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Full-Text Articles in Controls and Control Theory
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
George J. Pappas
In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For static deployment problems, a classical way of designing high- level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive deployment algorithms that may not be adaptive to uncertain dynamic environments. We address this challenge by showing that algorithms for a variety of deployment scenarios in stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, …
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
George J. Pappas
In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For static deployment problems, a classical way of designing high- level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive deployment algorithms that may not be adaptive to uncertain dynamic environments. We address this challenge by showing that algorithms for a variety of deployment scenarios in stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, …
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
Adaptive Robot Deployment Algorithms, Jerome Le Ny, George J. Pappas
George J. Pappas
In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For static deployment problems, a classical way of designing high- level feedback motion planners is to implement a gradient descent scheme on a suitably chosen objective function. This can lead to computationally expensive deployment algorithms that may not be adaptive to uncertain dynamic environments. We address this challenge by showing that algorithms for a variety of deployment scenarios in stochastic environments and with noisy sensor measurements can be designed as stochastic gradient descent algorithms, …