Adaptive path planning for UAVs for multi-resolution semantic segmentation

Stache, Felix; Westheider, Jonas; Magistri, Federico; Stachniss, Cyrill; Popović, Marija

doi:10.1016/j.robot.2022.104288

Cited by 20 publications

(9 citation statements)

References 34 publications

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“…In terms of algorithm innovation strategies, Hong et al [32] proposed an improved A * algorithm using a closed list with random access data structures, which improved the efficiency of path planning. In hierarchical search strategies, Felix et al [33] proposed using the idea of multi-resolution search for path planning, increasing the speed of planning, but the accuracy of the primary search path could not be guaranteed, leading to the inability to obtain the optimal solution. It is worth noting that most existing A * algorithms are mainly applicable to path planning tasks with small-scale data.…”

Section: Related Workmentioning

confidence: 99%

Two-Stage Path Planning for Long-Distance Off-Road Path Planning Based on Terrain Data

Zheng,

Ma,

Zhong

et al. 2024

IJGI

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In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-dimensional path planning, this paper proposes a two-stage path planning algorithm. First, an improved Probabilistic Roadmap (PRM) algorithm is used to quickly and roughly determine the initial path. Then, the morphological dilation is applied to process the grid points of the initial path, retaining the surrounding area of the initial path for a precise positioning of the search range. Finally, the idea of the A∗ algorithm is applied to achieve precise path planning in the refined search range. During the process of constructing the topology map, we utilized parallelization acceleration strategies to expedite the graph construction. In order to verify the effectiveness of the algorithm, we used terrain data to construct a wilderness environment model, and tests were conducted on off-road path planning tasks with different terrains and distances. The experimental results show a substantial enhancement in the computational efficiency of the proposed algorithm relative to the conventional A∗ algorithm by 30 to 60 times.

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Section: Related Workmentioning

confidence: 99%

Two-Stage Path Planning for Long-Distance Off-Road Path Planning Based on Terrain Data

Zheng,

Ma,

Zhong

et al. 2024

IJGI

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“…The set of all sensing planes generates a coverage tree and the authors propose a strategy to navigate it assuming no false positives in identifying the presence of a region of interest from each node. A similar multi-resolution coverage problem with a single UAV is presented in [11]. Also in this case, no prior information is taken into account and the UAV adapts at each step its altitude to obtain a suitable resolution to perform deep-learning based semantic segmentation of the terrain.…”

Section: Related Workmentioning

confidence: 99%

“…Since we are interested in minimizing the open path between the position of the UAV and one of the ends of a segment, we introduce the artificial node 2g + 1 that is connected to node 0 (UAV position) (10), and has equal distance from all other nodes. These distances are set to zero to not impact the path length (11). We find the minimum closed path on this problem and remove node 2g +1 from the solution.…”

Section: ) Distance Change With Fsmmentioning

confidence: 99%

Inspection of Ship Hulls with Multiple UAVs: Exploiting Prior Information for Online Path Planning

Grippa

Renzaglia

Rochebois

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Finally, ideas somewhat related to the ones considered here were used to pose a pathplanning algorithm for performing an energy-efficient close inspection on selected areas in agricultural fields [8].…”

Section: Related Workmentioning

confidence: 99%

“…The trial is performed on each cell only once to prevent added correlation effects at that altitude. The outcome of the single trial is added to the α and β values previously selected as the prior (shown in Figures 7 and 8) and then integrated numerically according to Equation (8). Figures 10 and 11 show the updating stage at different time instances and altitudes.…”

Section: A Single-altitude Bayesian Updatementioning

confidence: 99%

Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach

Pinkovich

Matalon

Rivlin

et al. 2022

Sensors

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This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirements of fast exploration and high resolution are solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that the spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of the patches becomes larger than a prespecified threshold, suitability for landing is declared. One of the main building blocks of the approach is a semantic segmentation algorithm that attaches probabilities to each pixel of a single view. The decision algorithm combines these probabilities with a priori data and previous measurements to obtain the best estimates. Feasibility is illustrated by presenting several examples generated by a realistic closed-loop simulator.

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Adaptive path planning for UAVs for multi-resolution semantic segmentation

Cited by 20 publications

References 34 publications

Two-Stage Path Planning for Long-Distance Off-Road Path Planning Based on Terrain Data

Two-Stage Path Planning for Long-Distance Off-Road Path Planning Based on Terrain Data

Inspection of Ship Hulls with Multiple UAVs: Exploiting Prior Information for Online Path Planning

Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach

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