Comparison of optimal path planning algorithms

Korkmaz, Mehmet; Durdu, Akif

doi:10.1109/tcset.2018.8336197

Cited by 37 publications

(27 citation statements)

References 11 publications

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“…Some of these methods allow exploring large areas of interest and reducing the computational time, such as Probabilistic Roadmaps (PRM). This work focuses on the PRM algorithm, due to its advantages compared with other algorithms (e.g., A*, Rapidly-Exploring Random Tree (RRT), bidirectional RRT (bRRT), and Genetic Algorithms (GA)) in terms of both shortest time and distance parameters [16].…”

Section: D Path Planningmentioning

confidence: 99%

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

Madridano

Al-Kaff

Martín

et al. 2020

Sensors

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The development in Multi-Robot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collision-free movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.

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Section: D Path Planningmentioning

confidence: 99%

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

Madridano

Al-Kaff

Martín

et al. 2020

Sensors

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“…A* algorithm shown in Figure 2. A* tries to minimize the cost for the path by the formulized function as (Korkmaz & Durdu, 2018):…”

Section: A* Algorithmmentioning

confidence: 99%

“…When leaf nodes in the random tree contain the goal point or enter the goal region, a path from the initial point to the goal point can be found (Xinyu, Xiaojuan, Yong, Jiadong, & Rui, 2019). RRT is suitable algorithm to solve path planning problems under holonomic and non-holonomic constraints (Korkmaz & Durdu, 2018). Figure 4 shows the RRT algorithm.…”

Section: Rapidly Exploring Random Tree Algorithmmentioning

confidence: 99%

“…The map of the environment illustrated in Figure 8 is obtained by implementing ROS gmapping package. Gmapping is a type of SLAM (Simultaneous Localization and Mapping) algorithm which is a powerful technique in order to mapping from the environment (Korkmaz & Durdu, 2018). Odometry and laser data are used as inputs of gmapping during 2D grid map creation.…”

Section: Running On Simulationmentioning

confidence: 99%

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A Moving Obstacles Planner Algorithm for a Six-Wheeled Surveying Robot

Bazhan

Ahmadabadian

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Nowadays, robotic systems such as ground vehicle robots are mostly used in many industrial and military applications. Therefore, the path planning problem in the robotics domain is very important. Moving Obstacles Planner (MOP) algorithms have got the researchers interests in recent years and some of the most recent ones have been implemented in Robot Operating System (ROS) which is an open source middle wear to work with robots. This paper aims to compare the state-of-the-art MOP algorithms including Rapidly exploring Random Tree (RRT) and those implemented in the ROS navigation stack such as Dynamic Window Approach (DWA) local planner coupled with Dijkstra and A* as global planners on a six-wheeled robot known as MOOR in simulation environment. The results reveal that all of these algorithms have been designed for a square shape footprint robot and thus have limitations for MOOR with a rectangular footprint shape.

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“…A* is best regarding the shortest distance, but the algorithm need a longer computational time. The algorithm is not appropriate to the situation for sequential tasks are assigned for robot [24]. The path planning with energy considerations insight were generated by weighted A* search to obtain the global navigation strategy [25].…”

mentioning

confidence: 99%

A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle

Debnath

Omar

Latip

et al. 2019

IJEECS

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<span>Unmanned Air Vehicle (UAV) has attracted attention in recent years in conducting missions for longer time with higher levels of autonomy. For the enhanced autonomous characteristic of UAV, path planning is one of the crucial issues. Current researches on the graph search algorithms under combinatorial method are mainly reviewed in this paper by keeping focus on the comprehensive surveys of its properties for path planning. The outcome is a pen picture of their assumptions and drawbacks.</span>

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Comparison of optimal path planning algorithms

Cited by 37 publications

References 11 publications

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

A Moving Obstacles Planner Algorithm for a Six-Wheeled Surveying Robot

A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle

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