A strategy and evaluation method for ground global path planning based on aerial images

Borges, Carlos David Braga; Almeida, Antonio Márcio Albuquerque; Paula, Ialis Cavalcante de; Mesquita, Jarbas Joaci de

doi:10.1016/j.eswa.2019.06.067

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Therefore, it is only necessary to construct the function ( ) , P x y to determine whether the two consecutive nodes satisfies the relationship in formula (7). Then decide whether to delete the current node n , and the principle of function…”

Section: Figure 8 Coordinates Of the Cross Pathmentioning

confidence: 99%

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Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

et al. 2021

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This research introduces a path planning method based on the geometric A-star algorithm.The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions ( ) , P x y and ( ) , W x y and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.

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Section: Figure 8 Coordinates Of the Cross Pathmentioning

confidence: 99%

“…Then a planning search algorithm is used to find a suitable path from the start point to the goal point. The focus of a path planning is to build a reasonable obstacle distribution map based on the known environment [7], and then find a path with the least cost to avoid obstacles [8].…”

Section: Introductionmentioning

confidence: 99%

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

et al. 2021

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“…It shows that the main area of focus of previous studies was the determination of optimal routes for the movement of autonomous robots (UGV—Unmanned Ground Vehicle) [ 32 ]. The authors of [ 33 ] used aviation photography for this purpose. Article [ 34 ] illustrates the applicability of the method through experiments with an unmanned ground vehicle in both structured and unstructured environments.…”

Section: Introductionmentioning

confidence: 99%

Methodology of Using Terrain Passability Maps for Planning the Movement of Troops and Navigation of Unmanned Ground Vehicles

Dawid

Pokonieczny

2021

Sensors

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The determination of the route of movement is a key factor which enables navigation. In this article, the authors present the methodology of using different resolution terrain passability maps to generate graphs, which allow for the determination of the optimal route between two points. The routes are generated with the use of two commonly used pathfinding algorithms: Dijkstra’s and A-star. The proposed methodology allows for the determination of routes in various variants—a more secure route that avoids all terrain obstacles with a wide curve, or a shorter route, which is, however, more difficult to pass. In order to achieve that, two functions that modify the value of the index of passability (IOP), which is assigned to the primary fields that the passability map consists of, have been used. These functions have a β parameter that augments or reduces the impact of the applied function on IOP values. The paper also shows the possibilities of implementation of the methodology for the movement of single vehicles or unmanned ground vehicles (UGVs) by using detailed maps as well as for determining routes for large military operational units moving in a 1 km wide corridor. The obtained results show that the change in β value causes the change of a course of the route as expected and that Dijkstra’s algorithm is more stable and slightly faster than A-star. The area of application of the presented methodology is very wide because, except for planning the movement of unmanned ground vehicles or military units of different sizes, it can be used in crisis management, where the possibility of reaching the area outside the road network can be of key importance for the success of the salvage operation.

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“…First, while ground robots navigate in real-world off-road environments, they experience terrains with a wide variety of characteristics that cannot be modeled beforehand, for example, tall grass terrain with hidden rocks as illustrated in Figure 1. Second, robots may also experience negative effects or the so-called setbacks (Borges et al (2019); Knight et al (2001)), that are defined as the changes in the robot's functionalities that increase the difficulty for a robot to accomplish its expected behaviors. Examples of the setbacks include malfunctioning robot joints, reduced wheel traction, and heavy payload.…”

Section: Introductionmentioning

confidence: 99%

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

Siva¹,

Wigness²,

Rogers³

et al. 2021

Preprint

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Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.

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A strategy and evaluation method for ground global path planning based on aerial images

Cited by 17 publications

References 24 publications

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

Methodology of Using Terrain Passability Maps for Planning the Movement of Troops and Navigation of Unmanned Ground Vehicles

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

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