2D Dubins Path in Environments with Obstacle

Yang, Daoyong; Li, Didong; Sun, Hongwei

doi:10.1155/2013/291372

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…Since then, a number of modifications and improvements have been proposed. In [ 84 ], authors have considered using Dubin’s curve with obstacle avoidance. Unmanned Aerial Vehicle (UAVs) navigation using predictive vector field control is proposed in [ 85 ] which uses Dubin curves.…”

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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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

View full text Add to dashboard Cite

show abstract

“…Segmental paths include the linear path, circular path, segmental function path, etc. Continuous paths includes the B-spline curve, spline function, polynomial function, Dubins curve [12], clothoid curve [13], etc. The above methods have the characteristics of optimizing velocity and acceleration curves, but the planned trajectory cannot change with dynamic obstacles.…”

Section: Introductionmentioning

confidence: 99%

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

Jia,

Zeng,

Feng

et al. 2023

Applied Sciences

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In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle information according to the shape information and position information of the obstacle. Then, the feature points of the obstacle are converted into the feature points of the curve, according to the scale coefficient and the center point of amplification. Furthermore, a Bezier curve representing the motion path at this moment is generated to realize real-time adjustment of the path. In addition, the 5-degree Bezier curve planning method consider the start direction and the end direction is used in the path planning to avoid the situation of abrupt change with oscillation of the trajectory. Finally, the GTVP method is applied to multi-obstacle environment to realize global time-varying dynamic path planning. Through theoretical derivation and simulation, it can be proved that the path planned by the GTVP method can meet the performance requirements of global regulation, real-time change and multi-obstacle avoidance simultaneously.

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“…There mainly four types of the geometry-based approach, i.e. the Dubin curve [21], Bezier curve [22,23], spline [24] and polynomial curves [25]. Besides, the numerical optimization-based methods find feasible trajectories based on the differential cost function and constraints [26].…”

Section: Introductionmentioning

confidence: 99%

Human-Machine Cooperative Trajectory Planning and Tracking for Safe Automated Driving

Huang

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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This paper investigates a human-machine cooperative trajectory planning and tracking control approach for automated vehicles. The proposed method is developed based on a novel algorithm of cooperative human-machine rapidly-exploring random (HM-RRT) for path planning, together with the risk assessment of driver behavior. First, the driver's behaviour is assessed according to the information of the predicted vehicle trajectory, the identified safe driving area and the driving risks evaluated in both lateral and longitudinal directions. Based on the driver's expected driving task, when driving risks are identified by real-time assessment, then the human-machine cooperation is activated during trajectory planning. By HM-RRT, the newly developed safety assurance mechanism for path planning, the cooperative trajectory is then generated, which incorporates the driver's desire and actions and automation's corrective actions, to ensure the safety, stability and smoothness of the human-vehicle system. The simulation and experimental results show that the proposed HM-RRT algorithm can effectively improve the convergence rate and reduce the computation load, comparing to the conventional method. Beyond this, the proposed human-machine cooperation approach is able to simultaneously ensure the safety, stability and smoothness of the vehicle and largely reduce human-machine conflicts in real-time applications, demonstrating its feasibility and effectiveness.

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2D Dubins Path in Environments with Obstacle

Cited by 19 publications

References 18 publications

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

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

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

Human-Machine Cooperative Trajectory Planning and Tracking for Safe Automated Driving

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