What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Motion planning, Simulation and Robot.
His work in the fields of Artificial intelligence, such as Rapidly exploring random tree, Needle steering, Virtual reality and Multi-agent system, overlaps with other areas such as Interface.
His Motion planning study integrates concerns from other disciplines, such as Discretization, Control theory and Humanoid robot.
His Simulation study incorporates themes from Grippers and Finite-state machine, Algorithm.
His studies in Robot integrate themes in fields like Small target and Trajectory optimization.
His Trajectory optimization research focuses on subjects like Quadratic equation, which are linked to Mathematical optimization.
His most cited work include:
- A Survey of Research on Cloud Robotics and Automation (489 citations)
- Motion planning with sequential convex optimization and convex collision checking (354 citations)
- Motion planning under uncertainty using iterative local optimization in belief space (213 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Robot, Computer vision, Mathematical optimization and Motion planning are his primary areas of study.
His work on Model predictive control and Robotic arm as part of general Artificial intelligence study is frequently connected to Noise and System identification, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Sachin Patil has researched Robot in several fields, including Grippers, Control engineering and Trajectory, Trajectory optimization.
Sachin Patil combines subjects such as Simulation, Virtual reality and Probabilistic method with his study of Computer vision.
Many of his research projects under Mathematical optimization are closely connected to Set with Set, tying the diverse disciplines of science together.
His Motion planning study combines topics in areas such as Holonomic, Configuration space, Optimal control and Mobile robot.
He most often published in these fields:
- Artificial intelligence (36.84%)
- Robot (31.58%)
- Computer vision (29.82%)
What were the highlights of his more recent work (between 2014-2017)?
- Robot (31.58%)
- Artificial intelligence (36.84%)
- Computer vision (29.82%)
In recent papers he was focusing on the following fields of study:
Sachin Patil mostly deals with Robot, Artificial intelligence, Computer vision, Mathematical optimization and GRASP.
The study incorporates disciplines such as Grippers and Trajectory, Trajectory optimization in addition to Robot.
In general Artificial intelligence study, his work on Segmentation, Cluster analysis and Mixture model often relates to the realm of Noise and Hierarchical Dirichlet process, thereby connecting several areas of interest.
His Computer vision research is multidisciplinary, incorporating elements of Robotic arm and Probabilistic method.
His Mathematical optimization research incorporates themes from State space and Motion planning.
His Motion planning research includes themes of Control theory and Holonomic.
Between 2014 and 2017, his most popular works were:
- A Survey of Research on Cloud Robotics and Automation (489 citations)
- Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping (108 citations)
- Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms (93 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
Sachin Patil mainly focuses on Robot, Trajectory optimization, Trajectory, Grippers and Computer vision.
His Robot research is multidisciplinary, relying on both Control theory and Holonomic.
His Trajectory optimization study contributes to a more complete understanding of Mathematical optimization.
His Mathematical optimization research integrates issues from State space, Curse of dimensionality and Mobile robot.
His work deals with themes such as Object, Finite-state machine and Small target, which intersect with Grippers.
His Computer vision study frequently draws connections between adjacent fields such as Artificial intelligence.
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