Efficient Trajectory Library Filtering for Quadrotor Flight in Unknown Environments

Viswanathan, Vaibhav K.; Dexheimer, Eric; Li, Guanrui; Loianno, Giuseppe; Kaess, Michael; Scherer, Sebastian

doi:10.1109/iros45743.2020.9341273

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…As an effort to extend the online applicability of our prediction algorithm, the recent works [21], [22] on the motion planning to reach a static goal are noteworthy. They consider feasible motion candidates of the drone in an offline library and pre-compute the region which each candidate passes through.…”

Section: Forecasting Target Movementmentioning

confidence: 99%

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Autonomous Aerial Dual-Target Following Among Obstacles

et al. 2021

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In contrast to recent developments in online motion planning to follow a single target with a drone among obstacles, a multi-target case with a single chaser drone has been hardly discussed in similar settings. Following more than one target is challenged by multiple visibility issues due to the inter-target occlusion and the limited field-of-view in addition to the possible occlusion and collision with obstacles. Also, reflecting multiple targets into planning objectives or constraints increases the computation load and numerical issues in the optimization compared to the single target case. To resolve the issues, we first develop a visibility score field for multiple targets incorporating the field-of-view limit and inter-occlusion between targets. Next, we develop a fast sweeping algorithm used to compute the field for the suitability of real-time applications. Last, we build an efficient hierarchical planning pipeline to output a chasing motion for multiple targets ensuring key objectives and constraints. For reliable chasing, we also present a prediction algorithm to forecast the movement of targets considering obstacles. The online performance of the proposed algorithm is extensively validated in challenging scenarios, including a large-scale simulation, and multiple real-world experiments in indoor and outdoor scenes. The full code implementation of the proposed method is released here: https://github.com/icsl-Jeon/dual_chaser.

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Section: Forecasting Target Movementmentioning

confidence: 99%

“…In this work, the voxels are set to have the same resolution with the octomap. For a voxel v ∈ V, we can identify the primitives in L that intersect with v, and the offline library encodes this information in the form of bitset in a similar manner with [22].…”

Section: Forecasting Target Movementmentioning

confidence: 99%

Autonomous Aerial Dual-Target Following Among Obstacles

et al. 2021

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Design and Control of Drones

Mueller

Lee

D’Andrea

2022

Annu. Rev. Control Robot. Auton. Syst.

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The design and control of drones remain areas of active research, and here we review recent progress in this field. In this article, we discuss the design objectives and related physical scaling laws, focusing on energy consumption, agility and speed, and survivability and robustness. We divide the control of such vehicles into low-level stabilization and higher-level planning such as motion planning, and we argue that a highly relevant problem is the integration of sensing with control and planning. Lastly, we describe some vehicle morphologies and the trade-offs that they represent. We specifically compare multicopters with winged designs and consider the effects of multivehicle teams. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Efficient Trajectory Library Filtering for Quadrotor Flight in Unknown Environments

Cited by 2 publications

References 24 publications

Autonomous Aerial Dual-Target Following Among Obstacles

Autonomous Aerial Dual-Target Following Among Obstacles

Design and Control of Drones

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