Vishal Chand

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About

Vishal Chand is a robotics researcher specializing in robot path planning, autonomous navigation, and the integration of machine learning into robotic systems. His work addresses the fundamental challenge of enabling robots to move efficiently, safely, and smoothly from one point to another in complex environments. Chand’s major contributions include comparative analyses of classical and heuristic-based path planning approaches, as well as the development of hybrid algorithms. Notably, his 2020 paper, “A Face-off - Classical and Heuristic-based Path Planning Approaches,” with 12 citations, provides a critical benchmark for evaluating different planning strategies. He further advanced the field with his 2022 work, “ACO-Kinematic: a hybrid first off the starting block” (7 citations), which introduces a novel hybrid method combining ant colony optimization with kinematic constraints for superior route planning. Demonstrating the application of machine learning, his 2021 paper, “Obstacle Avoidance of a Point-Mass Robot using Feedforward Neural Network” (5 citations), explores how neural networks can enhance real-time obstacle avoidance. Chand’s research is particularly relevant for robotics applications in manufacturing, transportation, healthcare, and hazardous environments. His work bridges classical robotics with modern AI techniques, offering practical solutions for safer and more efficient autonomous navigation.