Autonomous cooperative visual navigation for planetary exploration robots

Planetary robotics navigation has attracted the great attention of many researchers in recent years. Localization is one of the most important problems for robots on another planet in the lack of GPS. The robots need to be able to know their location and the surrounding map in the environment concurrently, to work and communicate together on another planet. In the current work, a novel algorithm is designed to cooperatively localize a team of robots on another planet. Consequently, a robust algorithm is developed for cooperative Visual Odometry (VO) to localize each robot in a planetary environment while detecting both intra-loop closure and inter-loop closures using previously observed area by the robot and shared area from other robots, respectively. To validate the proposed algorithm, a comparison is provided between the proposed cooperative VO and the single version of VO. Accordingly, a planetary analogue real dataset is employed to investigate the accuracy of the proposed algorithm. The results promise the concept of cooperative VO to significantly increase the accuracy of localization. </p

A new adaptive UKF algorithm to improve the accuracy of SLAM

SLAM (Simultaneous Localization and Mapping) is a fundamental problem when an autonomous mobile robot explores an unknown environment by constructing/updating the environment map and localizing itself in this built map. The all-important problem of SLAM is revisited in this paper and a solution based on Adaptive Unscented Kalman Filter (AUKF) is presented. We will explain the detailed algorithm and demonstrate that the estimation error is significantly reduced and the accuracy of the navigation is improved. A comparison among AUKF, Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) algorithms is investigated through simulated as well as experimental dataset. An indoor dataset is generated from a two-wheel differential mobile robot in order to validate the robustness of AUKF-SLAM to noise of modeling and observation, and to examine the applicability of the method for real-time navigation. Both experimental and simulation results illustrate that AUKF-SLAM is more accurate than the standard UKF-SLAM and the EKF-SLAM. Finally, the well-known Victoria Park dataset is used to prove the applicability of the AUKF algorithm in large-scale environments.</p

Robust adaptive fuzzy fractional control for nonlinear chaotic systems with uncertainties

The control of nonlinear chaotic systems with uncertainties is a challenging problem that has attracted the attention of researchers in recent years. In this paper, we propose a robust adaptive fuzzy fractional control strategy for stabilizing nonlinear chaotic systems with uncertainties. The proposed strategy combined a fuzzy logic controller with fractional-order calculus to accurately model the system’s behavior and adapt to uncertainties in real-time. The proposed controller was based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller subjected to fuzzy rules. The stability of the closed-loop system was guaranteed using Lyapunov theory. To evaluate the performance of the proposed controller, we applied it to the Duffing–Holmes oscillator. Simulation results demonstrated that the proposed control method outperformed a recently introduced controller in the literature. The response of the system was significantly improved, highlighting the effectiveness and robustness of the proposed approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties.</p

Optimal fuzzy robust PID controller for active suspension systems

The suspension systems are responsible for neutralizing the vibrations caused by the roughness of the road surface imposed on the car. In this paper, a quarter car model (with two degrees of freedom) is employed to investigate the exerted vibrations on the suspension system. After developing the equation of motion, a combination of two fuzzy and robust PID (FRPID) controllers is applied to the system to suppress the vibrations. The coefficients of these controllers are parameters that are optimized by the Whale Optimization Algorithm (WOA). It is observed that the proposed approach is successful to control the car suspension system properly with a very low error. Finally, the proposed controller is compared with a recently published method in the literature. As the results show, the proposed control method in this paper provides better outcomes.</p

Distributed cooperative visual odometry for planetary exploration rovers

Navigation is a basic skill for planetary exploration robots. In the last years, planetary robotics navigation has become an important research field that includes all the robot capabilities such as perception, localisation, and mapping. This paper provides a novel algorithm to cooperatively localise a distributed team of robots on another planet when the initial pose of the robots is unknown. To perform this, a single Visual Odometry (VO) algorithm based on the conventional KLT feature tracker is employed to localise each single exploration rover in a planetary environment while detecting loop closures. The trajectory of the robots is described by a pose graph form in the designed Cooperative VO (CVO) algorithm. Detecting loop closure from the previously observed areas by the robots leads to triggering the optimisation process and improving the accuracy of the localisation. Accordingly, a planetary analogue real dataset is used to investigate the accuracy of the proposed algorithm. The superiority of the proposed distributed CVO is proved by comparing the obtained results with the single VO algorithm.</p