Serial Manipulator

Serial manipulators, robotic arms with a single kinematic chain, are being actively researched to improve their performance and expand their applications. Current research focuses on enhancing control algorithms, particularly through machine learning techniques like reinforcement learning and neural networks, to address challenges such as inverse kinematics, collision avoidance, and trajectory optimization in complex environments. These advancements are driving improvements in teleoperation, collaborative robotics, and applications requiring precise and adaptable manipulation, impacting fields ranging from industrial automation to assistive technologies. The development of open-source platforms and standardized benchmarks further facilitates progress and collaboration within the field.