Intelligent Service Robotics最新文献

We propose a novel point cloud alignment algorithm, namely PCR-DAT, for radar inertial ranging and localization. In environments with complex feature variations, the distribution trend of features is always changing, and the traditional alignment algorithms often fall into local optimums when dealing with regional point clouds with a combination of rich and sparse feature points, thus affecting the accuracy and stability of point cloud alignment. This paper addresses this issue by constructing a cost function composed of distance factors obtained from lidar measurements, normal distribution factors, and IMU pre-integration measurement factors. The core idea involves analyzing and classifying features in the target environment, defining different residual factors based on feature categories. Sparse features correspond to distance factors, while rich features correspond to distribution factors. Subsequently, a nonlinear optimization process is employed to estimate the robot’s pose. We evaluate the accuracy and robustness of the algorithm in various scenarios, including experiments on the KITTI dataset and field data collected during UGV movement. The results demonstrate that the DAT point cloud registration algorithm effectively addresses the pose prediction problem in the presence of feature degradation.

Around one billion people (15% of the global population) suffer from impairments such as muscular weakness, partial or complete paralysis, and lack of assistance in their lower extremities. As a result, robotic devices such as the exoskeleton are used to assist paralysed patients with their day-to-day tasks, aid in neuro-rehabilitation, and enhance the mobility of the user, simultaneously. In this paper, the design and control of a lower extremity exoskeleton with a polycentric knee joint, named as EXXON is presented. It is designed in a way such that it can provide assistance to paralytic patients for performing daily tasks and improve their quality of life and to aid faster and better rehabilitation. Based on the degrees of freedom (DOF) and range of motion of the human lower limb joints, the joints of EXXON has been designed. EXXON has a total 10 DOF at both of its limbs: both active hip flexion/extension and adduction/abduction, active knee flexion/extension, active ankle dorsiflexion/plantarflexion and passive inversion/eversion. The knee joint of EXXON is designed to be a polycentric joint so that it can mimic the moving centrode of anatomical knee. For this, a double 4-bar mechanism is designed. The first 4-bar is for tracing the trajectory of the anatomical knee while the second one is for transmitting the torque to the first 4-bar from the knee actuator. A dynamic model of EXXON has been developed for its controlling. A Computed Torque Controller is designed to simulate the dynamic model of EXXON, such that its joints can track the desired gait trajectories. Simulation results with the developed controller are also presented.

Mars exploration significantly advances our understanding of planetary evolution, the origin of life, and possibilities for Earth’s future. It also holds potential for discovering new mineral resources, energy sources, and potential settlement sites. Navigating Mars’ complex environment and unknown terrain is a formidable challenge, particularly for autonomous exploration. The hexapod walking robot, inspired by ant morphology, emerges as a robust solution. This design offers diverse gait options, mechanical redundancy, high fault tolerance, and stability, rendering it well suited for Martian terrain. This paper details the development of an ant-inspired hexapod robot, emphasizing its terrain adaptability on Mars. A novel terrain detection method utilizing a convolutional neural network enables efficient identification of varied terrain types through semantic segmentation of visual images. Additionally, we introduce a comprehensive motion performance evaluation index for the hexapod robot, including speed and stability. These metrics facilitate effective performance assessment in different environments. A key innovation is the proposed gait switching method for the hexapod robot. This approach allows seamless transition between gaits while in motion, enhancing the robot's ability to traverse challenging terrains. The experimental results validate the effectiveness of this method. Utilizing gait switching leads to a significant improvement in robot performance and stability—58.5% and 41.4% better than using tripod and amble gaits, respectively. Compared to single tripod, amble, and wave gaits, the comprehensive motion performance indices of the robot improved by 36.3%, 30.6%, and 41.1%, respectively. This study can provide new ideas and methods for the motion evaluation and adaptive gait switching of multilegged robots in complex terrains. It significantly enhances the mobility and adaptability of such robots in challenging environments, contributing valuable knowledge to the field of planetary exploration robotics.

Abstract

Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.

This study presents a methodology employing Iterative Learning Control (ILC) to enhance the control performance of soft grippers equipped with multiple curvatures and variable stiffness. ILC is a learning-based control approach that progressively reduces errors in repetitive tasks, known for delivering superior performance in complex systems. In the context of the increasing utilization of robotic technology across various industries, the control technology of soft robots, especially soft grippers with multiple curvatures and variable stiffness, is a crucial issue. While prior research has focused on single-curvature and single-input single-output (SISO) systems, this study addresses the intricate control problem of multi-input multi-output (MIMO) soft gripper systems capable of multiple curvatures. It also proposes an enhanced design for soft grippers with multiple curvatures and variable stiffness while highlighting the potential of ILC for enhancing control performance.

