What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical optimization
- Computer network
His main research concerns Distributed computing, Motion control, Mobile robot, Robot and Telecommunications network.
The various areas that Michael M. Zavlanos examines in his Distributed computing study include Robotics, Hybrid system, Artificial intelligence, Robot kinematics and Weapon target assignment problem.
His Motion control research integrates issues from Graph theory, Connectivity, Control theory and Topology.
His Graph theory study integrates concerns from other disciplines, such as Network topology and Theoretical computer science.
The concepts of his Mobile robot study are interwoven with issues in Real-time computing, Graph and Network packet.
His biological study spans a wide range of topics, including Distributed algorithm, Routing and Multi-agent system.
His most cited work include:
- Graph-theoretic connectivity control of mobile robot networks (308 citations)
- Distributed Connectivity Control of Mobile Networks (290 citations)
- Potential Fields for Maintaining Connectivity of Mobile Networks (270 citations)
What are the main themes of his work throughout his whole career to date?
Michael M. Zavlanos mostly deals with Mathematical optimization, Robot, Distributed computing, Mobile robot and Linear temporal logic.
His research investigates the connection between Mathematical optimization and topics such as Reinforcement learning that intersect with issues in Bellman equation.
His Robot research also works with subjects such as
- Real-time computing together with Network packet and Workspace,
- Control theory together with Motion control and Rotation matrix.
The Motion control study combines topics in areas such as Graph theory and Multi-agent system.
His Distributed computing study combines topics from a wide range of disciplines, such as Telecommunications network, Wireless sensor network, State and Hybrid system.
His work focuses on many connections between Mobile robot and other disciplines, such as Network topology, that overlap with his field of interest in Adjacency list, Algebraic graph theory, Connectivity and Topology.
He most often published in these fields:
- Mathematical optimization (33.53%)
- Robot (28.82%)
- Distributed computing (23.53%)
What were the highlights of his more recent work (between 2019-2021)?
- Reinforcement learning (5.88%)
- Mathematical optimization (33.53%)
- Robot (28.82%)
In recent papers he was focusing on the following fields of study:
Michael M. Zavlanos mainly focuses on Reinforcement learning, Mathematical optimization, Robot, Linear temporal logic and Artificial intelligence.
Michael M. Zavlanos interconnects Control and State in the investigation of issues within Reinforcement learning.
Many of his research projects under Mathematical optimization are closely connected to Convex function with Convex function, tying the diverse disciplines of science together.
In general Robot study, his work on Mobile robot and Robotics often relates to the realm of Space, thereby connecting several areas of interest.
His Linear temporal logic study combines topics in areas such as Motion planning and Temporal logic.
The concepts of his Motion planning study are interwoven with issues in Upper and lower bounds, Automaton and Control theory.
Between 2019 and 2021, his most popular works were:
- Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning (20 citations)
- STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems: (15 citations)
- Distributed Constrained Online Learning (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Algorithm
Michael M. Zavlanos focuses on Linear temporal logic, Mathematical optimization, Function, Lipschitz continuity and Residual.
His work deals with themes such as Asymptotically optimal algorithm, Control, Motion planning and Reinforcement learning, which intersect with Linear temporal logic.
His biological study spans a wide range of topics, including Upper and lower bounds and Control theory.
Many of his studies involve connections with topics such as Bounded function and Mathematical optimization.
His Function research is multidisciplinary, relying on both Artificial neural network, Model order reduction and Interpolation.
Michael M. Zavlanos has researched Lipschitz continuity in several fields, including Control theory, Online optimization, Boosting, Rate of convergence and Point.
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