Boundary following using gyroscopic control

Zhang, F.; Justh, Eric W.; Krishnaprasad, P. S.

doi:10.1109/cdc.2004.1429634

Cited by 61 publications

(68 citation statements)

References 7 publications

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“…[15] and an earlier version [16]. The insight also enabled the work in [17] and [18] to design (time varying) steering control for obstacle avoidance and boundary following for a single constant speed particle.…”

Section: Introductionmentioning

confidence: 99%

Coordinated patterns of unit speed particles on a closed curve

Zhang

Leonard

2007

Systems & Control Letters

120

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Section: Introductionmentioning

confidence: 99%

Coordinated patterns of unit speed particles on a closed curve

Zhang

Leonard

2007

Systems & Control Letters

120

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“…Here, r 0 represents the desired separation between the moving vehicle and the boundary curve for boundary-following. This form of Lyapunov function has been used in recent papers regarding boundary following and curve tracking using the closest point information in [2], and [6].…”

Section: ) Limmentioning

confidence: 99%

“…curving towards the vehicle, the curvature κ > 0. The above settings for the interaction between the vehicle and the boundary curve were introduced in [2]. The key idea of curve tracking control is to control the relative motion between the vehicle and the detected point.…”

Section: Boundary-following Model With Rigidly Mounted Range Sensorsmentioning

confidence: 99%

“…Otherwise, the reference point might stop to wait for the vehicle. In [2] and [3], a gyroscopic feedback Jonghoek Kim, Fumin Zhang, and Magnus Egerstedt are with the School of Electrical and Computer Engineering, Georgia Institute of Technology, USA. Email: jkim37@mail.gatech.edu, {fumin,magnus}@ece.gatech.edu law was used to model the interaction of a particle with an image particle representing the closest point on a closed curve bounding an obstacle.…”

Section: Introductionmentioning

confidence: 99%

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Curve Tracking Control for Autonomous Vehicles with Rigidly Mounted Range Sensors

Kim

Zhang

Egerstedt

2009

J Intell Robot Syst

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Abstract-In this paper, we present a feedback control law to make an autonomous vehicle with rigidly mounted range sensors track a desired curve. In particular, we consider a vehicle which has two range sensors that emit rays perpendicular to the velocity of the vehicle. Under such a sensor configuration, singularities are bound to occur in the feedback control law. Thus, to overcome this, we derive a hybrid strategy of switching between control laws close to the singularity.

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“…These ideas were extended further by Zhang and Leonard [65,66] to design provable control laws for cooperative level set tracking, whereby small vehicle groups could cooperate to generate contour plots of noisy, unknown fields, adjusting their formation shape to provide optimal filtering of their noisy measurements. Related work addressed environmental boundary tracking [67,68], coverage control [69,70], target tracking [71,72], and maximization of information [50].…”

Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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Boundary following using gyroscopic control

Cited by 61 publications

References 7 publications

Coordinated patterns of unit speed particles on a closed curve

Coordinated patterns of unit speed particles on a closed curve

Curve Tracking Control for Autonomous Vehicles with Rigidly Mounted Range Sensors

Cooperative Vehicle Environmental Monitoring

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