What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Control theory
His primary areas of study are Robot, Control theory, Robot control, Control engineering and Mathematical optimization.
His work carried out in the field of Robot brings together such families of science as Distributed computing, Control theory, Voronoi diagram and Control, Decentralised system.
His Control theory research includes elements of Tracking, Robot kinematics, Task and Exponential convergence.
The subject of his Robot control research is within the realm of Artificial intelligence.
In his study, which falls under the umbrella issue of Control engineering, Convergence is strongly linked to Mobile robot.
His Mathematical optimization research incorporates elements of Q-learning, Motion planning, Algorithm and Signal temporal logic.
His most cited work include:
- Decentralized, Adaptive Coverage Control for Networked Robots (312 citations)
- Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments (197 citations)
- Persistent ocean monitoring with underwater gliders: Adapting sampling resolution (162 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Robot, Mathematical optimization, Control theory, Artificial intelligence and Control theory.
Mac Schwager has researched Robot in several fields, including Control engineering and Trajectory.
His studies in Mathematical optimization integrate themes in fields like Motion planning, Robustness and Convex optimization.
His Control theory study integrates concerns from other disciplines, such as Bounded function, Reference frame and Model predictive control.
His Adaptive control study in the realm of Control theory connects with subjects such as Scalability.
He has included themes like Distributed computing and Decentralised system in his Mobile robot study.
He most often published in these fields:
- Robot (57.22%)
- Mathematical optimization (24.06%)
- Control theory (20.86%)
What were the highlights of his more recent work (between 2019-2021)?
- Robot (57.22%)
- Mathematical optimization (24.06%)
- Trajectory (13.90%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Robot, Mathematical optimization, Trajectory, Artificial intelligence and Control theory.
The study incorporates disciplines such as Object, Probabilistic logic, Control theory and Model predictive control in addition to Robot.
His Trajectory optimization study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Stochastic process, bridging the gap between disciplines.
His Trajectory study combines topics from a wide range of disciplines, such as Intrinsics and Video camera.
Mac Schwager works mostly in the field of Artificial intelligence, limiting it down to concerns involving Computer vision and, occasionally, Kalman filter and Extended Kalman filter.
His research in Control theory intersects with topics in Robot kinematics and Inverse.
Between 2019 and 2021, his most popular works were:
- AirSim Drone Racing Lab (8 citations)
- Scalable Cooperative Transport of Cable-Suspended Loads With UAVs Using Distributed Trajectory Optimization (7 citations)
- Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Control theory
Mac Schwager mostly deals with Mathematical optimization, Robot, Artificial intelligence, Nash equilibrium and Drone.
His Mathematical optimization study combines topics in areas such as Model predictive control, Robustness and Nonlinear system.
His studies deal with areas such as Stochastic control, Control theory and Trajectory optimization as well as Model predictive control.
Robot and Filter are two areas of study in which Mac Schwager engages in interdisciplinary research.
His biological study spans a wide range of topics, including Control and Collision avoidance.
His research integrates issues of Quaternion, Computation and Distributed computing in his study of Trajectory.
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