An Efficient Framework for Autonomous UAV Missions in Partially-Unknown GNSS-Denied Environments

Mugnai, Michael; Losè, Massimo Teppati; Herrera-Alarcon, Edwin Paul; Baris, Gabriele; Satler, Massimo; Avizzano, Carlo Alberto

doi:10.3390/drones7070471

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…For UAV autonomous navigation in unknown environments, the planner not only needs to approach the target point but also needs to avoid obstacles that suddenly occur in the field of view. Although an efficient framework for partially unknown environments [27] has been proposed, most of the common outdoor task scenarios such as rescue and resource exploration need to face a fully unknown environment.…”

Section: Event-triggered Bi-level Hierarchical Planner (Ethp)mentioning

confidence: 99%

Event-Triggered Hierarchical Planner for Autonomous Navigation in Unknown Environment

Chen,

Song,

et al. 2023

Drones

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End-to-end deep neural network (DNN)-based motion planners have shown great potential in high-speed autonomous UAV flight. Yet, most existing methods only employ a single high-capacity DNN, which typically lacks generalization ability and suffers from high sample complexity. We propose a novel event-triggered hierarchical planner (ETHP), which exploits the bi-level optimization nature of the navigation task to achieve both efficient training and improved optimality. Specifically, we learn a depth-image-based end-to-end motion planner in a hierarchical reinforcement learning framework, where the high-level DNN is a reactive collision avoidance rerouter triggered by the clearance distance, and the low-level DNN is a goal-chaser that generates the heading and velocity references in real time. Our training considers the field-of-view constraint and explores the bi-level structural flexibility to promote the spatio–temporal optimality of planning. Moreover, we design simple yet effective rules to collect hindsight experience replay buffers, yielding more high-quality samples and faster convergence. The experiments show that, compared with a single-DNN baseline planner, ETHP significantly improves the success rate and generalizes better to the unseen environment.

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Section: Event-triggered Bi-level Hierarchical Planner (Ethp)mentioning

confidence: 99%

Event-Triggered Hierarchical Planner for Autonomous Navigation in Unknown Environment

Chen,

Song,

et al. 2023

Drones

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“…It is important to note that most research in this field focuses on multirotor UAVs. Among these, solutions typically include cameras [15], LIDAR technology [16], or both [17]. That was not the case for the solution presented in [18], where the solution was specifically designed to map indoor spaces with planar structures through graph optimization.…”

Section: Introductionmentioning

confidence: 99%

“…[5,15] m/s {0, 1, 2} {0, 1, 2} [0.5, 2] m [5, 15] m/s [0, 90] • Figure 11. Randomly generated collision scenario.…”

mentioning

confidence: 99%

Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs

Portugal,

Marta

2023

Modelling

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The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°.

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“…In recent years, the development of information technology has greatly promoted the application of UAVs (also know as drones) in many fields [1,2], such as public safety, environmental monitoring, military strikes, etc. The application of UAV platforms can significantly improve the efficiency of traditional business operations, reduce business costs, or enhance the security capabilities of business operations [3,4]. The task planning and trajectory setting for integrating the main body of UAVs and their many characteristics is of great significance for the operational efficiency of UAV platforms [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Path Planning Method for the Unmanned Aerial Vehicle in Highway Inspection Scenarios

Li,

Gao,

Liu

et al. 2023

Electronics

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Unmanned aerial vehicles (UAVs) have received widespread attention due to their flexible deployment characteristics. Automated airports equipped with UAVs are expected to become important equipment for improving quality and reducing costs in many inspection scenarios. This paper focuses on the automated inspection business of UAVs dispatched by automated airports in highway scenarios. On the basis of considering the shape of highway curves, inspection targets, and the energy consumption characteristics of UAVs, planning the flight parameters of UAVs is of great significance for ensuring the effectiveness of the inspection process. This paper first sets the inspection path points for the UAV based on highway curves, and then proposes an efficient heuristic method for the nonlinear non-convex parameter optimization problem, through which the parameters of the UAV’s inspection altitude, hovering altitude, and flight speed are planned. Simulation and analysis show that the proposed method possesses good parameter planning efficiency. By combining several existing trajectory planning methods, e.g., the traversal method, the deep Q-network based method, and the genetic method, it can be concluded that the proposed method in this paper has better overall planning performance including planning efficiency and inspection effectiveness.

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An Efficient Framework for Autonomous UAV Missions in Partially-Unknown GNSS-Denied Environments

Cited by 6 publications

References 42 publications

Event-Triggered Hierarchical Planner for Autonomous Navigation in Unknown Environment

Event-Triggered Hierarchical Planner for Autonomous Navigation in Unknown Environment

Optimal Multi-Sensor Obstacle Detection System for Small Fixed-Wing UAVs

An Efficient Path Planning Method for the Unmanned Aerial Vehicle in Highway Inspection Scenarios

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