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kharagpur, spain
It's currently 5:52 PM here
Joined July 15, 2012
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Vinay Y.
@vinayyadav3016
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kharagpur, spain
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Yadav
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Experience
Software developer
Apr 2011 - Present
We were supposed to build an AUV that could perform the tasks like buoy touching, marker dropping etc, after identifying the objects using computer vision, and also locating the object using underwater acoustic signal processing. This AUV was built to perform the tasks described in the 14th RoboSub problem statement. Details about the vehicle can be found at this link : ikat.iitkgp.ernet.in
Online POMDP Solver (SBT)
May 2013 - Jul 2013 (2 months, 1 day)
Developed and implemented a new online POMDP(Partially Observable Markov Decision Process) solver SBT(State-sequence based Belief Tree)
Summer Research Internship
May 2012 - Jul 2012 (2 months, 1 day)
During my stay at UJI, Castellon, I got exposed to various types of Robotic hands and arms. There, I also got exposed to different packages used by robotics community and I also created a package which can be used to control the robot by visual interaction in rviz package and I developed a graphical interface which will be used to plan robotic grasp based on the thesis of Dr. Mario Prats.
Education
Electrical Engineering
(4 years)
Publications
An Online POMDP Solver for Uncertainty Planning in Dynamic Environment
Springer
Motion planning under uncertainty is important for reliable robot operation
in uncertain and dynamic environment. Partially Observable Markov Decision
Processes (POMDPs) are general and systematic framework for motion planning
under uncertainty. To cope with dynamic environment well, we often need to modify
the POMDP model during runtime. However, despite recent tremendous advancement
in the speed of POMDP planning, most POMDP solvers are not fast enough to
generate a good solution when the POMDP mode
An Online POMDP Solver for Uncertainty Planning in Dynamic Environment
ISSR2013
Motion planning under uncertainty is important for reliable robot operation
in uncertain and dynamic environment. Partially Observable Markov Decision
Processes (POMDPs) are general and systematic framework for motion planning
under uncertainty. To cope with dynamic environment well, we often need to modify
the POMDP model during runtime. However, despite recent tremendous advancement
in the speed of POMDP planning, most POMDP solvers are not fast enough to
generate a good solution when the POMDP mode
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Log in to discuss any details over chat.
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