D-Index & Metrics Best Publications
Jonathan P. How

Jonathan P. How

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Electronics and Electrical Engineering D-index 74 Citations 19,213 332 World Ranking 206 National Ranking 115
Mechanical and Aerospace Engineering D-index 71 Citations 18,162 292 World Ranking 69 National Ranking 39

Research.com Recognitions

Awards & Achievements

2018 - IEEE Fellow For contributions to guidance and control of air and space vehicles

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Control theory

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.

His most cited work include:

  • Aircraft trajectory planning with collision avoidance using mixed integer linear programming (593 citations)
  • Real-Time Motion Planning With Applications to Autonomous Urban Driving (540 citations)
  • Consensus-Based Decentralized Auctions for Robust Task Allocation (507 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Control theory (24.13%)
  • Mathematical optimization (19.00%)
  • Artificial intelligence (18.23%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (18.23%)
  • Reinforcement learning (7.96%)
  • Robot (8.22%)

In recent papers he was focusing on the following fields of 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.

Between 2016 and 2021, his most popular works were:

  • Socially aware motion planning with deep reinforcement learning (220 citations)
  • Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning (139 citations)
  • Deep decentralized multi-task multi-agent reinforcement learning under partial observability (107 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Aircraft trajectory planning with collision avoidance using mixed integer linear programming

A. Richards;J.P. How.
american control conference (2002)

789 Citations

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)

645 Citations

Consensus-Based Decentralized Auctions for Robust Task Allocation

Han-Lim Choi;L. Brunet;J.P. How.
IEEE Transactions on Robotics (2009)

633 Citations

Spacecraft Formation Flying: Dynamics, Control and Navigation

Kyle Terry Alfriend;Srinivas Rao Vadali;Pini Gurfil;Jonathan How.
(2009)

597 Citations

Mixed integer programming for multi-vehicle path planning

Tom Schouwenaars;Bart De Moor;Eric Feron;Jonathan How.
european control conference (2001)

580 Citations

Relative Dynamics and Control of Spacecraft Formations in Eccentric Orbits

Gokhan Inalhan;Michael Tillerson;Jonathan P. How.
Journal of Guidance Control and Dynamics (2000)

529 Citations

A perception-driven autonomous urban vehicle

John Leonard;Jonathan How;Seth Teller;Mitch Berger.
Journal of Field Robotics (2008)

519 Citations

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)

490 Citations

Control with random communication delays via a discrete-time jump system approach

Lin Xiao;A. Hassibi;J.P. How.
american control conference (2000)

488 Citations

Real-time indoor autonomous vehicle test environment

J.P. How;B. Bethke;A. Frank;D. Dale.
IEEE Control Systems Magazine (2008)

448 Citations

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