What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
Steven M. LaValle mostly deals with Motion planning, Robot, Mathematical optimization, Artificial intelligence and Probabilistic roadmap.
His work carried out in the field of Motion planning brings together such families of science as Theoretical computer science, Mobile robot, Workspace, Algorithm and Trajectory.
Steven M. LaValle works mostly in the field of Robot, limiting it down to concerns involving Distributed computing and, occasionally, Algorithm design, Greedy algorithm and Randomized algorithm.
Steven M. LaValle has researched Mathematical optimization in several fields, including Any-angle path planning, State space and Graph.
His Artificial intelligence study integrates concerns from other disciplines, such as Compensation and Computer vision.
His research integrates issues of Kinodynamic planning and Rapidly exploring random tree in his study of Probabilistic roadmap.
His most cited work include:
- Planning Algorithms (4205 citations)
- Rapidly-exploring random trees : a new tool for path planning (2128 citations)
- Randomized kinodynamic planning (2077 citations)
What are the main themes of his work throughout his whole career to date?
Steven M. LaValle focuses on Motion planning, Robot, Artificial intelligence, Mathematical optimization and Computer vision.
Steven M. LaValle is studying Probabilistic roadmap, which is a component of Motion planning.
His Probabilistic roadmap research includes themes of Kinodynamic planning and Rapidly exploring random tree.
His Robot research integrates issues from Control engineering, Motion, Path and Control theory.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Any-angle path planning, Shortest path problem, Graph and Nonholonomic system.
His studies deal with areas such as Computational geometry and Pursuit-evasion as well as Computer vision.
He most often published in these fields:
- Motion planning (44.13%)
- Robot (41.31%)
- Artificial intelligence (35.21%)
What were the highlights of his more recent work (between 2014-2021)?
- Robot (41.31%)
- Virtual reality (7.98%)
- Human–computer interaction (7.51%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Robot, Virtual reality, Human–computer interaction, Task and Motion planning.
His Robot research is multidisciplinary, relying on both Immersion, Boundary and Control theory, Robustness.
His Boundary study which covers Mobile robot that intersects with Motion control and Topology.
Steven M. LaValle combines subjects such as Black box and Computer graphics with his study of Virtual reality.
His Motion planning research focuses on subjects like Heuristics, which are linked to Integer programming.
His research links Type with Mathematical optimization.
Between 2014 and 2021, his most popular works were:
- Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics (119 citations)
- Calibration of virtual reality systems (37 citations)
- Can Simulated Nature Support Mental Health? Comparing Short, Single-Doses of 360-Degree Nature Videos in Virtual Reality With the Outdoors. (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
His primary areas of investigation include Virtual reality, Motion planning, Job shop scheduling, Heuristics and Nondeterministic algorithm.
In his research, Computer vision, Artificial intelligence, Headset and Inertial measurement unit is intimately related to Computer graphics, which falls under the overarching field of Virtual reality.
His Motion planning research is multidisciplinary, incorporating elements of Perspective, Human–computer interaction and Plan.
Job shop scheduling combines with fields such as Computation, Mathematical optimization, Integer programming, Algorithm and Exact algorithm in his investigation.
His studies in Computation integrate themes in fields like Computational complexity theory and Multi-objective optimization.
Mathematical optimization connects with themes related to Type in his study.
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