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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Rajendra, Karthik
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Computer Science & IT Engineering

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A Horner’s polynomial based quadrupedal multi-gaits signal generation controller Olivier Akansie, Kouame Yann; C. Biradar, Rajashekhar; Rajendra, Karthik; D. Devanagavi, Geetha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3545-3558

Abstract

Animal locomotion is the process through which animals move from one location to another. Self-propelled locomotion is based on the animal performing a series of actions to move towards a predetermined target. All of these motions occur sequentially and repeat themselves during a gait cycle. A gait cycle may be simulated by duplicating each motion in the cycle sequentially. To achieve this goal, a problem known as the gait planning issue was formulated, in which various systems were created to provide suitable signals for the execution of distinct gaits (patterns of steps of an animal at a specified speed). This research approaches the problem using Horner's polynomials for quadruped robots. The approach entails first creating a sequence table for each gait and fit two polynomial equations. In this study, an attempt is made to combine several gaits using Horner's polynomials. An algorithm uses elaborated polynomials to generate the desired gaits signals.
A terrain data collection sensor box towards a better analysis of terrains conditions Olivier Akansie, Kouame Yann; Biradar, Rajashekhar C.; Rajendra, Karthik; Devanagavi, Geetha D.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4388-4402

Abstract

Autonomous mobile robots are increasingly used across various applications, relying on multiple sensors for environmental awareness and efficient task execution. Given the unpredictability of human environments, versatility is crucial for these robots. Their performance is largely determined by how they perceive their surroundings. This paper introduces a machine learning (ML) approach focusing on land conditions to enhance a robot’s locomotion. The authors propose a method to classify terrains for data collection, involving the design of an apparatus to gather field data. This design is validated by correlating collected data with the output of a standard ML model for terrain classification. Experiments show that the data from this apparatus improves the accuracy of the ML classifier, highlighting the importance of including such data in the dataset.
Co-Authors Biradar, Rajashekhar C. C. Biradar, Rajashekhar D. Devanagavi, Geetha Devanagavi, Geetha D. Olivier Akansie, Kouame Yann