Randomized Bidirectional B-Spline Parameterization Motion Planning

Elbanhawi, Mohamed; Simić, Milan; Jazar, Reza N.

doi:10.1109/tits.2015.2477355

Cited by 73 publications

(51 citation statements)

References 64 publications

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“…Since the path is known, we need to reconstruct the mapping between the known path and the speed profile, then represent the speed profile with parameters determined by the prescribed path. A rich set of parameterized path representations has been proposed in the literature, including B-spline [23,24], Bezier curve [25,26], clothoid [27,28], polynomial curve [29] and polynomial spiral [30,31]. It's trivial to convert all the listed curve models to a simple waypoints representation, but not vice versa.…”

Section: Path Representationmentioning

confidence: 99%

See 1 more Smart Citation

Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

Zhang¹,

Chen²,

Waslander³

et al. 2018

Preprint

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In this paper, we present a complete, flexible and safe convex-optimization-based method to solve speed planning problems over a fixed path for autonomous driving in both static and dynamic environments. Our contributions are five fold. First, we summarize the most common constraints raised in various autonomous driving scenarios as the requirements for speed planner developments and metrics to measure the capacity of existing speed planners roughly for autonomous driving. Second, we introduce a more general, flexible and complete speed planning mathematical model including all the summarized constraints compared to the state-of-the-art speed planners, which addresses limitations of existing methods and is able to provide smooth, safety-guaranteed, dynamic-feasible, and time-efficient speed profiles. Third, we emphasize comfort while guaranteeing fundamental motion safety without sacrificing the mobility of cars by treating the comfort box constraint as a semi-hard constraint in optimization via slack variables and penalty functions, which distinguishes our method from existing ones. Fourth, we demonstrate that our problem preserves convexity with the added constraints, thus global optimality of solutions is guaranteed. Fifth, we showcase how our formulation can be used in various autonomous driving scenarios by providing several challenging case studies in both static and dynamic environments. A range of numerical experiments and challenging realistic speed planning case studies have depicted that the proposed method outperforms existing speed planners for autonomous driving in terms of constraint type covered, optimality, safety, mobility and flexibility.

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Section: Path Representationmentioning

confidence: 99%

“…By doing so, we conserve the freedoms to explore full mobility of cars and capacity of breaking the comfort box constraint to recover the feasibility when necessary. The exact expression of the semi-hard constraint is shown in (24).…”

Section: Comfort Box Constraintsmentioning

confidence: 99%

Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

Zhang¹,

Chen²,

Waslander³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Localisation, mapping and motion planning processes are running in MATLAB environment. The only process running outside of MATLAB is a Rapidly exploring Random Tree path planning generation, implemented with Python 2.7 as already explained and presented in [13].…”

Section: Future Workmentioning

confidence: 99%

From Automotive to Autonomous: Time-Triggered Operating Systems

Spichkova

Simić

Schmidt

2016

Smart Innovation, Systems and Technologies

Self Cite

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This paper presents an approach for application of time-triggered paradigm to the domain of autonomous systems. Autonomous systems are intensively used in areas, or situations, which could be dangerous to humans or which are remote and hardly accessible. In the case when an autonomous system is safety critical and should react to the environmental changes running within a very limited time frame, we deal with the same kind of problems as automotive and avionic systems: timing properties and their analysis become a crucial part of the system development. To analyse timing properties and to show the fault-tolerance of the communication, a predictable timing of the system is needed.

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“…B-spline curve is one of the most efficient curve interpolations and has been widely applied in many disciplines, such as medical imaging [27], geometric modeling [28], surface reconstruction [29] and position control [30]. With the properties of the B-spline curve, this interpolation scheme is practically useful for path smoothing [31], [32]. However, the fitting degree between the B-spline curve and the original segments does not meet the requirements in some cases, which may affect the path safety.…”

Section: Introductionmentioning

confidence: 99%

A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

Yang

Miao

et al. 2019

IEEE Trans. Ind. Electron.

130

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This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.

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Randomized Bidirectional B-Spline Parameterization Motion Planning

Cited by 73 publications

References 64 publications

Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

From Automotive to Autonomous: Time-Triggered Operating Systems

A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

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