Search-based Planning of Dynamic MAV Trajectories Using Local Multiresolution State Lattices

Schleich, Daniel; Behnke, Sven

doi:10.1109/icra48506.2021.9560969

Cited by 11 publications

(14 citation statements)

References 18 publications

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“…This is done by grouping motion primitives that represent similar actions and considering only the longest collision-free primitive from each group. Schleich and Behnke [16] apply the idea of local multiresolution [17] to state lattices for faster replanning of dynamic UAV trajectories.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, we model the UAV state as 6-tuples s = (p, v) ∈ R 6 or 9-tuples s = (p, v, a) ∈ R 9 , consisting of a discretized 3D position p, velocity v, and acceleration a. To compare against the State of the Art, we combine δ-Spaces with the searchbased trajectory generation methods from [2] and [16]. In both works, the set of state transitions E h consists of motion primitives e u,τ which are generated by applying constant acceleration or jerk commands u over a short time interval τ .…”

Section: Application To Uav Trajectory Planningmentioning

confidence: 99%

“…4). Here, we use the method from [16] with the same parameters as in the original paper: ρ τ v max u max du Timeout 16 0.5 s 4 m/s 2 m/s 2 2 m/s 2 1 000 000 expansions .…”

Section: Successmentioning

confidence: 99%

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Two-step Planning of Dynamic UAV Trajectories using Iterative $δ$-Spaces

Sebastian¹,

Schleich²,

Behnke³

2022

Preprint

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UAV trajectory planning is often done in a two-step approach, where a low-dimensional path is refined to a dynamic trajectory. The resulting trajectories are only locally optimal, however. On the other hand, direct planning in higher-dimensional state spaces generates globally optimal solutions but is time-consuming and thus infeasible for timeconstrained applications. To address this issue, we propose δ-Spaces, a pruned high-dimensional state space representation for trajectory refinement. It does not only contain the area around a single lower-dimensional path but consists of the union of multiple near-optimal paths. Thus, it is less prone to local minima. Furthermore, we propose an anytime algorithm using δ-Spaces of increasing sizes. We compare our method against state-of-the-art search-based trajectory planning methods and evaluate it in 2D and 3D environments to generate second-order and third-order UAV trajectories.

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Section: Related Workmentioning

confidence: 99%

Section: Application To Uav Trajectory Planningmentioning

confidence: 99%

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Two-step Planning of Dynamic UAV Trajectories using Iterative $δ$-Spaces

Sebastian¹,

Schleich²,

Behnke³

2022

Preprint

Self Cite

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“…We employ our trajectory planning method from [21], which is based on the framework of Liu et al [12]. The MAV state is modeled as a 6-tuple s = (p, v) ∈ R 6 consisting of a 3D position p and velocity v. A state lattice graph G is generated by unrolling motion primitives from the initial MAV state s 0 .…”

Section: E Trajectory Planningmentioning

confidence: 99%

“…During search, we combine the 1D costs into an estimate for the 3D costs: The flight time is calculated as the maximum over the individual axes, and the control costs are those of the sub-problem with highest execution time. For further details on local multiresolutional state lattices and the proposed 1D heuristic, we refer to [21].…”

Section: E Trajectory Planningmentioning

confidence: 99%

Autonomous Flight in Unknown GNSS-denied Environments for Disaster Examination

Schleich,

Beul,

Quenzel

et al. 2021

Preprint

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Micro aerial vehicles (MAVs) exhibit high potential for information extraction tasks in search and rescue scenarios. Manually controlling MAVs in such scenarios requires experienced pilots and is error-prone, especially in stressful situations of real emergencies. The conditions of disaster scenarios are also challenging for autonomous MAV systems. The environment is usually not known in advance and GNSS might not always be available.We present a system for autonomous MAV flights in unknown environments which does not rely on global positioning systems. The method is evaluated in multiple search and rescue scenarios and allows for safe autonomous flights, even when transitioning between indoor and outdoor areas.

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Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

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Search-based Planning of Dynamic MAV Trajectories Using Local Multiresolution State Lattices

Cited by 11 publications

References 18 publications

Two-step Planning of Dynamic UAV Trajectories using Iterative $δ$-Spaces

Two-step Planning of Dynamic UAV Trajectories using Iterative $δ$-Spaces

Autonomous Flight in Unknown GNSS-denied Environments for Disaster Examination

Unmanned aerial vehicle navigation in underground structure inspection: A review

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