Path planning and control of differential and car-like robots in narrow environments

Nagy, Akos; Csorvási, Gábor; Kiss, Domokos

doi:10.1109/sami.2015.7061856

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…The Stanley method is used for trajectory tracking, the kinematic model of the vehicle is considered, and the discontinuous trajectory tracking error with a distance of 1 m under different vehicle speeds is tested and the experimental results are shown in the Table 2 below. 40 In fact, the control strategy based on the approach implemented at Stanley’s autonomous car has consistently shown better results than other approaches based on sliding mode control.…”

Section: Improved A* Algorithmmentioning

confidence: 96%

“…Collision avoidance was achieved through the proposed fuzzy logic control. Nagy et al 40 presents a comprehensive solution for path planning and control of two popular types of autonomous wheeled vehicles. The planning algorithm employs a rapidly exploring random tree based global planner (RTR), and present a local steering method (C*CS) which obtains a path consisting circular and straight movements based on the primary RTR-path, Simulations and real experiments show the effectiveness of these methods, even is constrained environments containing narrow corridors and passages.…”

Section: Introductionmentioning

confidence: 99%

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Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Wang

Liu

Yao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Path planning is a fundamental problem in the aspect of autonomous driving. A-star (A*) algorithm is a heuristic algorithm for path planning. However, there are two problems need to be solved in the traditional A-star algorithm: firstly, the tracking error caused by vehicle speed and vehicle size are not considered in path planning; secondly, the kinematics constraints of the vehicle itself and the smoothness of the path in the actual driving process are not considered. Therefore, this paper proposes an improved A-star algorithm to address the above deficiencies. By designing the collision cost heuristic function based on the position of obstacles, the vehicle contour and speed are considered and the vehicle safety is improved by setting a safe space around the vehicle, and establishing a steering cost heuristic function based on the control of heading angle difference, the vehicle dynamics constraints are satisfied, so as to improve the feasibility of path planning. The experimental results show that the improved A-star algorithm can avoid vehicle contours collision at different speeds and output a smoother path, and effectively generate higher quality path.

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Section: Improved A* Algorithmmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Wang

Liu

Yao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Previous versions of the proposed two-step planning approach can be found in [46][47][48][49], where the local planner used only straight and circular segments, resulting in a discontinuous curvature profile. The currently proposed generalized version uses clothoids as well (similarly to [31]).…”

Section: The Rtr + Tts-plannermentioning

confidence: 99%

Autonomous Path Planning for Road Vehicles in Narrow Environments: An Efficient Continuous Curvature Approach

Kiss

Tevesz

2017

Journal of Advanced Transportation

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In this paper we introduce a novel method for obtaining good quality paths for autonomous road vehicles (e.g., cars or buses) in narrow environments. There are many traffic situations in urban scenarios where nontrivial maneuvering in narrow places is necessary. Navigating in cluttered parking lots or having to avoid obstacles blocking the way and finding a detour even in narrow streets are challenging, especially if the vehicle has large dimensions like a bus. We present a combined approximation-based approach to solve the path planning problem in such situations. Our approach consists of a global planner which generates a preliminary path consisting of straight and turning-in-place primitives and a local planner which is used to make the preliminary path feasible to car-like vehicles. The approximation methodology is well known in the literature; however, both components proposed in this paper differ from existing similar planning methods. The approximation process with the proposed local planner is proven to be convergent for any preliminary global paths. The resulting path has continuous curvature which renders our method well suited for application on real vehicles. Simulation experiments show that the proposed method outperforms similar approaches in terms of path quality in complicated planning tasks.

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“…An increasing number of authors [21,27,28] use a virtual reality simulation platform to simulate the mobile robot's autonomous behavior. A comparison and possibilities of software tools for simulating mobile robot model's behavior, connected to environment sensors and integrated into the same, virtually defined environments, are given by Ivaldi et al [29].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements

Pavlic¹,

Kušec²,

Radočaj³

et al. 2021

Applied Sciences

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In recent years in mobile robotics, the focus has been on methods, in which the fusion of measurement data from various systems leads to models of the environment that are of a probabilistic type. The cognitive model of the environment is less accurate than the exact mathematical one, but it is unavoidable in the robot collaborative interaction with a human. The subject of the research proposed in this paper is the development of a model for learning and planning robot operations. The task of operations and mapping the unknown environment, similar to how humans do the same tasks in the same conditions has been explored. The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot. The mobile robot’s motion with developed artificial neural networks and genetic algorithms is defined. The transfer method of obtained knowledge from simulated to a real system (Sim-To-Real; STR) is proposed. This method includes a training step, a simultaneous reasoning step, and an application step of trained and learned knowledge to control a real robot’s motion. Use of the basic cognitive elements language, a robot’s environment, and its correlation to that environment is described. Based on that description, a higher level of information about the mobile robot’s environment is obtained. The information is directly generated by the fusion of measurement data obtained from various systems.

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Path planning and control of differential and car-like robots in narrow environments

Cited by 12 publications

References 14 publications

Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Improved A-star algorithm based on multivariate fusion heuristic function for autonomous driving path planning

Autonomous Path Planning for Road Vehicles in Narrow Environments: An Efficient Continuous Curvature Approach

Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements

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