scholarly journals RimJump*: Tangent Based Shortest Path Planning for Cluttered 3D Environment

2021 ◽  
Author(s):  
Zhuo Yao
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Autonomous Vehicles ◽  
Shortest Paths ◽  
Research Area ◽  
Time Cost ◽  
Geometrical Properties ◽  
3D Environment ◽  
Starting Point ◽  
Map Scale

Path planning in 3D environment is a fundamental research area for robots and autonomous vehicles. Based on the principle ``the shortest path consists of tangents'', RimJump* is proposed as a tangent-based path planning method suitable for finding the shortest path (both off-ground and on-ground) in 3D space (e.g., octomap and point cloud) for mobile platform to follow. RimJump* searches the tangent graph in the form of a path tree and considers the geometrical properties of the locally shortest path. Therefore, the method can provide all of the locally shortest paths that connect the starting point and the target, including the globally shortest path. And the time cost of RimJump* is insensitive to map scale increases in comparison to methods that search the whole passable space rather than the surface of the obstacle, e.g., Dijkstra and A*. In the Results, RimJump* is compared with other methods in terms of path length and time cost.

scholarly journals RimJump: Edge-based Shortest Path Planning for a 2D Map

Robotica ◽  
2018 ◽  
Vol 37 (4) ◽  
pp. 641-655 ◽  
Author(s):  
Zhuo Yao ◽  
Weimin Zhang ◽  
Yongliang Shi ◽  
Mingzhu Li ◽  
Zhenshuo Liang ◽  
...  
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Planning Method ◽  
Time Cost ◽  
Map Scale ◽  
Edge Based

SummaryPath planning under 2D map is a key issue in robot applications. However, most related algorithms rely on point-by-point traversal. This causes them usually cannot find the strict shortest path, and their time cost increases dramatically as the map scale increases. So we proposed RimJump to solve the above problem, and it is a new path planning method that generates the strict shortest path for a 2D map. RimJump selects points on the edge of barriers to form the strict shortest path. Simulation and experimentation prove that RimJump meets the expected requirements.

scholarly journals Accelerating exact constrained shortest paths on GPUs

2020 ◽  
Vol 14 (4) ◽  
pp. 547-559
Author(s):  
Shengliang Lu ◽  
Bingsheng He ◽  
Yuchen Li ◽  
Hao Fu
Keyword(s):  
Exact Solutions ◽  
Shortest Path ◽  
Performance Optimization ◽  
Autonomous Vehicles ◽  
Shortest Paths ◽  
Approximate Solutions ◽  
Shortest Path Problems ◽  
Labeling Algorithm ◽  
Constrained Shortest Paths ◽  
Computational Resources

The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and how we can efficiently find the exact solution for CSP. In this paper, we propose Vine , a framework that parallelizes the labeling algorithm to efficiently find the exact CSP solution using GPUs. The major challenge addressed in Vine is how to deal with a large number of subproblems that are mostly unpromising but require a significant amount of memory and computational resources. Our solution is twofold. First, we develop a two-level pruning approach to eliminate the subproblems by making good use of the GPU's hierarchical memory. Second, we propose an adaptive parallelism control model based on the observations that the degree of parallelism (DOP) is the key to performance optimization with the given amount of computational resources. Extensive experiments show that Vine achieves 18× speedup on average over the widely adopted CPU-based solution running on 40 CPU threads. Vine also has over 5× speedup compared with a GPU approach that statically controls the DOP. Compared to the state-of-the-art approximate solution with preprocessed indices, Vine provides exact results with competitive or even better performance.

scholarly journals Path Planning for Mobile Agents Using a Genetic Algorithm with a Direction Guided Factor

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 212 ◽  
Author(s):  
Hyeok-Yeon Lee ◽  
Hyunwoo Shin ◽  
Junjae Chae
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Shortest Path ◽  
Free Space ◽  
Mobile Agents ◽  
Dimensional Space ◽  
Target Point ◽  
Complex Environments ◽  
Search Spaces ◽  
Starting Point

This paper suggests a novel methodology in collision-free shortest path planning (CFSPP) problems for mobile agents (MAs) using a method that combines a genetic algorithm (GA) and a direction factor toward a target point. In the CFSPP problem, MAs find the shortest path from the starting point to the target point while avoiding certain obstacles. The paper proposes an obstacle-based search methodology that identifies critical collision-free points adjacent to given obstacles. When critical obstacles are found via CFSPP, this study suggests favorable paths in 2-dimensional space found using the obstacle-based GA (OBGA). The OBGA has four advantages. First, it effectively narrows the search spaces compared to free space-based methodologies. It also determines shorter collision-free paths, and it only requires a short amount of time. Finally, convergence occurs more quickly than in previous studies. The proposed method also works properly in larger and more complex environments, indicating that it can be applied to more practical problems.

