A Method of Enhancing Rapidly-Exploring Random Tree Robot Path Planning Using Midpoint Interpolation

Kang, Jin Gu; Choi, Yong-Sik; Jung, Jin‐Woo

doi:10.3390/app11188483

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…Accordingly, the proposed method can obtain a path that is close to optimal compared to the existing PTPMI [17] method. The interpolation point mr follows Equation (4):…”

Section: Backward Interpolation Processmentioning

confidence: 93%

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A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

Kang

Jung

2021

Sensors

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This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm’s performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples.

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“…Accordingly, the proposed method can obtain a path that is close to optimal compared to the existing PTPMI [17] method. The interpolation point mr follows Equation (4):…”

Section: Backward Interpolation Processmentioning

confidence: 93%

“…However, the amount of computation required to arrive at the convergence path is very high [16]. Recently, the post triangular processing of midpoint interpolation (PTPMI) method has been proposed to solve this problem [17]. Therefore, in this study, we prioritize finding a solution, which is the advantage of the sampling-based algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

Kang

Jung

2021

Sensors

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“…For example, it seems complicated to take into account the aeronautical constraints, as explained by Ligny et al [10] in their paper. Some papers [16][17][18] have proposed using a very efficient graph-based path planning algorithm. These methods seem to be very efficient in terms of computing time.…”

Section: Trajectory Generation Algorithmmentioning

confidence: 99%

An Accelerated Dual Fast Marching Tree Applied to Emergency Geometric Trajectory Generation

2022

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This paper addresses the generation of aircraft emergency trajectories with obstacle avoidance. After presenting in detail the fast marching tree algorithm, in this paper we propose an improvement of its performance. First, the free space checking function is sped up. Then, the algorithm is used twice, firstly with the sampling of a few points to generate an approximate trajectory, and secondly with a sampling of points close to the first computed trajectory to refine it. The proposed method significantly reduces the computing time of the emergency geometric trajectory generation.

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“…Global path planning algorithms can provide the optimal collision-free path; however, in a complex environment, the computations to obtain the solution take much more time than the movement of the mobile robot [6]. Satisfactory results with much lower computation times can be obtained by sampling-based rapidlyexploring random tree (RRT) instead of linear search of trajectory points [7,8]. Another problem related to global path planning algorithms is the dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

2021

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Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The Artificial Potential Field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of Artificial Potential Field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel Artificial Potential Field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

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A Method of Enhancing Rapidly-Exploring Random Tree Robot Path Planning Using Midpoint Interpolation

Cited by 11 publications

References 19 publications

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

An Accelerated Dual Fast Marching Tree Applied to Emergency Geometric Trajectory Generation

Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

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