Kinematic control of a cable-driven snake-like manipulator for deep-water based on fuzzy PID controller

The traditional deep-water manipulators have several problems to work in confined spaces, such as large volume, complex structure, and inability. To solve these problems, a novel cable-driven snake-like manipulator robot for deep-water is proposed. In this study, the structure design of the cable-driven snake-like manipulator robot is first introduced. Then, we establish the kinematics model of the proposed cable-driven snake-like manipulator robot, which includes three parts: motor-cable kinematics, cable-joint kinematics, and joint-end kinematics. Especially, a tip-following algorithm (Supplemental Material) is presented to fit the confined and complicated underwater scenarios. Furthermore, a kinematics control strategy based on fuzzy PID controller is presented to reduce the tracking error caused by transmission mechanism, and the simulation of the cable-driven snake-like manipulator is carried out based on the MATLAB. The results demonstrate that the tracking error is less than 0.04 mm, which shows the proposed control strategy is effective.

Novel adaptive backstepping control for uncertain manipulator robots using state and output feedback

In this paper, a new adaptive control strategy, based on the Modified Function Approximation Technique, is proposed for a manipulator robot with unknown dynamics. This novel strategy benefits from the backstepping control approach and the use of state and output feedback. Unlike the conventional Function Approximation Technique approach, the use of basis functions to approximate the dynamic parameters is completely eliminated in the proposed scheme. Another improvement is eliminating the need to measure velocity by means of integrating a high-order sliding mode observer. Furthermore, utilizing the Lyapunov function theory, it is demonstrated that all controller signals are uniformly ultimately bounded in the closed-loop form. Lastly, simulation and comparative studies are carried out to validate the effectiveness of the proposed control approach.

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.

A human factors analysis of the Stretch mobile manipulator robot

Assistive and mobile robots have potential to support everyday domestic tasks and enable independence for persons in the home. As a first step to evaluating this potential, we assessed the initial unboxing and setup of Hello Robot’s Stretch RE1– a novel mobile manipulator designed for domestic settings. All study procedures took place in the McKechnie Family LIFE Home, which is a smart home research facility on the University of Illinois Urbana-Champaign campus. We used subject matter experts (SMEs) and followed human factors principles to consider obstacles users with diverse needs and capabilities (e.g., older adults, persons with mobility disabilities) might encounter during the unboxing process. We then conducted 50 trials of user testing and critical task analyses in the LIFE home to assess the feasibility and usability for different use cases. Research team members controlled Stretch by using a game controller. We used Stretch to manipulate 15 different types of objects that would be part of domestic activities needed to live independently, such as tasks needed for meal preparation. We documented the frequency of errors, time spent manipulating the object, and informal qualitative feedback from teleoperators during and after each trial (using a think-aloud protocol). Implications for future domestic robot design using human factors approaches are discussed.

Integration of DE Algorithm with PDC-APF for Enhancement of Contour Path Planning of a Universal Robot

In the robotic engineering field, the main target, especially in industry, manufacturing, and surgical operations, is reaching the optimal performance of manipulators. The purpose of this paper is to quantify the contour tracking performance of collaborative universal manipulator robot (UR5) by setting the gain of position domain controller. In order to improve and enhance the track of manipulator in experimental applications we utilize differential evolution (DE) optimization, using MATLAB toolbox with an applied robot operating system (ROS). The adopted current approach does not only optimize the gain of position domain controller but also prevent collisions by detecting a “border crossing” without turning off the manipulator and allowing the automation agent to be on the scene, coexisting in harmonic mode and avoiding collisions. This requires the implementation of an algorithm that detects an obstacle to avoid anticipated collisions. For this purpose, the adopted algorithm uses the DE algorithm to modify the artificial potential field (APF). The results of this paper present that on one hand, meta-heuristic optimization algorithm features give the best performance indices for linear and non-linear contours, and on the other hand, DE algorithm features give good modification to APF to generate collision free contour path planning.