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Jonathan P. How

Jonathan P. How

Award Badge
Electronics and Electrical Engineering
USA
2026
Award Badge
Mechanical and Aerospace Engineering
USA
2026

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
100
Citations
39448
World Ranking
64
National Ranking
33

Electronics and Electrical Engineering

D-Index
102
Citations
40251
World Ranking
165
National Ranking
80

Research.com Recognitions

  • 2026 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 2026 - Research.com Mechanical and Aerospace Engineering in United States Leader Award
  • 2025 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 2018 - IEEE Fellow For contributions to guidance and control of air and space vehicles

Overview

Jonathan P. How is affiliated with MIT in the United States and specializes in research primarily within Computer Science and Engineering. Their work extensively covers several focused areas, including Artificial Intelligence, Computer Vision and Pattern Recognition, Aerospace Engineering, Control and Systems Engineering, and Electrical and Electronic Engineering. The scientific topics they cover emphasize Robotics and Sensor-Based Localization, Robotic Path Planning Algorithms, Reinforcement Learning in Robotics, Indoor and Outdoor Localization Technologies, Autonomous Vehicle Technology and Safety, Adversarial Robustness in Machine Learning, and Advanced Image and Video Retrieval Techniques.

Their recent publications include:

  • Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning, 2021, IEEE Access
  • Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems, 2022, IEEE Transactions on Robotics
  • Robust Adaptive Control Barrier Functions: An Adaptive and Data-Driven Approach to Safety, 2020, IEEE Control Systems Letters
  • Momelotinib versus danazol in symptomatic patients with anaemia and myelofibrosis (MOMENTUM): results from an international, double-blind, randomised, controlled, phase 3 study, 2023, The Lancet
  • FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments, 2021, IEEE Transactions on Robotics

The researcher frequently collaborates with a consistent group of coauthors, including Michael Everett, Rodolphe Sepulchre, Miroslav Krstić, Yasamin Mostofi, and Thomas Parisini. These collaborators have worked with them on numerous publications, reflecting a network of contributors in the field.

Jonathan P. How's work has been published extensively in well-known venues such as arXiv (Cornell University), IEEE Robotics and Automation Letters, IEEE Transactions on Robotics, IEEE Transactions on Control of Network Systems, and IEEE Transactions on Control Systems Technology. The high number of publications across these channels indicates sustained research activity within their domains of expertise.

In recognition of their contributions to guidance and control of air and space vehicles, they were named an IEEE Fellow in 2018.

Best Publications

  • Consensus-Based Decentralized Auctions for Robust Task Allocation

    Han-Lim Choi;L. Brunet;J.P. How

  • Real-Time Motion Planning With Applications to Autonomous Urban Driving

    Y. Kuwata;S. Karaman;J. Teo;E. Frazzoli

  • Aircraft trajectory planning with collision avoidance using mixed integer linear programming

    A. Richards;J.P. How

  • Spacecraft Formation Flying: Dynamics, Control and Navigation

    Kyle Terry Alfriend;Srinivas Rao Vadali;Pini Gurfil;Jonathan How

  • Mixed integer programming for multi-vehicle path planning

    Tom Schouwenaars;Bart De Moor;Eric Feron;Jonathan How

  • Socially aware motion planning with deep reinforcement learning

    Yu Fan Chen;Michael Everett;Miao Liu;Jonathan P. How

  • Spacecraft trajectory planning with avoidance constraints using mixed-integer linear programming

    Arthur Richards;Tom Schouwenaars;Jonathan P. How;Eric Feron

  • Relative Dynamics and Control of Spacecraft Formations in Eccentric Orbits

    Gokhan Inalhan;Michael Tillerson;Jonathan P. How

  • A New Nonlinear Guidance Logic for Trajectory Tracking

    Sanghyuk Park;John Deyst;Jonathan P. How

  • A perception-driven autonomous urban vehicle

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

    Yu Fan Chen;Miao Liu;Michael Everett;Jonathan P. How

  • Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method

    Sanghyuk Park;John Deyst;Jonathan P. How

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

    Lin Xiao;A. Hassibi;J.P. How

  • Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

    Michael Everett;Yu Fan Chen;Jonathan P. How

  • Real-time indoor autonomous vehicle test environment

    J.P. How;B. Bethke;A. Frank;D. Dale

  • Autonomous driving in urban environments: approaches, lessons and challenges

    Mark E. Campbell;Magnus Egerstedt;Jonathan P. How;Richard M Murray

  • COORDINATION AND CONTROL OF MULTIPLE UAVs

    Arthur Richards;John Bellingham;Michael Tillerson;Jonathan How

  • A path-following method for solving BMI problems in control

    A. Hassibi;J. How;S. Boyd

  • A Perception Driven Autonomous Urban Robot

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • Robust distributed model predictive control

    Arthur Richards;Jonathan P. How

Frequent Co-Authors

Girish Chowdhary
Girish Chowdhary University of Illinois at Urbana-Champaign
John Vian
John Vian Boeing (United States)
Kyle T. Alfriend
Kyle T. Alfriend Texas A&M University
Srinivas R. Vadali
Srinivas R. Vadali Texas A&M University
Pini Gurfil
Pini Gurfil Technion – Israel Institute of Technology
Tom Walsh
Tom Walsh University of Washington
Mark Campbell
Mark Campbell Cornell University
Ufuk Topcu
Ufuk Topcu The University of Texas at Austin

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