Intelligent Service Robotics最新文献

This research aims to design controllers based on the hedge-algebras (HA) theory to control differential robots that track reference trajectories. First, the HA-based controller (denoted as HA controller) is synthesized by selecting a suitable qualitative control rule base for the investigated model as a rule-based optimization problem. Then, the optimal HA-based controller (denoted as oHA controller) is established based on the problem of simultaneously optimizing the rule base, the reference interval of variables, and the fuzzy parameters of the variables. Optimization problems aim to minimize the distance between the robot and the reference trajectory. The optimization problems in this study use the Balancing composite motion optimization (BCMO) algorithm. A controller based on fuzzy set theory (denoted as FC controller) with the same parameters as the HA controller is also included for comparison. The simulation results show that the HA and oHA controllers demonstrate many advantages over the FC controller regarding reference trajectory tracking ability, calculation time, and control robustness. The main contribution of this work consists of (i) The development of a novel HA, oHA approaches to control a mobile robot to follow reference trajectories accurately; (ii) Providing optimal global-based BCMO in terms of minimal tracking error with computational efficiency; (iii) The investigation of one control rule base for HA and oHA controllers, which is effective for many different reference orbits; (iv) The development of a robust controller that adapts to the robot’s geometric parameters changes; (v) The proposed controllers have superior performance results compared to controllers based on fuzzy set theory in terms of position error between the robot and the reference trajectory, control action calculation time, and robust ability to change robot parameters.

Aiming at the shortcomings of traditional A* algorithm in 3D global path planning such as inefficiency and large computation, an A* optimization algorithm based on adaptive expansion convolution is proposed to realize UAV path planning. First, based on the idea of expansion convolution, the traditional A* algorithm is optimized to improve the search efficiency by improving the search step length and reducing the number of nodes needed to select the extended nodes in path planning; adding a weight factor to the cost function to select the appropriate weight of the cost function by keeping the principle of optimal path length while accelerating the planning speed to improve the planning speed of the algorithm; finally, using path pruning to further optimize the paths and reduce the problems of path redundancy. The simulation analysis results show that compared with the traditional A* algorithm, the improved algorithm in this paper reduces the number of extended nodes and shortens the planning time.

The use of social assistive robots for interactive stimulation has strong potential in neurorehabilitation therapies. It is of particular interest in the case of pediatric patients to promote children’s motivation and adherence, specially when those robots are able of guide gamified activities, as it is the case of NAOTherapist. NAOTherapist is a Social Assistive Robotics (SAR) platform for hands-off rehabilitation based on upper-limb activities, that was originally designed for pediatric patients with Cerebral Palsy (CP) or Obstetric Braxial Plexus Palsy (OBPP). Formerly, it endowed the therapists with tools to perform rehabilitation exercises. This paper proposes the gamification of NAOTherapist in order to incorporate additional characteristics which allow its intensive use in new rehabilitation procedures, such as the Hand-Arm Bimanual Intensive Therapy (HABIT). This intensive therapy setting involves daily activities in several consecutive days, which require a strong engagement of the patients with the therapeutic methods and the acceptation of the NAOTherapist as a rehabilitation system. The gamified system shows very accurate results considering the different aspects defined in the USUS methodology; namely Usability, Social acceptance, User experience and Societal impact.

This paper introduces an ultrasonic transducer with a concave curved structure. The transducer is based on a concave piezoelectric film on a silicone support with an air cavity. Specifically, an air cavity exists between the piezoelectric film and the support in the shape of a curved cuboid, providing space for the film to vibrate. We build a theoretical model of a concave piezoelectric transducer. To validate the model, we demonstrate a concave piezoelectric transducer and measure the ultrasound pressure field using an acoustic imaging camera. Two types of experiments are conducted by supplying a sinusoidal input voltage with a frequency sweep and a voltage sweep. The experimental results share similarity with the theoretical results. In addition, we conduct a parametric study to analyze the characteristics of the transducer. Interestingly, we find that the radius of curvature and axial length primarily contribute to ultrasound pressure.

Drones have performed various tasks, such as surveillance, photography, agriculture, and package delivery. However, these tasks typically involve drones simply observing or capturing information from their surroundings without physically interacting with them. Aerial manipulation shifts this paradigm and implements drones with robotic arms that allow interaction with the environment rather than simply touching it. For example, in construction, aerial manipulation in conjunction with human interaction could allow operators to perform several tasks, such as hosing decks, drilling into surfaces, and sealing cracks via a drone. For over a decade, researchers have been working on aerial manipulation for industrial applications. These works are valuable to aerial manipulation but have not been widespread in the public domain yet. This is because most of the works are conducted in controlled indoor environments (e.g., motion capture systems), and the knowledge gap exists between researchers and the wider public who are interested in deploying aerial manipulation for practical tasks. To fill this gap, our recent work integrated the worker’s experience into aerial manipulation using haptic technology. The net effect is that such a human-in-the-loop system could enable workers to leverage their experience to complete manipulation tasks while remotely controlling a mobile manipulating drone on the task site. The system increased the feasibility and adaptiveness of aerial manipulation. The remaining challenges are completing tasks beyond the operator’s line-of-sight and lack of dexterity. To address the challenges, we present a human-embodied drone interface in this article. The interface consists of immersive virtual/augmented reality and haptic technologies. Such an interface allows the drones to embody and transport the operator’s senses, actions, and presence to a remote location in real-time. Therefore, the operator can both physically interact with the environment and socially interact with actual workers on the worksite. Two different human-embodied interfaces are developed and tested with several tasks suggested by the United States Department-of-Transportation: pick-and-place, drilling, peg-in-hole, and key insert/rotation. The conclusion describes the advantages and challenges of the interface with future works.

