What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Gaurav S. Sukhatme spends much of his time researching Mobile robot, Robot, Artificial intelligence, Computer vision and Wireless sensor network.
His Mobile robot research incorporates elements of Kalman filter, Simulation, Global Positioning System and Motion planning.
His biological study spans a wide range of topics, including Object detection and Real-time computing.
Gaurav S. Sukhatme focuses mostly in the field of Real-time computing, narrowing it down to topics relating to Sampling and, in certain cases, Wireless.
His Computer vision study combines topics in areas such as Control theory, Visual servoing and Obstacle avoidance.
His Wireless sensor network research incorporates themes from Software deployment, Distributed computing, Key distribution in wireless sensor networks, Mobile wireless sensor network and Energy.
His most cited work include:
- Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem (1207 citations)
- Connecting the physical world with pervasive networks (782 citations)
- An Incremental Self-Deployment Algorithm for Mobile Sensor Networks (568 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Robot, Artificial intelligence, Mobile robot, Computer vision and Real-time computing.
His work carried out in the field of Robot brings together such families of science as Task, Distributed computing and Simulation.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Global Positioning System.
His Mobile robot research is multidisciplinary, relying on both Control engineering and Control theory.
His research investigates the connection between Real-time computing and topics such as Wireless sensor network that intersect with issues in Software deployment, Node, Key distribution in wireless sensor networks and Embedded system.
The Motion planning study combines topics in areas such as Sampling and Underwater.
He most often published in these fields:
- Robot (38.13%)
- Artificial intelligence (35.69%)
- Mobile robot (22.80%)
What were the highlights of his more recent work (between 2016-2021)?
- Robot (38.13%)
- Artificial intelligence (35.69%)
- Reinforcement learning (4.88%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Robot, Artificial intelligence, Reinforcement learning, Motion planning and Mathematical optimization.
His Robot study integrates concerns from other disciplines, such as Motion, Graph, Distributed computing, Task and Human–computer interaction.
The concepts of his Distributed computing study are interwoven with issues in Constraint satisfaction and Modulo.
His studies deal with areas such as Sampling and Machine learning as well as Artificial intelligence.
His studies in Reinforcement learning integrate themes in fields like Object, Domain, Control theory and Sample.
His Motion planning course of study focuses on Underwater and Seabed, Marine engineering and Vehicle dynamics.
Between 2016 and 2021, his most popular works were:
- Crazyswarm: A large nano-quadcopter swarm (123 citations)
- Interactive Perception: Leveraging Action in Perception and Perception in Action (115 citations)
- Trajectory Planning for Quadrotor Swarms (90 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Gaurav S. Sukhatme mostly deals with Robot, Artificial intelligence, Reinforcement learning, Machine learning and Trajectory.
Gaurav S. Sukhatme has included themes like Task, Graph, Distributed computing and Human–computer interaction in his Robot study.
He has researched Artificial intelligence in several fields, including Computer vision and Trajectory optimization.
As part of the same scientific family, Gaurav S. Sukhatme usually focuses on Computer vision, concentrating on Construct and intersecting with State.
His Machine learning research incorporates elements of Sampling, Environmental monitoring, Bottleneck and Component.
His Trajectory study integrates concerns from other disciplines, such as Smoothing, Mobile robot, Interpolation, Path and Process.
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