What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Computer vision
Kostas Alexis mainly investigates Control engineering, Model predictive control, Control theory, Motion planning and Simulation.
His Model predictive control research incorporates themes from Control theory, Obstacle avoidance and Trajectory.
The concepts of his Control theory study are interwoven with issues in Control and Flight envelope.
The study incorporates disciplines such as Computational complexity theory and Resampling in addition to Motion planning.
His research integrates issues of Planner and Real-time computing in his study of Robot.
His Artificial intelligence research is multidisciplinary, incorporating elements of Tree and Computer vision.
His most cited work include:
- Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances (241 citations)
- Model predictive quadrotor control: attitude, altitude and position experimental studies (219 citations)
- Receding horizon “next-best-view” planner for 3D exploration (183 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Robot, Control engineering, Artificial intelligence, Motion planning and Control theory.
Kostas Alexis focuses mostly in the field of Robot, narrowing it down to matters related to Real-time computing and, in some cases, Vehicle dynamics.
The various areas that Kostas Alexis examines in his Control engineering study include Control, Model predictive control, Thrust vectoring, Nonlinear system and Trajectory.
As a member of one scientific family, Kostas Alexis mostly works in the field of Artificial intelligence, focusing on Computer vision and, on occasion, Extended Kalman filter.
His Motion planning study incorporates themes from Tree, Planner, Travelling salesman problem and Distributed computing.
His studies deal with areas such as Flight envelope and Mobile robot as well as Control theory.
He most often published in these fields:
- Robot (47.66%)
- Control engineering (34.38%)
- Artificial intelligence (32.81%)
What were the highlights of his more recent work (between 2019-2021)?
- Robot (47.66%)
- Artificial intelligence (32.81%)
- Real-time computing (21.09%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Robot, Artificial intelligence, Real-time computing, Motion planning and Systems design.
Kostas Alexis has researched Robot in several fields, including Lift, Pose and Control theory.
His Pose research is multidisciplinary, relying on both Tree traversal, Simulation and Mobile robot.
His study focuses on the intersection of Artificial intelligence and fields such as Computer vision with connections in the field of Odometry.
His study ties his expertise on Tracking together with the subject of Real-time computing.
His Motion planning research incorporates elements of Agile software development, Representation and Vehicle dynamics.
Between 2019 and 2021, his most popular works were:
- Motion Primitives-based Path Planning for Fast and Agile Exploration using Aerial Robots (12 citations)
- Keyframe‐based thermal–inertial odometry (9 citations)
- Graph‐based subterranean exploration path planning using aerial and legged robots (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Control theory
His scientific interests lie mostly in Artificial intelligence, Robot, Inertial frame of reference, Motion planning and Odometry.
His research links Real-time computing with Robot.
His study brings together the fields of Agile software development and Real-time computing.
The Motion planning study combines topics in areas such as Educational robotics, Representation and Vehicle dynamics.
Kostas Alexis combines subjects such as Pose and Computer vision with his study of Odometry.
When carried out as part of a general Computer vision research project, his work on Sensor fusion is frequently linked to work in Lidar, therefore connecting diverse disciplines of study.
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