Unmanned Aerial Vehicle Path Planning for Exploration Mapping

Respall, Victor Massagué; Devitt, Dmitry; Fedorenko, Roman

doi:10.1109/nir50484.2020.9290232

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Because of the advantages mentioned above, many methods have been proposed to extend sensor FoVs on UAVs. One method is to use a sensor with a large FoV, such as a fisheye camera (8), catadioptric camera (9), and 360°LiDAR (10). However, for the fisheye camera and the catadioptric camera, obvious distortions often occur and must be properly compensated.…”

Section: Methods For Extending Sensor Fovs On Uavsmentioning

confidence: 99%

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

et al. 2023

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Uncrewed aerial vehicles (UAVs) rely heavily on visual sensors to perceive obstacles and explore environments. Current UAVs are limited in both perception capability and task efficiency because of a small sensor field of view (FoV). One solution could be to leverage self-rotation in UAVs to extend the sensor FoV without consuming extra power. This natural mechanism, induced by the counter-torque of the UAV motor, has rarely been exploited by existing autonomous UAVs because of the difficulties in design and control due to highly coupled and nonlinear dynamics and the challenges in navigation brought by the high-rate self-rotation. Here, we present powered-flying ultra-underactuated LiDAR (light detection and ranging) sensing aerial robot (PULSAR), an agile and self-rotating UAV whose three-dimensional position is fully controlled by actuating only one motor to obtain the required thrust and moment. The use of a single actuator effectively reduces the energy loss in powered flights. Consequently, PULSAR consumes 26.7% less power than the benchmarked quadrotor with the same total propeller disk area and avionic payloads while retaining a good level of agility. Augmented by an onboard LiDAR sensor, PULSAR can perform autonomous navigation in unknown environments and detect both static and dynamic obstacles in panoramic views without any external instruments. We report the experiments of PULSAR in environment exploration and multidirectional dynamic obstacle avoidance with the extended FoV via self-rotation, which could lead to increased perception capability, task efficiency, and flight safety.

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Section: Methods For Extending Sensor Fovs On Uavsmentioning

confidence: 99%

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

et al. 2023

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“…Proximity graphs are a practical class of graphs with applications in multiple areas. For example, they are used for motion planning, as rapidly exploring random trees in [1,2] and minimum spanning trees in clustering [3]. Most importantly, they lay in the core of log(|V|) search time data structures for large-scale multidimensional data indexing, where |V| stands for dataset cardinality.…”

Section: Introductionmentioning

confidence: 99%

Using Proximity Graph Cut for Fast and Robust Instance‐Based Classification in Large Datasets

Protasov

Khan

2021

Complexity

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K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work presents the design and implementation of a classification algorithm with index data structures, which would allow us to build fast and scalable solutions for large multidimensional datasets. We propose a novel approach that uses navigable small-world (NSW) proximity graph representation of large-scale datasets. Our approach shows 2–4 times classification speedup for both average and 99th percentile time with asymptotically close classification accuracy compared to the 1-NN method. We observe two orders of magnitude better classification time in cases when method uses swap memory. We show that NSW graph used in our method outperforms other proximity graphs in classification accuracy. Our results suggest that the algorithm can be used in large-scale applications for fast and robust classification, especially when the search index is already constructed for the data.

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Exploration and mapping of an indoor environment using Multirotor Aerial Vehicle

Hassan

Kulathunga

Klimchik

2021

2021 International Conference "Nonlinearity, Information and Robotics" (NIR)

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Unmanned Aerial Vehicle Path Planning for Exploration Mapping

Cited by 3 publications

References 22 publications

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

Using Proximity Graph Cut for Fast and Robust Instance‐Based Classification in Large Datasets

Exploration and mapping of an indoor environment using Multirotor Aerial Vehicle

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