2018 - IEEE Fellow For contributions to guidance and control of air and space vehicles
His primary areas of investigation include Control theory, Motion planning, Mathematical optimization, Linear programming and Trajectory. His research investigates the link between Control theory and topics such as Model predictive control that cross with problems in Computation and Optimal control. The various areas that he examines in his Motion planning study include Task, Collision avoidance and Mobile robot.
His research in Mathematical optimization focuses on subjects like Probabilistic logic, which are connected to Sampling and Data mining. His Linear programming study integrates concerns from other disciplines, such as Control system, Remotely operated underwater vehicle, Spacecraft and Integer programming. His Trajectory research includes elements of Path and Simulation.
Jonathan P. How mainly focuses on Control theory, Mathematical optimization, Artificial intelligence, Control engineering and Motion planning. His study in Control theory, Robust control, Nonlinear system, Robustness and Adaptive control is carried out as part of his studies in Control theory. As a part of the same scientific study, Jonathan P. How usually deals with the Mathematical optimization, concentrating on Task and frequently concerns with Bundle.
His Artificial intelligence research incorporates elements of Machine learning and Computer vision. His Control engineering research is multidisciplinary, incorporating perspectives in Control and Actuator. Linear programming and Integer programming are frequently intertwined in his study.
Jonathan P. How spends much of his time researching Artificial intelligence, Reinforcement learning, Robot, Trajectory and Mathematical optimization. The Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His research in Reinforcement learning intersects with topics in Task, Collision avoidance, Human–computer interaction and Motion planning.
His work carried out in the field of Robot brings together such families of science as Path and Distributed computing. The subject of his Trajectory research is within the realm of Control theory. His research integrates issues of Aerodynamics and Model predictive control in his study of Control theory.
His scientific interests lie mostly in Artificial intelligence, Reinforcement learning, Robot, Trajectory and Collision avoidance. His Artificial intelligence study combines topics in areas such as Machine learning, Pedestrian and Computer vision. His biological study spans a wide range of topics, including Artificial neural network, Reinforcement, Motion planning and Human–computer interaction.
He has researched Trajectory in several fields, including Distributed computing, Intersection, Path, Vehicle dynamics and Solver. His study with Robustness involves better knowledge in Control theory. Jonathan P. How works in the field of Control theory, namely Nonlinear system.
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Aircraft trajectory planning with collision avoidance using mixed integer linear programming
A. Richards;J.P. How.
american control conference (2002)
Real-Time Motion Planning With Applications to Autonomous Urban Driving
Y. Kuwata;S. Karaman;J. Teo;E. Frazzoli.
IEEE Transactions on Control Systems and Technology (2009)
Consensus-Based Decentralized Auctions for Robust Task Allocation
Han-Lim Choi;L. Brunet;J.P. How.
IEEE Transactions on Robotics (2009)
Spacecraft Formation Flying: Dynamics, Control and Navigation
Kyle Terry Alfriend;Srinivas Rao Vadali;Pini Gurfil;Jonathan How.
(2009)
Mixed integer programming for multi-vehicle path planning
Tom Schouwenaars;Bart De Moor;Eric Feron;Jonathan How.
european control conference (2001)
Relative Dynamics and Control of Spacecraft Formations in Eccentric Orbits
Gokhan Inalhan;Michael Tillerson;Jonathan P. How.
Journal of Guidance Control and Dynamics (2000)
A perception-driven autonomous urban vehicle
John Leonard;Jonathan How;Seth Teller;Mitch Berger.
Journal of Field Robotics (2008)
Spacecraft trajectory planning with avoidance constraints using mixed-integer linear programming
Arthur Richards;Tom Schouwenaars;Jonathan P. How;Eric Feron.
Journal of Guidance Control and Dynamics (2002)
Control with random communication delays via a discrete-time jump system approach
Lin Xiao;A. Hassibi;J.P. How.
american control conference (2000)
Real-time indoor autonomous vehicle test environment
J.P. How;B. Bethke;A. Frank;D. Dale.
IEEE Control Systems Magazine (2008)
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