Relative ultra-wideband based localization of multi-robot systems with kinematic extended Kalman filter

Localization plays a critical role in the field of distributed swarm robotics. Previous work has highlighted the potential of relative localization for position tracking in multi-robot systems. Ultra-wideband (UWB) technology provides a good estimation of the relative position between robots but suffers from some limitations. This paper proposes improving the relative localization functionality developed in our previous work, which is based on UWB technology. Our new approach merges UWB telemetry and kinematic model into an extended Kalman filter to properly track the relative position of robots. We performed a simulation and validated the improvements in relative distance and angle accuracy for the proposed approach. An additional analysis was conducted to observe the increase in performance when the robots share their control inputs.

Optimizing an Adaptive Fuzzy Logic Controller of a 3-DOF Helicopter with a Modified PSO Algorithm

This paper investigates the controller optimization for a helicopter system with three degrees of freedom (3-DOF). To control the system, we combined fuzzy logic with adaptive control theory. The system is extensively nonlinear and highly sensitive to the controller's parameters, making it a real challenge to study these parameters' effect on the controller's performance. Using metaheuristic algorithms for determining these parameters is a promising solution. This paper proposes using a modified particle swarm optimization (MPSO) algorithm to optimize the controller. The algorithm shows a high ability to perform the global search and find a reasonable search space. The algorithm modifies the search space of each particle based on its fitness function value and substitutes weak particles for new ones. These modifications have led to better accuracy and convergence rate. We prove the efficiency of the MPSO algorithm by comparing it with the standard PSO and six other well-known metaheuristic algorithms when optimizing the adaptive fuzzy logic controller of the 3-DOF helicopter. The proposed method's effectiveness is shown through computer simulations while the system is subject to uncertainties and disturbance. We demonstrate the method's superiority by comparing the results when the MPSO and the standard PSO optimize the controller.