Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

Kang, Jin Gu; Lim, Dong‐Woo; Choi, Yong-Sik; Jang, Won Il; Jung, Jin‐Woo

doi:10.3390/s21020333

Cited by 79 publications

(46 citation statements)

References 26 publications

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“…Table 1 lists the performance of the computer used in the simulation. The simulator used for the simulation [6] was developed based on C# WPF (Microsoft Visual Studio Community 2019 Version 16.1.6 Microsoft .NET Framework Version 4.8.03752), and only a single thread was used for the calculations, except for the visual part. Indeed, there might be differences in the planning time during simulation depending on the computer performance.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed "post triangular rewiring" method can be applied to path planning algorithms that do not guarantee optimality, such as RRT, and rewired based on the triangular inequality principle [6].…”

Section: Proposed Post Triangular Rewiring Methodsmentioning

confidence: 99%

“…The sampling-based algorithms [5][6][7][8][9][10], including the RRT algorithm, have the advantage of being able to plan a path in less time with less computation than the classical path planning algorithms, such as visibility graph-based [11], cell decomposition-based [12], and potential field-based [13] algorithms. However, they do not guarantee optimality, and have the disadvantage that completeness is guaranteed probabilistically.…”

Section: Introductionmentioning

confidence: 99%

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Post Triangular Rewiring Method for Shorter RRT Robot Path Planning

Kang¹,

Jung²

2021

IJFIS

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This paper proposed the "post triangular rewiring" method that minimizes the sacrifice of planning time and overcomes the limit of optimality of sampling-based algorithms such as the rapidly exploring random tree (RRT) algorithm. The proposed "post triangular rewiring" method creates a path closer to the optimal path than the RRT algorithm before application, using the triangular inequality principle. Experiments were conducted to verify the performance of the proposed method. When the proposed method is applied to the RRT algorithm, the optimality efficiency increases compared to the planning time.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Post Triangular Rewiring Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Post Triangular Rewiring Method for Shorter RRT Robot Path Planning

Kang¹,

Jung²

2021

IJFIS

Self Cite

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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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“…For rigid-bodied robots, collision-free trajectory methods have proved reliable both in simulation and in physical systems [4,5]. Specifically, rapidly exploring random tree (RRT) algorithms are heavily used to generate paths for rigid robots [6][7][8]. These algorithms explore random points within a given range and assign a point with minimal cost as the new waypoint towards the goal.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance Path Planning for Worm-like Robot Using Bézier Curve

et al. 2021

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Worm-like robots have demonstrated great potential in navigating through environments requiring body shape deformation. Some examples include navigating within a network of pipes, crawling through rubble for search and rescue operations, and medical applications such as endoscopy and colonoscopy. In this work, we developed path planning optimization techniques and obstacle avoidance algorithms for the peristaltic method of locomotion of worm-like robots. Based on our previous path generation study using a modified rapidly exploring random tree (RRT), we have further introduced the Bézier curve to allow more path optimization flexibility. Using Bézier curves, the path planner can explore more areas and gain more flexibility to make the path smoother. We have calculated the obstacle avoidance limitations during turning tests for a six-segment robot with the developed path planning algorithm. Based on the results of our robot simulation, we determined a safe turning clearance distance with a six-body diameter between the robot and the obstacles. When the clearance is less than this value, additional methods such as backward locomotion may need to be applied for paths with high obstacle offset. Furthermore, for a worm-like robot, the paths of subsequent segments will be slightly different than the path of the head segment. Here, we show that as the number of segments increases, the differences between the head path and tail path increase, necessitating greater lateral clearance margins.

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Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

Cited by 79 publications

References 26 publications

Post Triangular Rewiring Method for Shorter RRT Robot Path Planning

Post Triangular Rewiring Method for Shorter RRT Robot Path Planning

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

Obstacle Avoidance Path Planning for Worm-like Robot Using Bézier Curve

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