tests conducted (78.3%).

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Section: Related Workmentioning

confidence: 99%

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation

Barreiro

Lara

Manrique

et al. 2023

PeerJ Computer Science

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Section: Introductionmentioning

confidence: 99%

“…In recent years, unmanned aerial vehicles (UAVs) play an increasingly important role in civil, commercial and military fields, especially in search, rescue and monitoring of targets in marine environments. [1][2][3][4] The UAV search problems, including those utilizing a single UAV or multiple UAVs to search for static or dynamic targets, have been studied extensively. 5 However, due to the difficulty of the search missions, the complexity of marine environments, and limited capabilities of the sensors, it is often difficult for a single UAV to complete the mission effectively, so the collaboration of multiple UAVs has attracted more and more attention.…”

Section: Introductionmentioning

confidence: 99%

A new search scheme using multi‐bee‐colony elite learning method for unmanned aerial vehicles in unknown environments

Yue

Liu

et al. 2022

Optim Control Appl Methods

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In this research, a cooperative search for multiple dynamic targets in an unknown marine environment by multiple unmanned aerial vehicles is studied based on a novel multi‐bee‐colony (MBC) elite learning algorithm. First, a specialized searching model is established which includes the UAV dynamics, the sensor model, the target probability and environmental certainty at different flight altitudes. Then, a new search strategy, which consists of rough search and accurate search, is proposed by maximizing the multiobjective utility function with the dynamic changing of the flight altitude. In order to solve the optimization problem, an improved MBC algorithm based on elite learning is designed, which can improve the adaptability and computation speed of the standard artificial bee colony (ABC) algorithm under different search missions. Finally, extensive simulations are conducted to show the effectiveness and superiority of the proposed search strategy.

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“…In recent years, low-altitude unmanned aerial vehicles (UAVs), or drones, have been increasingly adopted in various commercial applications including but not limited to surveillance (Berrahal et al, 2016), wildlife photography (Joyce et al, 2019), warehouse management (Chen et al, n.d), structural health monitoring (Sajedi and Liang, 2020), wildlife population monitoring (Gonzalez et al, 2016), as well as oil spill mapping (Odonkor et al, n.d). In keeping with this exponential rise in demand for UAVs, there has been a growing body of work devoted to motion planning algorithms ranging from trajectory tracking (Adlakha and Zheng, 2020; Adlakha and Zheng, 2021; Chen et al, 2020), path planning (Modares et al, 2017), obstacle avoidance (Ramos et al, 2019), suspended payload stability (Mohammadi et al, 2020; Ogunbodede et al, 2019), and agile motion planning and control (Frazzoli et al, 2002). In tandem, engineers have been analyzing the spectral and spatial patterns associated with the acoustic signature of UAVs and studying the effects of noise emitted by such systems on humans and wildlife in different environments.…”

Section: Introductionmentioning

confidence: 99%

Integration of acoustic compliance and noise mitigation in path planning for drones in human–robot collaborative environments

Adlakha

Liu

Chowdhury

et al. 2022

Journal of Vibration and Control

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This work presents a framework aimed at mitigating adverse effects of high-amplitude drone noise ranging from hearing loss to reduced productivity in human–robot collaborative environments by infusing acoustic awareness in a path planning algorithm without imposing any additional design layers or hardware to an operational drone. Following a detailed outline of the proposed approach, it is shown that a significant reduction of noise levels perceived by human workers at noise-sensitive locations is realized via a path planner which generates optimal paths ranging from quietest to shortest paths. The approach is then augmented with a path-correction mechanism which accounts for noise exposure duration to ensure the aforementioned optimal paths are compliant with a given industrial/environmental standard. The correction mechanism enforces an adjustment of subsets of the planned paths inside quiet zones designated around noise-sensitive locations. The presented concepts were verified using numerical simulations conducted for a 2-dimensional rasterized obstacle field followed by a statistical design of experiments. The proposed framework is highly versatile and integrable with widely used industrial path planners, rendering it a highly valuable tool for noisy collaborative workplaces.

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Knowledge Transfer Between Different UAVs for Trajectory Tracking

Cited by 11 publications

References 33 publications

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation

A new search scheme using multi‐bee‐colony elite learning method for unmanned aerial vehicles in unknown environments

Integration of acoustic compliance and noise mitigation in path planning for drones in human–robot collaborative environments

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