Hello, I'm David van Wijk

I am an Aerospace Ph.D. Candidate in the Land, Air and Space Robotics Laboratory at Texas A&M University passionate about developing provably safe control algorithms for autonomous systems. My research focuses on robotic and spacecraft applications, where I leverage insights from control theory, optimization, and estimation to enhance the reliability of autonomous operations in complex environments.


Texas A&M University
Ph.D. Aerospace Engineering, (2021 - Expected 2025)

Cornell University
B.S. Mechanical and Aerospace Engineering, (2017 - 2021)


Journal Publications

Disturbance-Robust Backup Control Barrier Functions: Safety Under Uncertain Dynamics

Disturbance-Robust Backup Control Barrier Functions: Safety Under Uncertain Dynamics

IEEE Control Systems Letters (L-CSS) with ACC option, 2024

Novel class of CBFs for online controlled invariance of systems with input constraints and unmodelled dynamics disturbances.

Autonomous Satellite Servicing Infrastructure for In-Space Assembly and Manufacturing

Autonomous Satellite Servicing Infrastructure for In-Space Assembly and Manufacturing

Ian Down, David E.J. van Wijk, Deep Parikh, Manoranjan Majji
ASME Journal of Manufacturing Science and Engineering, 2024

Closed-loop GNC framework for spacecraft servicing module deployment, optimizing low-thrust trajectories while addressing state constraints and impact uncertainty.

Safe Spacecraft Inspection via Deep Reinforcement Learning and Discrete Control Barrier Functions

Safe Spacecraft Inspection via Deep Reinforcement Learning and Discrete Control Barrier Functions

AIAA Journal of Aerospace Information Systems (JAIS), 2024

RL-based on-orbit spacecraft inspection with safety ensured via control barrier functions and RTA, analyzing task performance under various state constraints.

Directional Sensor Planning for Occlusion Avoidance

Directional Sensor Planning for Occlusion Avoidance

IEEE Transactions on Robotics (T-RO), 2022

Novel motion planning framework for directional sensors ensuring target visibility, obstacle avoidance, and efficiency in cluttered environments.

Conference Publications

Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection

Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection

AIAA ASCEND, 2024

RTA-enhanced RL training for 6-DOF spacecraft inspection, enforcing safety via control barrier functions and analyzing trade-offs in performance and safety.

On-Manifold Collision Avoidance using Tori Parametrization and Control Barrier Functions

On-Manifold Collision Avoidance using Tori Parametrization and Control Barrier Functions

David E.J. van Wijk, Ian Down, Manoranjan Majji
Rocky Mountain AAS GN&C Conference, 2024 Oral Presentation

Collision mitigation for low-thrust spacecraft in a quasi-periodic orbit using high order control barrier functions.

Fault Tolerant Run Time Assurance with Control Barrier Functions for Rigid Body Spacecraft Rotation

Fault Tolerant Run Time Assurance with Control Barrier Functions for Rigid Body Spacecraft Rotation

David E.J. van Wijk, Manoranjan Majji, Kerianne Hobbs
AIAA Scitech Forum, 2024 Oral Presentation

Safety-critical spacecraft control in the presence of faults and rapidly variable dynamics using sequential estimation and control barrier functions.

Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination

Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination

AAS/AIAA Astrodynamics Specialist Conference, 2023 Oral Presentation

RL-based control for on-orbit spacecraft inspection, optimizing illumination and evaluating performance with statistically robust metrics.

Run Time Assurance for Autonomous Spacecraft Inspection

Run Time Assurance for Autonomous Spacecraft Inspection

Kyle Dunlap, David E.J. van Wijk, Kerianne Hobbs
AAS/AIAA Astrodynamics Specialist Conference, 2023

Development of spacecraft constraints for multi-agent inspection mission, enforced using control barrier functions.

Deep Reinforcement Learning Controller for Autonomous Tracking of Evasive Ground Target

Deep Reinforcement Learning Controller for Autonomous Tracking of Evasive Ground Target

David E.J. van Wijk, Kameron Eves, John Valasek
AIAA Scitech Forum, 2023 Oral Presentation

RL-based control for multirotor tracking of maneuvering ground targets using a fixed optical sensor, addressing occlusions and target motion types in simulation.

Other Publications

Stochastic Control Barrier Functions for Economics

Stochastic Control Barrier Functions for Economics

David E.J. van Wijk
arXiv Preprint, 2023

Stochastic control barrier functions and stochastic optimal control applied to classical economics problems.