Trajectory planning for automated parking using multi-resolution state roadmap considering non-holonomic constraints

Fuji, Hiroshi; Xiang, Jingyu; Tazaki, Yuichi; Levedahl, Blaine; Suzuki, Tatsuya

doi:10.1109/ivs.2014.6856433

Cited by 18 publications

(7 citation statements)

References 7 publications

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“…The resultant trajectory is smooth, however, the computational load is usually higher compared to other methods. There are various curve families that are used commonly such as clothoids [255], polynomials [256], Bezier curves [257] and splines [100].…”

Section: B Local Planningmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

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Section: B Local Planningmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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“…Recently, clothoids have been used in trajectory generation of autonomous Audi TTS car [ 102 ], and vehicles in VisLab Intercontinental Autonomous Challenge [ 103 ]. Some researchers have used clothoids for autonomous parallel parking with geometric continuous-curvature path planning in [ 104 , 105 , 106 ].…”

Section: Path Smoothing Using Special Curvesmentioning

confidence: 99%

Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges

Ravankar

Kobayashi

et al. 2018

Sensors

224

110

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Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.

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“…Trajectory smoothing is then performed to satisfy the kinematic constraints. Other methods that rely on pre-known knowledge of the parking space includes the artificial potential field [11] and state roadmap [12]. These solutions, however, usually hinge on careful tuning of the algorithmic parameters (cost in A*, the potential field, etc.…”

Section: B Related Workmentioning

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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Trajectory planning for automated parking using multi-resolution state roadmap considering non-holonomic constraints

Cited by 18 publications

References 7 publications

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges

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

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