This paper presents a safe A* algorithm for the path planning of automated guided vehicles (AGVs) operating in storage environments. Firstly, to overcome the problems of great collision risk and low search efficiency in the path produced by traditional A* algorithm, a new evaluation function is designed by introducing repulsive term and assigning dynamic adjustment weights to heuristic items. Secondly, a Floyd deletion algorithm based on the safe distance is proposed to remove redundant path points for reducing the path length. Moreover, the algorithm replaces the broken line segments at the turns with a cubic B-spline to ensure the smoothness of turning points. The simulation applied to different scenarios and different specifications showed that, compared with other three typical path planning algorithms, the path planned by the proposed safe A* algorithm always keeps a safe distance from the obstacle and the path length is reduced by 1.95(%), while the planning time is reduced by 25.03(%) and the number of turning point is reduced by 78.07(%) on average.

Abstract

How to realize flexible behavior decision making is an important prerequisite for mobile robots to perform various tasks. To solve the problems of poor real-time performance and adaptability of traditional methods, this paper proposes a method that simulates cerebellar function through developmental network, and simulates the function of “what” and “where” channels in the visual system as well as the neuromodulatory mechanisms of dopamine and serotonin, so as to improve the adaptability of cerebellar model to behavioral decision making under supervised learning strategies. At the same time, this paper pays special attention to the strategy of simulating cerebellar reinforcement learning. By simulating the sleep recall mechanism of hippocampus and the neuromodulatory mechanism of acetylcholine and norepinephrine, mobile robots can have continuous and stable learning ability in unfamiliar environment, and improve the real-time and adaptability of their behavioral decision making. Simulation results in both static and dynamic environments, as well as the results in the static physical environment, validate the potential of this model, indicating that the cerebellar model based on reinforcement learning plays an important role in the behavioral decision making of mobile robots.

Many studies have shown that robots can provide medical help to patients, such as supporting physical movements, managing mood, or simulating cognitive function. However, robotic cognitive/language assessment, which is vital for mental health care, has not been fully explored and is limited to only a few types of assessment. The aim of this study is to present and evaluate a social robot equipped with a web-based language assessment for sentence comprehension test (SCT) with a dialogue system involving yes/no questions. A total of 50 participants took the test with 36 items conducted by a robot (robot-SCT), while a total of 55 participants took the same test but conducted by a human examiner (human-SCT). Comparative analyses were performed to evaluate the validity of the robot-SCT in terms of test scores and time-related measures. Usability was evaluated through the system usability score and interview feedback. With regard to the validity of the robot-SCT, the test scores indicated no significant differences between the robot-SCT and human-SCT. In addition, conditional differences in reaction time for the test items were observed, similar to the previous paper-and-pencil researches. The high system usability scores (i.e., mean = 78.5, SD = 11) demonstrated the high usability of the robot-SCT. This study demonstrates the validity and usability of robotic language assessment among normal adults. However, further evaluation is required for people with dementia or mild cognitive impairment.

This paper presents a novel neural network-based control approach designed for industrial robot manipulators characterized by uncertain closed architectures and unknown dynamics. Industrial and commercial robot manipulators typically employ closed control architectures, which limit the ability to make modifications or comprehend the inner control processes. Users are generally restricted to providing joint position or velocity commands for controlling the manipulator. Furthermore, the integration of these robots with external sensors for modern applications poses challenges to system stability. Our proposed solution utilizes neural networks to approximate the robot’s dynamic model and low-level controller. The proposed controller is introduced as an outer (external feedback) loop, ensuring independence from the inner controller configuration. This outer loop leverages external sensor data and the desired trajectory to calculate commands for joint velocities. Consequently, this approach offers greater design flexibility for modern control applications. Unlike previous studies, our work introduces novelty through unconstrained control actions, avoiding the need for inner controller configuration and control gain structure. To validate our method, we conducted experiments using two industrial manipulators, namely the UR5e and UR10e, and the results clearly demonstrate the superior performance and industrial applicability of the framework we have developed.

Tactile sensing is essential for robots to adequately interact with the physical world, but creating tactile sensors for the robot’s soft and flexible body surface has been a challenge. The resistance tomography-based tactile sensors have been introduced as a promising approach to creating soft tactile skins because the sensor fabrication can be greatly simplified with the aid of a computation model. This article introduces an electronic design strategy dividing frontend and backend electronics for the resistance tomography-based tactile sensors. In this scheme, the frontend is made of the piezoresistive structure and electrodes that can be changed depending on the required geometry. The backend is the electronic circuit for resistance tomography, which can be used for various frontend geometries. To evaluate the use of a unified backend for different frontend geometries, two frontend specimens with a square shape and a circular shape are tested. The minimum detectable contact force and the minimum discernible contact distance are calculated as (0.83 times 10^{-4}) N/mm(^2), 2.51 mm for the square-shaped frontend and (1.19 times 10^{-4}) N/mm(^2), 3.42 mm for the circular-shaped frontend. The results indicated that the proposed electronic design strategy can be used to create tactile skins with different scales and geometries while keeping the same backend design.