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

Noreen, Iram; Khan, Amna; Asghar, Khurshid; Habib, Zulfiqar

doi:10.3390/sym11070945

Cited by 33 publications

(23 citation statements)

References 27 publications

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“…The goal of this work is to obtain a numerical value that represents the expected performance of each one of the most-used path planning methods and techniques, and in that sense, several path planners are reviewed, studied and implemented in a controlled environment for a surface vehicle. Although there are many reviews on this topic [17,18], the focus in this work is to provide not only a standardized path-planning testing procedure, but also results for an autonomous surface vehicle in a low current speed environment such is the Ypacarai Lake (Paraguay). The most well-known/used algorithms are reviewed, implemented and tested in a controlled environment.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Peralta

Arzamendia

Gregor

et al. 2020

Sensors

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Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.

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Section: Related Workmentioning

confidence: 99%

A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Peralta

Arzamendia

Gregor

et al. 2020

Sensors

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“…By comparison, the Rapidly Exploring Random Trees (RRT) algorithm is a feasible, relatively fast, and compatible solution in a searching space which avoids colliding with obstacles [21]. It has mainly been applied to robot maneuvering [22][23][24][25][26] and modified to adapt to trajectory curvature constraint [27], collision detection [28], quicker planning by the triangular inequality method [29], and to consider the bias goal factor [30]; however, the above were all carried out in numerical simulation only. Recent advances in the RRT algorithm have been extending to path planning of autonomous vehicles by numerical simulation [31,32].…”

Section: Introductionmentioning

confidence: 99%

Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Yang

Lin

2021

Sensors

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Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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“…While the base RRT algorithms are inherently sequential, there are modules that can be parallelized such as the obstacle collision detection [9,20]. An experimental comparison is proposed in [46] Membrane Computing [58,24,61,62] is a computing paradigm inspired from the living cells and it provides distributed, massively parallel devices, see [1,66,67]. Models in Membrane Computing are generically called P systems and they have been used in different contexts.…”

Section: Introductionmentioning

confidence: 99%

A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

Pérez–Hurtado

Amor

Zhang

et al. 2020

ICA

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In recent years, incremental sampling-based motion planning algorithms have been widely used to solve robot motion planning problems in high-dimensional configuration spaces. In particular, the Rapidly-exploring Random Tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT* are popular algorithms used in real-life applications due to its desirable properties. Such algorithms are inherently iterative, but certain modules such as the collision-checking procedure can be parallelized providing significant speedup with respect to sequential implementations. In this paper, the RRT and RRT* algorithms have been adapted to a bioinspired computational framework called Membrane Computing whose models of computation, a.k.a. P systems, run in a non-deterministic and massively parallel way. A large number of robotic applications are currently using a variant of P systems called Enzymatic Numerical P systems (ENPS) for reactive controlling, but there is a lack of solutions for motion planning in the framework. The novel models in this work have been designed using the ENPS framework. In order to test and validate the ENPS models for RRT and RRT*, we present two ad-hoc implementations able to emulate the computation of the models using OpenMP and CUDA. Finally, we show the speedup of our solutions with respect to sequential baseline implementations. The results show a speedup up to 6x using OpenMP with 8 cores against the sequential implementation and up to 24x using CUDA against the best multi-threading configuration.

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

Cited by 33 publications

References 27 publications

A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

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