Sabin Hitimana
Papers
1
Total Citations
7
H-Index
1
About
Sabin Hitimana is a robotics researcher advancing intelligent motion planning through artificial intelligence. His work centers on self-collision avoidance for articulated arm robots—a critical challenge in ensuring safe, autonomous operation in cluttered environments. In his most-cited paper, “Self-Collision Avoidance of Arm Robot Using Generative Adversarial Network and Particles Swarm Optimization (GAN-PSO)” (2021, 7 citations), Hitimana pioneers a hybrid approach that combines Generative Adversarial Networks (GANs) with Particle Swarm Optimization (PSO) to train robots to avoid collisions with objects, their surroundings, and their own bodies. By integrating GAN-generated training data with PSO’s optimization power, his method achieves robust Inverse Kinematics (IK) solutions across 96,000 motion configurations. This work demonstrates how generative models can enhance traditional optimization for real-time robotic control. Hitimana’s contributions are particularly valuable for industrial automation and collaborative robotics, where preventing self-damage is paramount. As a researcher at the intersection of deep learning and swarm intelligence, he continues to push the boundaries of safe, adaptive robot behavior.
Research Focus
Key Achievements
Top Papers
- 1