A Shortest Path Planning Algorithm for PSO Base Firefighting Robots

2018 ◽  
Author(s):  
D. Mihiraj Ranaweera ◽  
K.T.M. Udayanga Hemapala ◽  
A. G. Buddhika ◽  
P. Jayasekara
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals Any-Angle Path Planning

AI Magazine ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 85-107 ◽  
Author(s):  
Alex Nash ◽  
Sven Koenig
Keyword(s):  
Video Games ◽  
Path Planning ◽  
Shortest Path ◽  
Grid Cells ◽  
Grid Graph ◽  
Planning Algorithm ◽  
Planning Algorithms ◽  
Planning Methodology ◽  
Path Planning Algorithm

In robotics and video games, one often discretizes continuous terrain into a grid with blocked and unblocked grid cells and then uses path-planning algorithms to find a shortest path on the resulting grid graph. This path, however, is typically not a shortest path in the continuous terrain. In this overview article, we discuss a path-planning methodology for quickly finding paths in continuous terrain that are typically shorter than shortest grid paths. Any-angle path-planning algorithms are variants of the heuristic path-planning algorithm A* that find short paths by propagating information along grid edges (like A*, to be fast) without constraining the resulting paths to grid edges (unlike A*, to find short paths).

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 Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Surface Vehicles

2019 ◽  
Vol 72 (04) ◽  
pp. 850-874 ◽  
Author(s):  
Hanlin Niu ◽  
Al Savvaris ◽  
Antonios Tsourdos ◽  
Ze Ji
Keyword(s):  
Path Planning ◽  
Voronoi Diagram ◽  
Shortest Path ◽  
Performance Comparison ◽  
Computational Time ◽  
Planning Problem ◽  
Planning Algorithm ◽  
Quantitative Results ◽  
Path Planning Algorithm

In this paper, a novel Voronoi-Visibility (VV) path planning algorithm, which integrates the merits of a Voronoi diagram and a Visibility graph, is proposed for solving the Unmanned Surface Vehicle (USV) path planning problem. The VM (Voronoi shortest path refined by Minimising the number of waypoints) algorithm was applied for performance comparison. The VV and VM algorithms were compared in ten Singapore Strait missions and five Croatian missions. To test the computational time, a high-resolution, large spatial dataset was used. It was demonstrated that the proposed algorithm not only improved the quality of the Voronoi shortest path but also maintained the computational efficiency of the Voronoi diagram in dealing with different geographical scenarios, while also keeping the USV at a configurable clearance distance c from coastlines. Quantitative results were generated by comparing the Voronoi, VM and VV algorithms in 2,000 randomly generated missions using the Singapore dataset.

scholarly journals A Composite Metric Routing Approach for Energy-Efficient Shortest Path Planning on Natural Terrains

2021 ◽  
Vol 11 (15) ◽  
pp. 6939
Author(s):  
Mohamed Saad ◽  
Ahmed I. Salameh ◽  
Saeed Abdallah ◽  
Ali El-Moursy ◽  
Chi-Tsun Cheng
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Shortest Path ◽  
Energy Cost ◽  
Energy Efficient ◽  
Cost Minimization ◽  
Running Time ◽  
Path Distance ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper explores the problem of energy-efficient shortest path planning on off-road, natural, real-life terrain for unmanned ground vehicles (UGVs). We present a greedy path planning algorithm based on a composite metric routing approach that combines the energy consumption and distance of the path. In our work, we consider the Terramechanics between the UGV and the terrain soil to account for the wheel sinkage effect, in addition to the terrain slope and soil deformation limitations in the development of the path planning algorithm. As benchmarks for comparison, we use a recent energy-cost minimization approach, in addition to an ant colony optimization (ACO) implementation. Our results indicate that the proposed composite metric routing approach outperforms the state-of-the-art energy-cost minimization method in terms of the resulting path distance, with a negligible increase in energy consumption. Moreover, our results indicate also that the proposed greedy algorithm strongly outperforms the ACO implementation in terms of the quality of the paths obtained and the algorithm running time. In fact, the running time of our proposed algorithm indicates its suitability for large natural terrain graphs with thousands of nodes and tens of thousands of links.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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