Autonomous navigation of UAV in forest

Cui, Jinqiang; Lai, Shupeng; Dong, Xin; Liu, Peidong; Chen, Ben M.; Lee, Tong H.

doi:10.1109/icuas.2014.6842317

Cited by 29 publications

(21 citation statements)

References 12 publications

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“…In fact, most of the articles reviewed here used multi-rotor systems to monitor understorey vegetation (Table 1). Manoeuvrability is especially important in closed environments, where recent flights in foliage (forested) environments were possible using a quadcopter mounted with a Kalman filter and a 2D laser range finder, in combination with an algorithm used for real-time path planning, to assess obstacle presence and avoid them [54,55]. Cui [54] reported that their real-time object avoidance was possible through very slow flight speeds (0.5 m/s).…”

Section: Sfmmentioning

confidence: 99%

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Hernández-Santín

Rudge

Bartolo³

et al. 2019

Drones

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Understorey vegetation plays an important role in many ecosystems, yet identifying and monitoring understorey vegetation through remote sensing has proved a challenge for researchers and land managers because understorey plants tend to be small, spatially and spectrally similar, and are often blocked by the overstorey. The emergence of Unmanned Aerial Systems (UAS) is revolutionising how vegetation is measured, and may allow us to measure understorey species where traditional remote sensing previously could not. The goal of this paper was to review current literature and assess the current capability of UAS to identify and monitor understorey vegetation. From the literature, we focused on the technical attributes that limit the ability to monitor understorey vegetation—specifically (1) spatial resolution, (2) spectral sensitivity, (3) spatial extent, and (4) temporal frequency at which a sensor acquires data. We found that UAS have provided improved levels of spatial resolution, with authors reporting successful classifications of understorey vegetation at resolutions of between 3 mm and 200 mm. Species discrimination can be achieved by targeting flights to correspond with phenological events to allow the detection of species-specific differences. We provide recommendations as to how UAS attributes can be tailored to help identify and monitor understorey species.

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Section: Sfmmentioning

confidence: 99%

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Hernández-Santín

Rudge

Bartolo³

et al. 2019

Drones

View full text Add to dashboard Cite

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“…Sensors 2020, 20, 1890 3 of 21 In comparison, simultaneous localization and mapping (SLAM), which is one of the online-based methods, can successfully solve the AMP problem even when the environment is unknown [12][13][14][15]. Wang [12] presented a complete navigation system for an indoor UAV that was based on SLAM, and this system was able to ensure that the UAV could fly along the internal walls of a room without collisions.…”

Section: Related Workmentioning

confidence: 99%

“…Wang [12] presented a complete navigation system for an indoor UAV that was based on SLAM, and this system was able to ensure that the UAV could fly along the internal walls of a room without collisions. Cui [13] combined SLAM with an online path planning module to allow UAVs to navigate autonomously through a foliage environment. However, since SLAM needs to model the environment online first, if the environment changes, it could perform poorly.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Wan

Gao

et al. 2020

Sensors

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Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.

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“…The dynamics of the quadrotor consists of inner loop dynamics and outer loop dynamics. The inner loop dynamics relates the control input to the angular motion while the outer loop dynamics relates the angular motion to the linear velocity and position [2].…”

Section: System Structurementioning

confidence: 99%

“…While the inner loop dynamics is stabilized by the conventional PID controller, a Robust and Perfect Tracking (RPT) control law [3] is designed to control the UAV's outer loop to track the trajectory reference from the path planning module [2]. The RPT problem is to design a controller with the 978-1-4799-7862-5/15/$31.00 © 2015 IEEE The navigation state estimation framework consists of two parts: the front-end and the back-end.…”

Section: System Structurementioning

confidence: 99%

Relaxed formation in cluttered environments using rotor-craft UAV

Lai

Cui

Chen³

2015

2015 10th Asian Control Conference (ASCC)

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Though multi-agent formation and consensus have been studied intensively recently, very few of the research focuses on their engineering realization using rotor-craft unmanned air vehicle (UAV) as the basic platform. Some assumptions in many theoretical study present challenges in engineering realization, such as the over-simplified dynamic model or sensor model of each agent. In this paper, we present an engineering approach of path planning and trajectory generation based on the model predictive control (MPC) with implementations on small-size, low-cost rotor-craft UAV platform. The proposed method presents a practical solution to the low level path planning and obstacle avoidance problem while opening possibilities for other high level mission planning logic. Based on this method, a study on relaxed formation with two quad-rotor UAV in cluttered environment is presented without making any idealized assumptions in measurement, communication and control.

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Autonomous navigation of UAV in forest

Cited by 29 publications

References 12 publications

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Relaxed formation in cluttered environments using rotor-craft UAV

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