Vehicle networks for gradient descent in a sampled environment

Bachmayer, Ralf; Leonard, Naomi Ehrich

doi:10.1109/cdc.2002.1184477

Cited by 186 publications

(152 citation statements)

References 7 publications

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“…Artificial potential methods were also used by Bachmayer and Leonard in [63] to design cooperative gradient climbing strategies for a group of vehicles that could each only take a scalar measurement at a time of the field of interest (e.g., ocean temperature). Capitalizing on this idea and building on the methods of [55,56],Ögren et al [4] developed a provable methodology to control the shape of the formation as well as the rotation, translation and expansion of the formation (see also [64]); this was used to design control strategies for a network of vehicles to adaptively climb gradients in the sampled field and thus robustly find peaks (see Section 3.1 below for a review of the implementation of this methodology in the field).…”

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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Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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“…As an illustration, we follow the approach proposed in [BL02] to stabilize formations of (non-oriented) particles: the desired formation is specified by the critical points of a scalar potential…”

Section: Stabilization Of Parallel Formationsmentioning

confidence: 99%

Collective Motion and Oscillator Synchronization

Sepulchre

Paley

Leonard

2004

Cooperative Control

110

114

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“…In robotics, these interactions can be considered as a special case of the potential field approach [3], in which robots are attracted by the goal and repelled by obstacles and other robots. In swarms, attractive forces are generally modeled through the gradient descent of specific functions [4], [5]. Unfortunately, as in regular potential field approaches, the presence of obstacles and local repulsion forces among the robots may cause convergence problems in general gradient descent approaches, mainly when robots are required to synthesize shapes.…”

Section: Introductionmentioning

confidence: 99%

No robot left behind: Coordination to overcome local minima in swarm navigation

Marcolino¹,

Chaimowicz²

2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-In this paper, we address navigation and coordination methods that allow swarms of robots to converge and spread along complex 2D shapes in environments containing unknown obstacles. Shapes are modeled using implicit functions and a gradient descent approach is used for controlling the swarm. To overcome local minima, that may appear in these scenarios, we use a coordination mechanism that reallocates some robots as "rescuers" and sends them to help other robots that may be trapped. Simulations and real experiments demonstrate the feasibility of the proposed approach.

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Vehicle networks for gradient descent in a sampled environment

Abstract: Fish in a school efficiently find the densest source of food by individually responding not only to local environmental stimuli but also to the behavior of nearest neighbors.

Cited by 186 publications

References 7 publications

Cooperative Vehicle Environmental Monitoring

Cooperative Vehicle Environmental Monitoring

Collective Motion and Oscillator Synchronization

No robot left behind: Coordination to overcome local minima in swarm navigation

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