Desired orientation RRT (DO-RRT) for autonomous vehicle in narrow cluttered spaces

Shin, Seokmin; Ahn, Joonwoo; Park, Jaeheung

doi:10.1109/iros.2016.7759696

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…Bi-RRT* with hybrid curvature steer and cost function design is implemented to explore environments based on two RRT* trees to generate a solution with continuous curvature and the desired pattern [7]. Also, the desired orientation RRT (DO-RRT) is proposed to overcome the exploring inefficiency in narrow spaces [8]. Nevertheless, the uncertainty of path quality is still not solved and the method in [8] is limited to some template of goal configuration.…”

Section: Literature Surveymentioning

confidence: 99%

“…Also, the desired orientation RRT (DO-RRT) is proposed to overcome the exploring inefficiency in narrow spaces [8]. Nevertheless, the uncertainty of path quality is still not solved and the method in [8] is limited to some template of goal configuration. Furthermore, a two-stage RRT planner is proposed in [9].…”

Section: Literature Surveymentioning

confidence: 99%

“…According to (8) and (9), most of the random polar coordinates focus on the region between the circles with radius and 3 , respectively, as shown in Figure 2(a).…”

Section: A Collision-avoidable Samplermentioning

confidence: 99%

See 2 more Smart Citations

An Autonomous Parking System of Optimally Integrating Bidirectional Rapidly-Exploring Random Trees and Parking-Oriented Model Predictive Control

Jhang

Lian

2020

IEEE Access

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Autonomous parking techniques can be used to tackle the lacking problem of parking spaces. In this paper, a sampling-based motion planner consisting of optimizing bidirectional rapidly-exploring random trees* (Bi-RRT*) and parking-oriented model predictive control (MPC) is proposed to properly deal with various parking scenarios. The optimal Bi-RRT* approach aims to improve the common defects of traditional sampling-based motion planners, such as uncertainties of path quality and consistency, and exploring inefficiency in narrow spaces. For this reason, the proposed motion planner is able to overcome strict environments with obstacles and narrow spaces. The parking-oriented MPC is then designed for steering and speed controls simultaneously for accurately and smoothly tracking parking paths. Furthermore, the proposed controller is dedicated to work under the practical scenarios, such as vehicle considerations, realtime control, and signal delay. To verify the effects of the proposed autonomous parking system, extensive simulations and experiments are conducted in common and strict parking scenarios, such as perpendicular parking, parallel parking. The simulation results not only verify the effects of each technical element, but also show the capability to deal with the various parking scenarios. Furthermore, various on-car experiments sufficiently demonstrate that the proposed system can be actually implemented in everyday life. INDEX TERMS Autonomous parking system, sampling-based motion planning, parking-oriented vehicle control, bidirectional rapidly-exploring random trees* (Bi-RRT*), model predictive control (MPC), perpendicular parking, parallel parking.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

An Autonomous Parking System of Optimally Integrating Bidirectional Rapidly-Exploring Random Trees and Parking-Oriented Model Predictive Control

Jhang

Lian

2020

IEEE Access

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“…But as admitted in the paper, the adopted algorithm was slow, and the planned path not smooth enough. In [15] orientation of the RRT seed was specified. But the orientation is dependent on the parking environment; the authors discussed perpendicular parking only.…”

Section: B Related Workmentioning

confidence: 99%

“…The overall algorithmic flow involves 3 stages, i.e. initialization (lines 1-2), RRT tree growth (lines 3-13), and path connection/optimization (lines [14][15]. The baseline RRT hinges on the randomized seeds-biased exploration of the environment, which is asymptotically complete if the tree nodes number approaches infinity.…”

Section: Sampling-based Parking Solution a The Rrt-based Schemementioning

confidence: 99%

Knowledge-Biased Sampling-Based Path Planning for Automated Vehicles Parking

2020

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We consider automated vehicles operation in constrained environments, i.e. the automated parking (AP). The core of AP is formulated as a path planning problem, and Rapidly-exploring Randomized Tree (RRT) algorithm is adopted. To improve the baseline RRT, we propose several algorithmic tweaks, i.e. reversed RRT tree growth, direct tree branch connection using Reeds-Shepp curves, and RRT seeds biasing via regulated parking space/vehicle knowledge. We prove that under these tweaks the algorithm is complete and feasible. We then examine its performance (time, success rate, convergence to the optimal path) and scalability (to different parking spaces/vehicles) via batched simulations. We also test it using a real vehicle in a realistic parking environment. The proposed solution presents itself more applicable when compared with other baseline algorithms. INDEX TERMSAutomated vehicles; Automated parking; Sampling-based path planning; Knowledgebased biasing.

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Hierarchical Parking Path Planning Based on Optimal Parking Positions

Zhang

Chen

et al. 2023

Automot. Innov.

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Desired orientation RRT (DO-RRT) for autonomous vehicle in narrow cluttered spaces

Cited by 14 publications

References 14 publications

An Autonomous Parking System of Optimally Integrating Bidirectional Rapidly-Exploring Random Trees and Parking-Oriented Model Predictive Control

An Autonomous Parking System of Optimally Integrating Bidirectional Rapidly-Exploring Random Trees and Parking-Oriented Model Predictive Control

Knowledge-Biased Sampling-Based Path Planning for Automated Vehicles Parking

Hierarchical Parking Path Planning Based on Optimal Parking Positions

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