Sensor-driven evolutionary optimisation of mobile robot controllers using enzymatic numerical P systems

In recent years, the application of membrane computing within the field of robotics has been widely investigated, providing a bio-inspired alternative to traditional control paradigms. This paper proposes an approach that combines Enzymatic Numerical P Systems (ENPS) with Non-dominated Sorting Genetic Algorithm (NSGA-II), a well-known multi-objective evolutionary optimisation algorithm, as core components of a real-time obstacle avoidance robot controller. The ENPS is employed as the computational framework due to its inherent parallelism, modular structure and suitability for sensor-driven control, enabling a natural mapping between sensory inputs and motor actions. To enhance real-time adaptability, we design NSGA-II to interact with the robot controller during the simulation, optimising the key parameters of the equations that define the robot behaviour, which are implemented as production functions within the ENPS model instance. This improvement leads not only to passing the testing scenarios used in designing the robot controller, but also to the robot speed and trajectory optimisation. In addition, the paper provides a comparative analysis, employing non-parametric testing to benchmark the proposed NSGA-II-based controller against static-weight alternatives. A comprehensive suite of quantitative performance metrics, including execution time, traveled distance or memory consumption is utilised to validate the superiority of the proposed approach.