To achieve the autonomy of mobile robots, effective localization is an essential process. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is most commonly used in many indoor environments. However, when the initial position is unknown, the efficiency and success rate of localization based on the AMCL algorithm decrease with the increasing area of the map. In this paper, an improved MCL algorithm named off-line feature matching and improved particle swarm optimization for Monte Carlo Localization (OFM-IPSO MCL) is proposed. Feature matching is adopted to reduce the online computational burden. Compared with the AMCL algorithm, OFM-IPSO MCL shows better results in the problems of positioning without initial pose and kidnapping robot by using a small number of particles. For positioning without an initial pose, the OFM-IPSO algorithm uses the feature extraction and feature matching methods to find the possible positions of the robot. In the problem of kidnapping robot, a method for determining if the robot has been "kidnapped" is proposed, which determines whether the robot has lost its pose. The validity and efficiency of the OFM-IPSO MCL algorithm are demonstrated by the Robotic Operating System (ROS). Extensive results and comparisons are also provided in this paper.

This paper presents an efficient cascade calibration method with an improved Levenberg–Marquardt and sine–cosine hybrid algorithm to enhance the absolute positioning accuracy of robotic grinding systems. To expedite convergence in the Levenberg–Marquardt algorithm, a dynamic adaptive weight mechanism is introduced, enhancing global and local search capabilities. Furthermore, a novel learning rate, combining exponential and cosine functions, addresses local optima in the algorithm. The improved Levenberg–Marquardt algorithm is employed to obtain suboptimal values for robot kinematic parameter deviations. Subsequently, these values are used as central points for generating a candidate solution set in the sine–cosine algorithm, resulting in more accurate kinematic parameter deviation identification. This innovative dual-search optimization approach combines the two algorithms. Experimental results confirm the substantial improvements in absolute positioning accuracy and surface machining precision achieved by the proposed model, with the calibration method’s effectiveness verified through experimentation.

Lumbar stabilization exercises are commonly employed in the rehabilitation of patients with low back pain. However, many patients discontinue these exercises, generally calisthenics using various postures or tools, due to the difficulty of providing an appropriate exercise load intensity. This challenge results in an inability to apply the desired strength to the target lumbar muscles and sometimes leads to an excessive load on unintended areas during calisthenics. Consequently, a method that enables patients to exercise continuously and progressively recover is required, specifically one that can target the lumbar muscles with a desired load. To address this issue, we propose a rehabilitation assistive device that quantitatively controls the lumbar spine load. In isometric lumbar stabilization exercises, our method involves precise compensation for gravity. The device, equipped with a series elastic actuator, is positioned beneath the patient in a lying posture. It applies an assistive force in the direction opposite to gravity, enabling precise control of the load on the lumbar region and reducing the vertical load on the spine. To validate the effectiveness of our proposed method, we conducted experiments with 20 healthy subjects across three exercises and analyzed the electromyography signal using nonparametric statistical methods. Our objective was to determine whether the load on the target lumbar muscles could be precisely and gradually controlled. The statistical results indicate that exercises performed using the proposed device produce statistically significant load changes in the target lumbar muscles.

Redundant robots are gaining popularity for their agility in service tasks, but they struggle with managing multiple tasks in dynamic and unstructured environments. Research is currently centered around adjusting task priorities to facilitate the robot’s adaptability to different situational demands. This paper addresses the challenge of automated task prioritization in multi-task handling and presents a solution for robots to effectively execute demanding tasks, even when faced with limited redundancy and multiple constraints. We introduce the concept of secondary merged tasks and formulate task merging as a matrix design problem. An iterative updating algorithm based on real-time task status is proposed to enable automatic prioritization and dynamic adjustment of tasks. This methodology ensures appropriate execution of all tasks at the right time. We analyze the convergence of weight transfer between redundancies and task dependencies, ensuring stable task execution. Simulation experiments and real-world experiments using 9-DOF mobile manipulator and 6-DOF fixed manipulator are conducted to validate the proposed method. This research provides a feasible approach for task prioritization in multi-task handling and holds potential applications.

Abstract

Gripping objects firmly and quickly is an important function of the human hand for everyday life. Prosthetic devices face significant challenges in replicating these capabilities, particularly in achieving a delicate balance between swift grasping and substantial grip strength while adhering to weight and form-factor constraints. To address these challenges, this study introduces a novel posture-dependent variable transmission (PDVT) that mimics the human hand’s behavior by employing a spiral-shaped spool. The PDVT’s spiral-shaped spool replicates the human hand’s quick and gentle pre-contact movements followed by a stronger force application after contact with the object. Additionally, a compressive series elastic spring enhances tendon tension across a wide range of finger postures. The manufacturing method of PDVT, utilizing both 3D printing and metal processing, enables the creation of complex spiral shapes. The PDVT demonstrates improvements in both speed and grip strength compared to conventional rigid spool mechanisms. The PDVT has the potential to be applied to various robotic grasping systems.