Trajectory planning and sliding-mode control based trajectory-tracking for cybercars

Șolea, Răzvan; Nunes, Urbano

doi:10.3233/ica-2007-14104

Cited by 67 publications

(46 citation statements)

References 8 publications

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“…Examples include but are not limited to target following, 183,239 environmental extremum seeking, 182 and trajectory tracking. 259,299 Sliding-mode-based boundary following with a pre-specified margin was addressed in refs. [177,181] for a planar under-actuated nonholonomic vehicle or wheeled mobile robot modeled as unicycle.…”

Section: Sliding Mode Controlmentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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SUMMARYWe review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

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Section: Sliding Mode Controlmentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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“…Control methods include sliding mode control [1], [2], [3], flatness-based control [4], [5], optimal linear-quadratic control [6], backstepping-based strategies [7], [8], [9], optimal preview control [10] and optimization-based methods like model predictive control (MPC) [11], [12].…”

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confidence: 99%

Comparison of trajectory tracking controllers for autonomous vehicles

Calzolari

Schürmann

Althoff

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Controlling autonomous vehicles typically has two main components: planning a trajectory and tracking this trajectory using feedback controllers. To benefit from the recent progress in planning algorithms, it is key that the underlying tracking controller is able to follow the planned trajectory as desired. In emergency situations in particular, it is crucial that feedback controllers steer the vehicle as close as possible to the planned trajectory to remain within a safe corridor. While there exists much work on the design of trajectory and path tracking controllers for vehicles, little work has been done to systematically compare different approaches, especially when considering extreme situations, uncertain parameters, and disturbances. In this work, we compare eight tracking controllers in a systematic way, each of them representing a different controller family. By not only considering nominal behavior, but also sensor noise and uncertain parameters, we obtain for the first time a broad comparison of the behavior of different controllers in various situations.

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“…AGVs and mobile robots share, in general, common issues regarding localization [1], trajectory planning [2], path-following, obstacle avoidance, local navigation [3], communication, sensor fusion, among others. However, some current applications impose new requirements for real-world cases, namely, dynamic path planning, navigation in cluttered environments, and human safety.…”

Section: Introductionmentioning

confidence: 99%

“…Geometric solutions, including the control law used in the Stanley autonomous vehicle [4] and Pure Pursuit [5], were proven to be reliable and suitable to real world situations. In [2], sliding mode control was applied to differential and car-like robots, providing robust and stable control laws to systems under uncertainties and external disturbances. In [6] and [7], Fuzzy logic controllers were proposed and deployed successfully in autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

An RRT-based navigation approach for mobile robots and automated vehicles

Garrote

Premebida

Silva

et al. 2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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Abstract-Advances in autonomous navigation, safety, and natural-landmark based localization, are among the key objectives in the development of the next generation of autonomous vehicles, to be deployed in manufacturing and semi-structured environments. In this paper, autonomous navigation and collision detection will be focused, where it is proposed a novel navigation approach that incorporates a RRT-based dynamic path planning and a path-following controller. Safety issues are taken into account in the form of a laser-based object detection and tracking.Experimental results obtained in a virtual environment provide evidence that our proposed navigation method is promising for real-world applications.

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Trajectory planning and sliding-mode control based trajectory-tracking for cybercars

Cited by 67 publications

References 8 publications

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Comparison of trajectory tracking controllers for autonomous vehicles

An RRT-based navigation approach for mobile robots and automated vehicles

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