Engineering Commons^™

Software And System Health Management For Autonomous Robotics Missions, Johann Schumann, Timmy Mbaya, Ole J. Mengshoel

Ole J Mengshoel

Advanced autonomous robotics space missions rely heavily on the flawless interaction of complex hardware, multiple sensors, and a mission-critical software system. This software system consists of an operating system, device drivers, controllers, and executives; recently highly complex AI-based autonomy software have also been introduced. Prior to launch, this software has to undergo rigorous verification and validation (V&V). Nevertheless, dormant software bugs, failing sensors, unexpected hardware-software interactions, and unanticipated environmental conditions—likely on a space exploration mission—can cause major software faults that can endanger the entire mission.

Our Integrated Software Health Management (ISWHM) system continuously monitors the hardware sensors and the software …

Adaptive Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

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 Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

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 Algorithms For Coverage Control And Space Partitioning In Mobile Robotic Networks, Jerome Le Ny, George J. Pappas

George J. Pappas

We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic …

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, …