Wheeled mobile robots (WMRs) are being increasingly integrated into various sectors such as logistics and transportation. However, their accurate trajectory tracking remains a challenge. To address this control issue, this study proposes a trajectory correction technique for a wheeled mobile robot (WMR). This proposal uses a functional-link neural network (FLNN) that adjusts the trajectory error with the aim of minimizing it. This error is propagated backward by adjusting the different parameters of the controller. The controller was designed using a combination of linearization feedback, sliding mode control, and FLNN, where the latter provides adaptability to the controller. Using the Lyapunov stability theory, the stability of the proposal was demonstrated. Experiments and simulation analyses were also carried out to demonstrate the practical feasibility of the proposal.

For robots to navigate effectively in human-shared spaces, they should follow behavioral rules that not only prevent collisions with humans but also reduce uncertainty about the robot’s actions. These rules should be grounded in human collision-avoidance behavior, as humans can often recognize evasive intentions by observing another person’s movements, and robots are expected to exhibit behaviors that allow their evasive intentions to be similarly understood. This paper identifies and characterizes human collision-avoidance maneuvers using a multi-method approach comprising qualitative analysis of video recordings to identify the maneuvers, survey-based analysis to assess self-perceived evasion distances, and computer robot simulator-like human–robot interaction experiments to validate the observed metrics and investigate whether the identified human behavior patterns extend to human-guided robot navigation. A symmetry–asymmetry perspective is then adopted to investigate these maneuvers, and a functional interpretation of the main evasive behaviors is proposed based on their morphological properties and Path Selection Freedom. This interpretation is subsequently translated into human-inspired collision-avoidance strategies for mobile robots through a Behavior Management Module implemented using fuzzy inference mechanisms for maneuver selection and trajectory generation. Beyond the implementation itself, this work establishes a principled basis for transferring the functional principles underlying human collision avoidance to mobile robot navigation, contributing to the development of more natural, understandable, and socially conforming robot behavior in shared human environments.