scholarly journals Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Pablo Marin-Plaza ◽  
Ahmed Hussein ◽  
David Martin ◽  
Arturo de la Escalera
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Starting Point ◽  
Model Configuration ◽  
Software Prototyping ◽  
Research Platform ◽  
Local Path ◽  
Low Sensitivity ◽  
Global And Local ◽  
Carlos Iii

The aim of this work is to integrate and analyze the performance of a path planning method based on Time Elastic Bands (TEB) in real research platform based on Ackermann model. Moreover, it will be proved that all modules related to the navigation can coexist and work together to achieve the goal point without any collision. The study is done by analyzing the trajectory generated from global and local planners. The software prototyping tool is Robot Operating System (ROS) from Open Source Robotics Foundation and the research platform is the iCab (Intelligent Campus Automobile) from University Carlos III. This work has been validated from a test inside the campus where the iCab has performed the navigation between the starting point and the goal point without any collision. During the experiment, we proved the low sensitivity of the TEB method to variations of the vehicle model configuration and constraints.

scholarly journals Sublinear search spaces for shortest path planning in grid and road networks

2021 ◽  
Author(s):  
Johannes Blum ◽  
Stefan Funke ◽  
Sabine Storandt
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Shortest Paths ◽  
Road Networks ◽  
Grid Graphs ◽  
Search Spaces ◽  
Grid Networks ◽  
Fundamental Building Block ◽  
Important Challenge ◽  
Practical Performance

AbstractShortest path planning is a fundamental building block in many applications. Hence developing efficient methods for computing shortest paths in, e.g., road or grid networks is an important challenge. The most successful techniques for fast query answering rely on preprocessing. However, for many of these techniques it is not fully understood why they perform so remarkably well, and theoretical justification for the empirical results is missing. An attempt to explain the excellent practical performance of preprocessing based techniques on road networks (as transit nodes, hub labels, or contraction hierarchies) in a sound theoretical way are parametrized analyses, e.g., considering the highway dimension or skeleton dimension of a graph. Still, these parameters may be large in case the network contains grid-like substructures—which inarguably is the case for real-world road networks around the globe. In this paper, we use the very intuitive notion of bounded growth graphs to describe road networks and also grid graphs. We show that this model suffices to prove sublinear search spaces for the three above mentioned state-of-the-art shortest path planning techniques. Furthermore, our preprocessing methods are close to the ones used in practice and only require expected polynomial time.

scholarly journals Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

2021 ◽  
Vol 13 (22) ◽  
pp. 4644
Author(s):  
Heba Kurdi ◽  
Shaden Almuhalhel ◽  
Hebah Elgibreen ◽  
Hajar Qahmash ◽  
Bayan Albatati ◽  
...  
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Execution Time ◽  
Autonomous Vehicles ◽  
The Moon ◽  
Breadth First Search ◽  
The Earth ◽  
Planning Algorithm ◽  
Np Hard Problem ◽  
Path Planning Algorithm

With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.

scholarly journals Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5053 ◽  
Author(s):  
Saba Arshad ◽  
Muhammad Sualeh ◽  
Dohyeong Kim ◽  
Dinh Van Nam ◽  
Gon-Woo Kim
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Unified Framework ◽  
Human Errors ◽  
Safe Driving ◽  
Planning And Control ◽  
Starting Point ◽  
And Control

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

A Practical Algorithm for Shortest Paths on Polyhedral Surfaces

1993 ◽  
Author(s):  
P. Rambeaud ◽  
Saïd Zeghloul
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Shortest Paths ◽  
Experimental Results ◽  
Planning Problem ◽  
Polyhedral Surface ◽  
Planning Systems ◽  
Practical Algorithm ◽  
Path Planning Problem ◽  
3D Problem

Abstract This paper describes a method for treating the shortest path planning problem along a convex polyhedral surface using an unfolding process. Since most planning systems use polyhedral environments, finding the shortest possible path is very useful for some typical robotics applications such as spacecraft or submersible robot motions. The basic idea in our algorithm is to unfold the polyhedral surface into a plane, in order to convert the 3D problem to a 2D one. We provide experimental results on a box and on a sphere to illustrate the unfolding process.

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