Real-time trajectory planning for collaborative robots using incremental multi-objective optimization

Human–robot collaboration plays a crucial role in industries such as manufacturing, healthcare, and service. It enhances operational efficiency, ensures safety, and optimizes the interaction experience between humans and robots. In close-range human–robot collaboration, trajectory planning for collaborative robots needs to consider the safety, efficiency, and comfort of the trajectory, and be able to respond quickly to changes in the environment. This paper proposes an incremental multi-objective trajectory optimization algorithm. The algorithm integrates real-time human motion data into the optimization problem, ensuring real-time responsiveness to environmental changes during collaboration. Experimental results show that, compared to existing algorithms, our algorithm demonstrates superior overall performance in terms of safety, efficiency, and comfort.