Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Vision Based Tracking and Interception of Moving Target by Mobile Robot Using Fuzzy Control

This paper presents a simple Fuzzy Logic Controllers (FLC) based control strategy to solve the tracking and interception problem of a moving target by a mobile robot equipped with a pan-tilt camera. Before sending commands to the mobile robot, video acquisition and image processing techniques are employed to estimate the target’s position in the image plane. The estimate coordinates are used by a fuzzy logic controller to control the pan-tilt camera angles. The objective is to ensure that the moving target is always at the middle of the camera image plane. A second FLC is used to control the robot orientation and to guarantee the tracking and interception of the target. The proposed pan-tilt camera and robot orientation controllers’ efficiency has been validated by simulation under Matlab using Virtual Reality Toolbox

Mobile robot visual navigation based on fuzzy logic and optical flow approaches

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.Peer ReviewedPostprint (author's final draft

Search Methods for Mobile Manipulator Performance Measurement

Mobile manipulators are a potential solution to the increasing need for additional flexibility and mobility in industrial robotics applications. However, they tend to lack the accuracy and precision achieved by fixed manipulators, especially in scenarios where both the manipulator and the autonomous vehicle move simultaneously. This thesis analyzes the problem of dynamically evaluating the positioning error of mobile manipulators. In particular, it investigates the use of Bayesian methods to predict the position of the end-effector in the presence of uncertainty propagated from the mobile platform. Simulations and real-world experiments are carried out to test the proposed method against a deterministic approach. These experiments are carried out on two mobile manipulators - a proof-of-concept research platform and an industrial mobile manipulator - using ROS and Gazebo. The precision of the mobile manipulator is evaluated through its ability to intercept retroreflective markers using a photoelectric sensor attached to the end-effector. Compared to the deterministic search approach, we observed improved interception capability with comparable search times, thereby enabling the effective performance measurement of the mobile manipulator

Stochastic Modeling for Mobile Manipulators

Mobile manipulators are valuable and highly desired in many fields, especially in industrial environments. However, determining the end effector position has been challenging for scenarios where the base moves at the same time that the arm follows commands to perform specific tasks. Earlier works have attempted to dynamically evaluate the problem of positioning error for mobile manipulators, but there is still room for further improvement. In this thesis, we devise a dynamical model that leverages stochastic search strategies for mobile manipulators. More specifically, we develop a dynamic model that estimates the position of the robot using an Unscented Kalman filter. Simulations using the Robot Operating System (ROS) and Gazebo were carried out to evaluate our model. Our results for the stochastic method show that it outperforms a deterministic approach (spiral search) under specific Kalman filter covariances of the process and observation noises. Compared to the state of the art, our proposed approach is more robust and efficient, proving to work under different arrangement scenarios with significant better performance

Autonomous Visual Servo Robotic Capture of Non-cooperative Target

This doctoral research develops and validates experimentally a vision-based control scheme for the autonomous capture of a non-cooperative target by robotic manipulators for active space debris removal and on-orbit servicing. It is focused on the final capture stage by robotic manipulators after the orbital rendezvous and proximity maneuver being completed. Two challenges have been identified and investigated in this stage: the dynamic estimation of the non-cooperative target and the autonomous visual servo robotic control. First, an integrated algorithm of photogrammetry and extended Kalman filter is proposed for the dynamic estimation of the non-cooperative target because it is unknown in advance. To improve the stability and precision of the algorithm, the extended Kalman filter is enhanced by dynamically correcting the distribution of the process noise of the filter. Second, the concept of incremental kinematic control is proposed to avoid the multiple solutions in solving the inverse kinematics of robotic manipulators. The proposed target motion estimation and visual servo control algorithms are validated experimentally by a custom built visual servo manipulator-target system. Electronic hardware for the robotic manipulator and computer software for the visual servo are custom designed and developed. The experimental results demonstrate the effectiveness and advantages of the proposed vision-based robotic control for the autonomous capture of a non-cooperative target. Furthermore, a preliminary study is conducted for future extension of the robotic control with consideration of flexible joints

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Robotic Ball Catching with an Eye-in-Hand Single-Camera System

In this paper, a unified control framework is proposed to realize a robotic ball catching task with only a moving single-camera (eye-in-hand) system able to catch flying, rolling, and bouncing balls in the same formalism. The thrown ball is visually tracked through a circle detection algorithm. Once the ball is recognized, the camera is forced to follow a baseline in the space so as to acquire an initial dataset of visual measurements. A first estimate of the catching point is initially provided through a linear algorithm. Then, additional visual measurements are acquired to constantly refine the current estimate by exploiting a nonlinear optimization algorithm and a more accurate ballistic model. A classic partitioned visual servoing approach is employed to control the translational and rotational components of the camera differently. Experimental results performed on an industrial robotic system prove the effectiveness of the presented solution. A motion-capture system is employed to validate the proposed estimation process via ground truth