E
Emmanuel G. Collins
Researcher at Florida State University
Publications - 239
Citations - 3644
Emmanuel G. Collins is an academic researcher from Florida State University. The author has contributed to research in topics: Control theory & Linear-quadratic-Gaussian control. The author has an hindex of 26, co-authored 239 publications receiving 3371 citations. Previous affiliations of Emmanuel G. Collins include University College of Engineering & Harris Corporation.
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Journal ArticleDOI
Robust decentralized control laws for the ACES structure
TL;DR: In this article, a control system design for the Active Control Technique Evaluation for Spacecraft (ACES) structure at NASA Marshall Space Flight Center at NASA's Space Station is discussed, where the primary objective is to design controllers that provide substantial reduction of the line-of-sight pointing errors.
Book ChapterDOI
Motion Planning for Mobile Robots Via Sampling-Based Model Predictive Optimization
TL;DR: Sampling-based methods represent a type of model based motion planning algorithm that incorporate the system model and has been used in many applications including manipulator path planning, Kuffner & LaValle, and the Synergistic Combination of Layers of Planning multi-layered planning framework.
Journal ArticleDOI
Dynamically feasible, energy efficient motion planning for skid-steered vehicles
TL;DR: The results show that a small increase in the distance of a trajectory over the distance optimal trajectory can result in a dramatic savings in the AGV’s energy consumption and that distance optimal planning can often produce trajectories that violate the motor torque constraints for skid-steered AGVs, which can results in poor navigation performance.
Proceedings ArticleDOI
Nonlinear Model Predictive Control using sampling and goal-directed optimization
TL;DR: A novel method called Sampling-Based Model Predictive Control (SBMPC) is proposed as an efficient MPC algorithm to generate control inputs and system trajectories that avoids the local minima which can limit the performance of MPC algorithms implemented using traditional, derivative-based, nonlinear programming.
Journal ArticleDOI
An Efficient Stochastic Clustering Auction for Heterogeneous Robotic Collaborative Teams
TL;DR: The contribution of this work is to present an efficient SCA for heterogeneous teams, based on a modified Swendsen-Wang method, that maintains the efficiency of the second SCA and can yield similar performance to the baseline CSA in far fewer iterations.