A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance

Matveev, Alexey S.; Teimoori, Hamid; Savkin, Andrey V.

doi:10.1016/j.automatica.2011.01.024

Cited by 173 publications

(149 citation statements)

References 21 publications

Supporting

Mentioning

149

Contrasting

Order By: Relevance

“…For example, in ref. [181] , the navigation strategy was based on a sliding-mode navigation law; uses the minimum obstacle distance as input; and is suitable for guiding nonholonomic vehicles traveling at constant speed. In ref.…”

Section: Distance-basedmentioning

confidence: 99%

“…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. It travels with a constant speed v and is controlled by the angular velocity u limited by a given constant u.…”

Section: Sliding Mode Controlmentioning

confidence: 99%

“…It is shown that under some technical and partly unavoidable assumptions, the control law (5) does ensure the desired non-local convergence d(t) → d 0 as t → ∞ with respecting the safety margin d safe . In doing so, sliding-mode effects still play an essential role though the overall trajectory may be more complicated than the union of an initial turn and subsequent sliding motion, like in the case from [181]. Some other reactive controllers fed by data from perpendicularly mounted sensors have been proposed in refs.…”

Section: Sliding Mode Controlmentioning

confidence: 99%

See 2 more Smart Citations

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

2014

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Distance-basedmentioning

confidence: 99%

Section: Sliding Mode Controlmentioning

confidence: 99%

Section: Sliding Mode Controlmentioning

confidence: 99%

See 1 more Smart Citation

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

2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…The cost functions depend on the problem and may be the time of travel or deviation from a reference path or any other dynamic or kinematic property of the vehicle. Several optimization methods for the path planning problem of robots have been applied in the past (Yahja et al, 2000;Arras et al, 2002;Spenko et al, 2004;Minguez and Montano, 2005;Rashid et al, 2013) and some have recently been used in the autonomous vehicle researches (Purwin and D'Andrea, 2006;Yoon et al, 2009;Zhe et al, 2009;Matveev et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Global optimal path planning of an autonomous vehicle for overtaking a moving obstacle

Mashadi

Majidi

2014

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

“…The problem we consider is that of developing an algorithm that enables each agent to reconstruct the relative position of all the others along the curve, based on these intermittent measurements and on the knowledge of the agents' dynamics. Problems like this are often found when considering a surveillance problem in which a group of heterogeneous artificial agents, equipped with proximity sensors, must patrol a given closed route encircling a sensitive area [9,4,8]. The surveillance is effective if they appropriately space along the path and level out their speed to keep the formation.…”

mentioning

confidence: 99%

Estimating the relative position of mobile agents on Jordan curves from ambiguous proximity data

DeLellis¹,

Garofalo²,

Iudice³

et al. 2015

2015 Proceedings of the Conference on Control and Its Applications

View full text Add to dashboard Cite

In this paper, we aim at estimating the relative positions of an ensemble of mobile agents. Specifically, the agents are assumed to move along a C 0 Jordan closedcurve of the plane with different speeds. When two of them get sufficiently close in the Euclidean norm they can measure, with a certain degree of precision, their relative Euclidean distance. Based on the knowledge of agent dynamics the prediction-correction algorithm described in this paper enables each agent to recursively estimate the distance on the C 0 curve of all other agents from this intermittent flow of ambiguous and uncertain proximity data. On the basis of these position estimates, any form of decentralized agent formation control could be implemented on the closed curve. The theoretical analysis of the estimation algorithm is validated numerically using a representative example. IntroductionIn this paper, we consider a group of agents moving along a closed curve in the plane, at given (possibly time varying) speeds. Each agent can spot other agents that are close to him in the plane (but not necessarily close on the curve) and gather, with a given sampling rate, a measurement of their Euclidean distance. The problem we consider is that of developing an algorithm that enables each agent to reconstruct the relative position of all the others along the curve, based on these intermittent measurements and on the knowledge of the agents' dynamics. Problems like this are often found when considering a surveillance problem in which a group of heterogeneous artificial agents, equipped with proximity sensors, must patrol a given closed route encircling a sensitive area [9,4,8]. The surveillance is effective if they appropriately space along the path and level out their speed to keep the formation. This formation control problem requires, as a first step, the estimation of the agents' relative position on the curve starting from sampled measurements of their Euclidean distance on the plane. Motivated by similar surveillance * Supported by the RETE DI ECCELLENZA MASTRI, P.O.R. Campania FSE 2007 2013, Asse V.† Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125, Naples, Italy.problems, in the recent literature on multi-agent systems [14,3,6,2,15], the problem of achieving balanced formations along circular paths has been considered. Specifically, the problem has been tackled with given assumptions on the connectivity among the agents: an allto-all connectivity was assumed in [12,13], while fixed and connected graphs were considered in [7,11]. Recently, an algorithm was proposed to estimate the relative phase among oscillators from proximity data, see [1].All cited authors, although considering different agent dynamics, do make reference to circular paths. In this paper, instead, we aim at extending the algorithm proposed in [1] to cope with generic closed curves. As the agents have, in general, different speeds, the proximity-based interaction between them implies alternate activations and de...

show abstract

A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance

Cited by 173 publications

References 21 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

Global optimal path planning of an autonomous vehicle for overtaking a moving obstacle

Estimating the relative position of mobile agents on Jordan curves from ambiguous proximity data

Contact Info

Product

Resources

About