scholarly journals A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 945 ◽  
Author(s):  
Iram Noreen ◽  
Amna Khan ◽  
Khurshid Asghar ◽  
Zulfiqar Habib
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Optimal Path ◽  
Performance Comparison ◽  
Computational Time ◽  
And Performance ◽  
Commercial Applications ◽  
Space Requirements

With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

scholarly journals Time-efficient path planning using two virtual robots

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988674
Author(s):  
Jonghoek Kim
Keyword(s):  
Path Planning ◽  
Finite Time ◽  
Free Space ◽  
Path Length ◽  
Optimal Path ◽  
Three Dimensional ◽  
Computational Time ◽  
Topological Map ◽  
Planning Algorithms ◽  
Safe Path

This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot’s size so that as the robot traverses the constructed path, it doesn’t collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and near-optimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.

scholarly journals Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141666366 ◽  
Author(s):  
Imen Chaari ◽  
Anis Koubaa ◽  
Hachemi Bennaceur ◽  
Adel Ammar ◽  
Maram Alajlan ◽  
...  
Keyword(s):  
Path Planning ◽  
Execution Time ◽  
Large Scale ◽  
Optimal Path ◽  
Heuristic Methods ◽  
Solution Quality ◽  
Good Trade ◽  
And Performance ◽  
Path Planner ◽  
The Cost

This article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.

scholarly journals Collision Free Smooth Path for Mobile Robots in Cluttered Environment Using an Economical Clamped Cubic B-Spline

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1567
Author(s):  
Iram Noreen
Keyword(s):  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Mobile Robotics ◽  
Optimal Path ◽  
Post Processing ◽  
B Spline ◽  
Smooth Path ◽  
Path Smoothing ◽  
Narrow Passage

Mobile robots have various applications in agriculture, autonomous cars, industrial automation, planetary exploration, security, and surveillance. The generation of the optimal smooth path is a significant aspect of mobile robotics. An optimal path for a mobile robot is measured by various factors such as path length, path smoothness, collision-free curve, execution time, and the total number of turns. However, most of the planners generate a non-smooth less optimal and linear piecewise path. Post processing smoothing is applied at the cost of increase in path length. Moreover, current research on post-processing path smoothing techniques does not address the issues of post smoothness collision and performance efficiency. This paper presents a path smoothing approach based on clamped cubic B-Spline to resolve the aforementioned issues. The proposed approach has introduced an economical point insertion scheme with automated knot vector generation while eliminating post smoothness collisions with obstacles. It generates C2 continuous path without any stitching point and passes more closely to the originally planned path. Experiments and comparison with previous approaches have shown that the proposed approach generates better results with reduced path length, and execution time. The test cases used for experiments include a simple structure environment, complex un-structured environment, an environment full of random cluttered narrow obstacles, and a case study of an indoor narrow passage.

Comparison between Waveform and Bug Path Planning Algorithm for Mobile Robot

2014 ◽  
Vol 607 ◽  
pp. 774-777
Author(s):  
Swee Ho Tang ◽  
Che Fai Yeong ◽  
Eileen Lee Ming Su
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Simulation Software ◽  
Breadth First Search ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Grid Based

Mobile robots frequently find themselves in a circumstance where they need to find a trajectory to another position in their environment, subject to constraints postured by obstacles and the capabilities of the robot itself. This study compared path planning algorithms for mobile robots to move efficiently in a collision free grid based static environment. Two algorithms have been selected to do the comparison namely wavefront algorithm and bug algorithm. The wavefront algorithm involves a breadth-first search of the graph beginning at the goal position until it reaches the start position. The bug algorithm uses obstacles borders as guidance toward a goal with restricted details about the environment. The algorithms are compared in terms of parameters such as execution time of the algorithm and planned path length by using Player/Stage simulation software. Results shown that wavefront algorithm is a better path planning algorithm compared to bug algorithm in static environment.

scholarly journals Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4156
Author(s):  
Luís B. P. Nascimento ◽  
Dennis Barrios-Aranibar ◽  
Vitor G. Santos ◽  
Diego S. Pereira ◽  
William C. Ribeiro ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Configuration Space ◽  
Autonomous Robot ◽  
Breadth First Search ◽  
Propagation Process ◽  
Robot Systems ◽  
And Performance ◽  
Safe Path ◽  
High Clearance

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

scholarly journals MODIFIED VIRTUAL SEMI-CIRCLE PATH PLANNING

2016 ◽  
Vol 78 (6-6) ◽  
Author(s):  
R. N. Farah ◽  
Amira Shahirah ◽  
N. Irwan ◽  
R. L. Zuraida
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimal Path ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Reactive Navigation ◽  
Optimal Path Planning ◽  
Two Phases

The challenging part of path planning for an Unmanned Ground Vehicle (UGV) is to conduct a reactive navigation. Reactive navigation is implemented to the sensor based UGV. The UGV defined the environment by collecting the information to construct it path planning. The UGV in this research is known as Mobile Guard UGV-Truck for Surveillance (MG-TruckS). Modified Virtual Semi Circle (MVSC) helps the MG-TruckS to reach it predetermined goal point successfully without any collision. MVSC is divided into two phases which are obstacles detection phase and obstacles avoidance phase to compute an optimal path planning. MVSC produces shorter path length, smoothness of velocity and reach it predetermined goal point successfully.

Improving Firm Performance Through a Mobile Auditing Assistance System

2014 ◽  
Vol 10 (4) ◽  
pp. 22-35 ◽  
Author(s):  
Wen-Lung Shiau
Keyword(s):  
Information Technology ◽  
Path Planning ◽  
Mobile Technology ◽  
Optimal Path ◽  
Business Environment ◽  
Assistance System ◽  
Optimal Path Planning ◽  
Mobile Information ◽  
Time Basis ◽  
And Performance

Generalized Audit Software (GAS) is often used in the current business environment. Despite the advent of mature mobile technology, designs for effective mobile information technology to support auditing tasks have not been developed. The purpose of this study is to develop a mobile auditing assistance system to support auditing tasks in an organization. A mobile auditing assistance system is implemented with five functions: auditing tasks, optimal path planning, searching assets location, scanning assets, and returning auditing tasks. Based on a cost and benefits analysis, a mobile auditing assistance system can reduce costs, increase benefits, and improve firm productivity and performance. Firms can use this approach to design their own mobile auditing assistance system. This allows auditors to perform auditing tasks independently on a real time basis and achieve the objectives of a mobile auditing assistance system.

scholarly journals A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 99 ◽  
Author(s):  
Haiyan Wang ◽  
Zhiyu Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robots ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Elastic Particle

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

scholarly journals An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning

2019 ◽  
Vol 9 (6) ◽  
pp. 1057 ◽  
Author(s):  
Chenguang Liu ◽  
Qingzhou Mao ◽  
Xiumin Chu ◽  
Shuo Xie
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimal Path ◽  
Water Current ◽  
Path Generation ◽  
Collision Risk ◽  
A Algorithm ◽  
Risk Models ◽  
Planning Methods ◽  
Path Cost

A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real scenarios. The results of experiments show that the proposed A-Star path planning methods can deal with the problems denoted in this article well, and realize the trade-off between the path length and the navigation safety.

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