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Nicholas R. Gans

6 accepted papers

2020

Extremum Seeking Control for Stiffness Auto-Tuning of a Quasi-Passive Ankle Exoskeleton

RA-L 2020

Recently, it has been shown that light-weight, passive, ankle exoskeletons with spring-based energy store-and-release mechanisms can reduce the muscular effort of human walking. The stiffness of the spring in such a device must be properly tuned in order to minimize the muscular effort. However, thi

Cited by 36SourceScholar
2020

Surface Parameterization and Trajectory Generation on Regular Surfaces With Application in Robot-Guided Deposition Printing

RA-L 2020

In this work, we present a novel approach to design and carry out trajectories over regular curved surfaces. This has application in a number of robot path planning problems, including our primary interest in deposition printing. Existing solutions are often ad-hoc in terms of path generation and co

Cited by 12SourceScholar
2019

Robust 3D Distributed Formation Control With Collision Avoidance And Application To Multirotor Aerial Vehicles

ICRA 2019poster

We present a distributed control strategy for a team of agents to autonomously achieve a desired 3D formation. Our approach is based on local relative position measurements and can be applied to multirotor aerial vehicles. We assume that agents have a common sense of direction, which is used to alig…

Cited by 22SourceScholar
2018

QuEst: A Quaternion-Based Approach for Camera Motion Estimation From Minimal Feature Points

RA-L 2018

In this letter, we consider the problem of recovering the rotation and translation changes of a moving camera from captured images. This problem is traditionally solved using the epipolar constraint, where the rotation and translation changes are recovered from the essential matrix. We propose a new

Cited by 37SourceScholar
2016

Optimal Placement for a Limited-Support Binary Sensor

RA-L 2016

We present an optimal strategy for placement of a binary sensor based on maximizing the mutual information between the distribution of possible target locations, the sensor footprint, and probability of sensor errors. The result replaces the direct computation of information gradients by a sensor co

Cited by 4SourceScholar