scholarly journals Path Planning for Indoor UAV Using A* and Late Acceptance Hill Climbing Algorithms Utilizing Probabilistic Roadmap

2020 ◽  
Vol 9 (4) ◽  
pp. 857
Author(s):  
Jacob Hopkins ◽  
Forrest Joy ◽  
Alaa Sheta ◽  
Hamza Turabieh ◽  
Dulal Kar
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Real Life ◽  
Search Space ◽  
Hill Climbing ◽  
Specific Criterion ◽  
Indoor Space ◽  
Probabilistic Roadmap ◽  
Package Delivery ◽  
Late Acceptance Hill Climbing

The main objective of an unmanned aerial vehicle (UAV) path planning is to generate a flight path that links a start point to an endpoint in an indoor space avoiding obstacles.  Path planning is essential for many real-life applications such as an autonomous car, surveillance mission, farming robots, unmanned aerial vehicles package delivery, space exploration, and many others. To create an optimal path, we need to adopt a specific criterion to minimize the distance the UAV must travel such as the Euclidean distance. In this paper, we provide our initial idea of creating an optimal path for indoor UAV using both A* and the Late Acceptance Hill Climbing (LAHC) algorithms. We are adopting an indoor search environment with various complexity and utilize the Probabilistic Roadmap algorithm (PRM) as a search space for both algorithms. The basic idea following PRM is to generate random sample points in the space and search these points for an optimal path. The developed results show that the LAHC algorithm outperforms the A* algorithm.

Path planning of a mobile robot using genetic heuristics

Robotica ◽  
1998 ◽  
Vol 16 (5) ◽  
pp. 575-588 ◽  
Author(s):  
Andreas C. Nearchou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Hill Climbing ◽  
Planning Problem ◽  
Well Performance ◽  
Simulation Results ◽  
Task Oriented ◽  
Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

scholarly journals UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yudong Zhang ◽  
Lenan Wu ◽  
Shuihua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Minimum Probability ◽  
Chaotic Particle Swarm Optimization

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

scholarly journals OctoPath: An OcTree-Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3606
Author(s):  
Bogdan Trăsnea ◽  
Cosmin Ginerică ◽  
Mihai Zaha ◽  
Gigel Măceşanu ◽  
Claudiu Pozna ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Supervised Learning ◽  
Optimal Path ◽  
Real Life ◽  
Classification Problem ◽  
Training Dataset ◽  
Measurement Unit ◽  
Optimal Path Planning ◽  
Environment Model

Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of perception and path planning, in this paper, we introduce OctoPath, which is an encoder-decoder deep neural network, trained in a self-supervised manner to predict the local optimal trajectory for the ego-vehicle. Using the discretization provided by a 3D octree environment model, our approach reformulates trajectory prediction as a classification problem with a configurable resolution. During training, OctoPath minimizes the error between the predicted and the manually driven trajectories in a given training dataset. This allows us to avoid the pitfall of regression-based trajectory estimation, in which there is an infinite state space for the output trajectory points. Environment sensing is performed using a 40-channel mechanical LiDAR sensor, fused with an inertial measurement unit and wheels odometry for state estimation. The experiments are performed both in simulation and real-life, using our own developed GridSim simulator and RovisLab’s Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath in different driving scenarios, both indoor and outdoor, while benchmarking our system against a baseline hybrid A-Star algorithm and a regression-based supervised learning method, as well as against a CNN learning-based optimal path planning method.

Research on Vehicle Path Planning Based on the BDD in the Uncertain Environment

2013 ◽  
Vol 373-375 ◽  
pp. 1144-1149
Author(s):  
Ya Li Peng ◽  
Jia Yao Liu ◽  
Hong Yin
Keyword(s):  
Path Planning ◽  
Heuristic Search ◽  
High Efficiency ◽  
Road Traffic ◽  
Optimal Path ◽  
Search Space ◽  
Routing Problem ◽  
Uncertain Environment ◽  
The Road ◽  
Global Planning

Aimed at the high dynamics and uncertainty of road traffic, we propose a method combine BDD (binary decision diagram)-Based heuristic algorithm which used to do the initial path planning with BDD-Based incremental to solve the route replanning problem. In order to get the optimal path set, BDD-Based heuristic Search is firstly used for global planning. BDD is a compact data structure, the BDD-Based heuristic Search use this characteristic to represent state space and compress the search space through heuristic information at the same time; when the road network information changes, incremental replanning was used in difference type of congestion and the optimum path set again. The simulation results show that the BDD-Based heuristic Search and incremental replanning method has high efficiency and practicability in solving vehicle routing problem under dynamic and uncertain environment.

scholarly journals Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

2021 ◽  
Vol 11 (12) ◽  
pp. 5340
Author(s):  
Abdul Majeed ◽  
Seong Oun Hwang
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Optimal Path ◽  
Three Dimensional ◽  
Search Space ◽  
Urban Environments ◽  
Time Performance ◽  
Probability Spaces ◽  
Obstacle Geometry ◽  
Time Overhead

Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. Ignoring high-probability spaces and instead exploring too many spaces on maps while searching for a path yields extensive computing-time overhead. This paper presents a new PP method that finds optimal/quasi-optimal and safe (e.g., collision-free) working paths for UAVs in a 3D urban environment encompassing substantial obstacles. By using Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) while searching for a path, the proposed PP method significantly lowers pathfinding time complexity without degrading the length of the path by much. We suggest an intelligent method exploiting obstacle geometry information to constrain the search space in a 3D polygon form from which a quasi-optimal flyable path can be found quickly. Furthermore, we perform task modeling with an ESWG using as few nodes and edges from the CPS as possible, and we find an abstract path that is subsequently improved. The results achieved from extensive experiments, and comparison with prior methods certify the efficacy of the proposed method and verify the above assertions.

scholarly journals LAV Path Planning by Enhanced Fireworks Algorithm on Prior Knowledge

2016 ◽  
Vol 1 (1) ◽  
pp. 65-78 ◽  
Author(s):  
Li Bing ◽  
You Ning ◽  
Du YeHong
Keyword(s):  
Path Planning ◽  
Prior Knowledge ◽  
Optimal Path ◽  
Optimal Solution ◽  
Search Space ◽  
Intelligent Algorithm ◽  
Fireworks Algorithm ◽  
Dynamic Path Planning ◽  
Minimum Probability ◽  
Dynamic Path

AbstractPath planning plays an extremely important role in the design of LAVs (Loitering Air Vehicles) to accomplish the air combat task fleetly and reliably. The planned path should ensure that LAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. So it takes a lot of time and consumes a lot of computing resources. In this paper, a new young intelligent algorithm-fireworks algorithm is introduced, and EFWA (enhanced fireworks algorithm)-its enhanced version is used to find the optimal solution. At the same time, the battlefield prior knowledge is fully utilized to predict the existence space of the potential optimal trajectory. Greatly the search space reduced, plan planning efficiency is significantly improved. Path planning method effectiveness in this paper has further been improved compared with FACPSO. Moreover, the EFWA on prior knowledge performs well on the application of dynamic path planning when the threats cruise randomly than FAC-PSO.

scholarly journals Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

2021 ◽  
Vol 17 (4) ◽  
pp. 491-505
Author(s):  
G. Kulathunga ◽  
◽  
D. Devitt ◽  
R. Fedorenko ◽  
A. Klimchik ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Free Path ◽  
Optimal Path ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Algorithm ◽  
Feasible Path ◽  
Straight Lines ◽  
Path Planning Algorithm

Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.

Multi-Robot Navigation in Unknown Environment Using Strawberry Algorithm

2020 ◽  
pp. 1121-1142
Author(s):  
B. Sai Charan ◽  
Ayush Mittal ◽  
Ritu Tiwari
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Robot Navigation ◽  
Optimal Path ◽  
Real Life ◽  
Initial Position ◽  
Robot Motion ◽  
Final Position ◽  
Life Challenges ◽  
Optimization Parameters

Path Planning focuses on the robot motion from the initial position to final position such that it must avoid the hurdles and finally reach the goal in optimal path. But it is not an easy task because many conditions are included for the efficiency of final result like working on different environments, known or unknown target etc. In this paper the authors have proposed an algorithm inspired by the strawberry plants, and is applied in the path planning. The algorithm can efficiently work for different optimization parameters like Path Length, Energy and Number of Turns. The proposed algorithm is compared with RRT, A-star, PSO and the results obtained are satisfactory. The work can be applied in the real life challenges faced during area exploration

Mining High Utility Itemsets with Hill Climbing and Simulated Annealing

2022 ◽  
Vol 13 (1) ◽  
pp. 1-22
Author(s):  
M. Saqib Nawaz ◽  
Philippe Fournier-Viger ◽  
Unil Yun ◽  
Youxi Wu ◽  
Wei Song
Keyword(s):  
Simulated Annealing ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Search Space ◽  
Population Diversity ◽  
Hill Climbing ◽  
Target Values ◽  
High Utility ◽  
High Utility Itemsets ◽  
Search Space Pruning

High utility itemset mining (HUIM) is the task of finding all items set, purchased together, that generate a high profit in a transaction database. In the past, several algorithms have been developed to mine high utility itemsets (HUIs). However, most of them cannot properly handle the exponential search space while finding HUIs when the size of the database and total number of items increases. Recently, evolutionary and heuristic algorithms were designed to mine HUIs, which provided considerable performance improvement. However, they can still have a long runtime and some may miss many HUIs. To address this problem, this article proposes two algorithms for HUIM based on Hill Climbing (HUIM-HC) and Simulated Annealing (HUIM-SA). Both algorithms transform the input database into a bitmap for efficient utility computation and for search space pruning. To improve population diversity, HUIs discovered by evolution are used as target values for the next population instead of keeping the current optimal values in the next population. Through experiments on real-life datasets, it was found that the proposed algorithms are faster than state-of-the-art heuristic and evolutionary HUIM algorithms, that HUIM-SA discovers similar HUIs, and that HUIM-SA evolves linearly with the number of iterations.

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