Curve Tracking Control for Autonomous Vehicles with Rigidly Mounted Range Sensors

Kim, Jonghoek; Zhang, Fumin; Egerstedt, Magnus

doi:10.1007/s10846-009-9308-z

Cited by 32 publications

(32 citation statements)

References 35 publications

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“…[270,271], the navigation law calculation is based purely on the length of the detection ray, while in refs. [177,273] additional information is also collected to estimate the tangential angle of the obstacle at the intersection point, while 125 also requires the boundary curvature. Additional information would presumably result in improved behavior, however comparisons of closed loop performance are difficult.…”

Section: Distance-basedmentioning

confidence: 99%

“…Some other examples of perceptually and computationally demanding input variables not confined to the area of visual navigation include the closest point on the obstacle boundary, the distance to this point, or the value of another function determined by the entire boundary; see, e.g., [24,41,75,112,165,174,178,188,230,306,307,312] for representative samples. Another such variable employed by many proposed controllers, see, e.g., [125,165,307], is the boundary curvature, which is particularly sensitive to corruption by measurement noises since this is a second derivative property.…”

Section: Sliding Mode Controlmentioning

confidence: 99%

“…Some other reactive controllers fed by data from perpendicularly mounted sensors have been proposed in refs. [125,270]. The control law from [270] is aimed at pure obstacle avoidance, with no objective to follow the boundary with a pre-specified margin.…”

Section: Sliding Mode Controlmentioning

confidence: 99%

“…An example of such behavior may be slowing down at concavities of a boundary and speeding up otherwise, or completely skipping concavities of sufficiently small size that serve only to introduce singularities into the motion. 125,181 One such approach using abstract obstacle information is the VisBug class of algorithms, which navigates toward a visible edge of an obstacle inside the detection range (see, e.g., [140,160,197]). However, these algorithms are concerned with the overall strategy, and are not concerned with details relating to vehicle kinematics or the sensor model.…”

Section: Full Information-basedmentioning

confidence: 99%

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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: Distance-basedmentioning

confidence: 99%

Section: Sliding Mode Controlmentioning

confidence: 99%

Section: Sliding Mode Controlmentioning

confidence: 99%

Section: Full Information-basedmentioning

confidence: 99%

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

2014

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“…While our "local" analysis is still myopic, we are able to provide conditions directly related to the robot state and curvature-like perturbation terms which can provide almost-global guarantees against failure, and which are not considered in typical controller stability analyses [26], [27] in the prior literature from the best of our reading.…”

Section: B Organization and Contributions Of The Papermentioning

confidence: 99%

Toward dynamical sensor management for reactive wall-following

Koditschek

2013

2013 IEEE International Conference on Robotics and Automation

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We propose a new paradigm for reactive wallfollowing by a planar robot taking the form of an actively steered sensor model that augments the robot's motion dynamics. We postulate a foveated sensor capable of delivering third-order infinitesimal (range, tangent, and curvature) data at a point along a wall (modeled as an unknown smooth plane curve) specified by the angle of the ray from the robot's body that first intersects it. We develop feedback policies for the coupled (point or unicycle) sensorimotor system that drive the sensor's foveal angle as a function of the instantaneous infinitesimal data, in accord with the trade-off between a desired standoff and progress-rate as the wall's curvature varies unpredictably in the manner of an unmodeled noise signal. We prove that in any neighborhood within which the thirdorder infinitesimal data accurately predicts the local "shape" of the wall, neither robot will ever hit it. We empirically demonstrate with comparative physical studies that the new active sensor management strategy yields superior average tracking performance and avoids catastrophic collisions or wall losses relative to the passive sensor variant. Abstract-We propose a new paradigm for reactive wallfollowing by a planar robot taking the form of an actively steered sensor model that augments the robot's motion dynamics. We postulate a foveated sensor capable of delivering third-order infinitesimal (range, tangent, and curvature) data at a point along a wall (modeled as an unknown smooth plane curve) specified by the angle of the ray from the robot's body that first intersects it. We develop feedback policies for the coupled (point or unicycle) sensorimotor system that drive the sensor's foveal angle as a function of the instantaneous infinitesimal data, in accord with the trade-off between a desired standoff and progress-rate as the wall's curvature varies unpredictably in the manner of an unmodeled noise signal. We prove that in any neighborhood within which the thirdorder infinitesimal data accurately predicts the local "shape" of the wall, neither robot will ever hit it. We empirically demonstrate with comparative physical studies that the new active sensor management strategy yields superior average tracking performance and avoids catastrophic collisions or wall losses relative to the passive sensor variant.

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