Socially acceptable sequential approaches to groups in dynamic environments

Abstract This paper introduces a novel strategy for mobile robot navigation, emphasizing sequential routing to maximize visits to people organized in groups within dynamic environments featuring both static obstacles and moving individuals. Our approach consists of three main phases: (i) processing approach points, (ii) path planning, and (iii) adaptive navigation with group-oriented approaching. In the first phase, a custom technique segments social zones and assigns rewards to potential approach regions, enabling the robot to prioritize the most advantageous locations for visiting each group. In the second phase, the classic elastic band method is utilized to generate a visibility graph, optimizing the sequence of group visits. The scenario is modeled as a Clustered Orienteering Problem with Subgroups, and a Tabu Search algorithm is employed to refine the solution, avoiding local optima and ensuring efficient routing. In the final phase, moving individuals are incorporated into the environment. The elastic band method is reapplied, using the planned path as the initial trajectory. The robot then adapts its path in real time to account for environmental changes, enabling it to visit multiple groups efficiently while maintaining safe and adaptive navigation and adhering to the robot’s operational budget constraints. Experimental simulation results validate the effectiveness of the proposed method, demonstrating superior performance compared to conventional algorithms that prioritize only obstacle avoidance and static route optimization. Our approach marks a significant advancement in social mobile robotics by effectively addressing the sequential routing problem in dynamic environments. Furthermore, it showcases its adaptability and suitability for applications involving both static and dynamic elements in robot–human interactions.