Path Planning for UGVs Based on Traversability Hybrid A*

Thoresen, Marius; Nielsen, Niels Hygum; Mathiassen, Kim; Pettersen, Kristin Y.

doi:10.1109/lra.2021.3056028

Cited by 47 publications

(12 citation statements)

References 28 publications

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“…For instance, the modified Hybrid A* [ 26 ] algorithm has been enhanced with route planning, optimizing the distance to the endpoint and minimizing the cost of travel in the form of road traversability. In experiments, the proposed method was successfully applied to autonomous driving for a distance of up to 270 min in rough terrain.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of colors and features than traditional cameras and to perceive the environment more accurately. Several surface types, such as mud, can be challenging to detect using an RGB camera. In our system, the hyperspectral camera is used for ground recognition (e.g., grass, bumpy road, asphalt). Traditional global path planning methods take the shortest path length as the optimization objective. We propose an improved A* algorithm to generate the collision-free path. Semantic information makes it possible to plan a feasible and safe path in a complex off-road environment, taking traveling time as the optimization objective. We presented the results of the experiments for data collected in a natural environment. An important novelty of this paper is using a modified nearest neighbor method for hyperspectral data analysis and then using the data for path planning tasks in the same work. Using the nearest neighbor method allows us to adjust the robotic system much faster than using neural networks. As our system is continuously evolving, we intend to examine the performance of the vehicle on various road surfaces, which is why we sought to create a classification system that does not require a prolonged learning process. In our paper, we aimed to demonstrate that the incorporation of a hyperspectral camera can not only enhance route planning but also aid in the determination of parameters such as speed and acceleration.

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Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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show abstract

“…In online motion planning, several studies have been done in 3DoF (Three Degrees of Freedom), such as [23]- [25] The motion is implemented after generating map and calculating the trajectory based on observed obstacles and free paths using vehicle sensors. Even though the concept is most wellknown in robotics, it cannot be implemented directly on UAV due to 6DoF, hardware constraints, payload capacity and flight limitation.…”

Section: Related Workmentioning

confidence: 99%

Online Path Planning Framework for UAV in Rural Areas

Airlangga

Liu

2022

IEEE Access

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A motion strategy plays an important role in supporting autonomous Unmanned Aerial Vehicle (UAV) movement. Many studies have been conducted to improve the motion frameworks in terms of its robustness, safety and performance. Most of them worked on the prior known maps scenario where the area information was collected by Global Positioning System (GPS) and satellite cameras. Even though the scheme can provide high quality map, the computation of motion planning remains dependent on the communication signal. In the rural areas such as forests and mountains, where communication signal does not perform well, unclear and noisy terrain maps can be generated and lead to mission failure. Therefore, it is significant that an alternative framework to enhance autonomous UAV motion performance in these certain conditions should be developed. Our work focuses on developing a high performance path planner for autonomous UAV motion when communication signal does not work well in rural areas. The search mission problem in forest terrain has been implemented in 3D simulation as an evaluation. By conducting a simulation process repeatedly with different test cases for positions, time constraints, flight speed (3-11 m/s) and flight range, our path planning framework can achieve completeness between 90-100% and better performance compared to others.INDEX TERMS Online path planning, motion planning, UAV, rural areas.

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“…The elevation map, created by Lidar point clouds [25], [26], has been used to compute the geometrical features of the 3D surface [27], [28]. Using geometry-based traversability cost, local and global navigation algorithms have been proposed in [19], [29], [30]. However, such methods disregard the vehicleterrain interactions derived from the terrain conditions, such as friction coefficients.…”

Section: B Vehicle Navigation In View Of Traversabilitymentioning

confidence: 99%

“…To validate the performance of the proposed algorithm, we conduct a comparative analysis with two baseline navigation methods: i) a method that only considers the geometric traversability cost [25]; and ii) a method that considers the hybrid traversability cost, which accounts for both semantic and geometric traversability costs by simply adding two different costs [27]. For real-time path planning of the baseline navigation methods, we choose A * as the global path planner and Hybrid A * as the local path planner since it has been proven to be successfully applied for many offroad vehicle navigation [29], [34], [50], [51]. Pure pursuit controller is applied for path following of baseline navigation methods [52].…”

Section: B Comparative Analysismentioning

confidence: 99%

Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains

Lee¹,

Kwon²,

Kwon³

2022

Preprint

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This paper presents a safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments. Off-road navigation is subject to uncertain vehicleterrain interactions caused by different terrain conditions on top of 3D terrain topology. The existing works are limited to adopt overly simplified vehicle-terrain models. The proposed algorithm learns the terrain-induced uncertainties from driving data and encodes the learned uncertainty distribution into the traversability cost for path evaluation. The navigation path is then designed to optimize the uncertainty-aware traversability cost, resulting in a safe and agile vehicle maneuver. Assuring real-time execution, the algorithm is further implemented within parallel computation architecture running on Graphics Processing Units (GPU).

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Path Planning for UGVs Based on Traversability Hybrid A*

Cited by 47 publications

References 28 publications

Hyperspectral Imaging for Mobile Robot Navigation

Hyperspectral Imaging for Mobile Robot Navigation

Online Path Planning Framework for UAV in Rural Areas

Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains

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