Systematic comparison of path planning algorithms using PathBench

Hsueh, Hao-Ya; Toma, Alexandru-Iosif; Jaafar, Hussein Ali; Stow, Edward; Murai, Riku; Saeedi, Sajad

doi:10.1080/01691864.2022.2062259

Cited by 5 publications

(4 citation statements)

References 60 publications

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“…Path planning algorithms are classified into graph-based, sampling-based, sensor-based, and learning-based approaches [7]. Graph-based path planners such as A * [8] generally generate an optimal path with sharp changes in direction that need to be smoothed further with additional techniques, such as the Bézier curve.…”

Section: A Related Workmentioning

confidence: 99%

“…Property 3: Derivative-The derivative of an m th degree Bézier curve, Ṗm−1 (t), is a Bézier curve of degree (m − 1) described by equation (7). Its control points are calculated as the difference of two successive control points of the original Bézier curve (P m (t)).…”

Section: B Bézier Curve Definitionmentioning

confidence: 99%

“…7) In our work, this property is used to calculate the robot position's first and second derivatives. When applied to the position Bézier curve (P m (t)), it results in the first derivative components ( Ṗx…”

mentioning

confidence: 99%

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Optimal Kinodynamic Trajectory Planner for Mobile Robots in an Unknown Environment Using Bézier Contours

Mazen,

Faied,

Krishnan

2024

IEEE Access

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Trajectory planning in the field of mobile robotics involves the generation of a trajectory to navigate a robot from a start state to a goal state. One widely employed technique involves a twostep approach: a path planner generates a path made up of piecewise linear segments with sharp turns, which are then smoothed in the trajectory generation step. In contrast, this work formulates trajectory generation as an optimization problem based on the Bézier curve, denoted as 'BTP', to generate the robot's trajectory in one step. It uses a weighted objective function to adopt different optimization strategies, while considering the robot kinematics and dynamics limitations. BTP adopts matrix-based formulations for all mathematical operations to enable dynamic adjustment of the degree of the Bézier curve during the optimization process, if convergence is not obtained with the current degree. Additionally, BTP guarantees that the robot's trajectory is always within the open space identified by the robot's sensors. The efficacy of BTP has been evaluated through simulations and real-world experimentation using different scenarios and objective functions. Finally, the performance is benchmarked against some of the existing trajectory planners.

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Section: A Related Workmentioning

confidence: 99%

Section: B Bézier Curve Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Kinodynamic Trajectory Planner for Mobile Robots in an Unknown Environment Using Bézier Contours

Mazen,

Faied,

Krishnan

2024

IEEE Access

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“…Conventional approaches to mobile robot navigation encompass fuzzy logic-based controllers and potential field methods [3], [4]. While these methods find widespread application due to their simplicity, they often have limitations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of popular path planning algorithms

Kara

2024

International Journal of Electronics and Telecommunications

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The navigation of mobile robots is a key element of autonomous systems, which allows robots to move effectively and securely in changing environments with greater autonomy and precision. This study aims to provide researchers with a comprehensive guide for selecting the best path-planning methods for their particular projects. We evaluate some popular algorithms that are regularly used in mobile robot navigation, in order to demonstrate their specifications and determine where they are most effective. For example, one algorithm is used to model the problem as a standard graph, and another algorithm is found to be the most suitable for highly dynamic and highly dimensional environments, due to its robust path-planning capabilities and efficient route construction. We also filter high-performance algorithms in terms of computational complexity, accuracy, and robustness. In conclusion, this study provides valuable information on its individual strengths and weaknesses, helping robotics and engineers make informed decisions when selecting the most appropriate algorithm for their specific applications.

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