NDLI: Inducing Human-like Motion in Robots

Content Provider	ACM Digital Library
Author	Pattanaik, Vishwajeet Suran, Shweta Prabakaran, S.
Abstract	The paper's objective is to reduce dynamic rigidity and real time computation in robotic systems. Dynamic rigidity has been a key issue in the field of robotic motion. Most methodologies in this field focus on optimal navigation strategies. These strategies are complex and require enormous amount of computation. The proposed system handles these concerns using the following process: first, divide motion into 'n' time steps; then, acquire approximate to precise knowledge of the environment with respect to each time step (t1 to tn) and generate kinematic response to stimuli for respective (tn)s using the concept of muscle memory. Approaches such as Moving Average Smoothing and Motor Learning have been brought to use for the same. Since mobile robots are based on real-time application, response time has also been taken into consideration. Simulations support the proposed theory and show drastic reduction in computation time, response time and dynamic rigidity.
Starting Page	1
Ending Page	3
Page Count	3
File Format	PDF
ISBN	9781450330374
DOI	10.1145/2662117.2662118
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-10-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Motion planning Kinematics Artificial intelligence Motor learning Moving average smoothing
Content Type	Text
Resource Type	Article

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Inducing Human-like Motion in Robots