Robotics Research Research Articles

GESTURE CONTROL HUMAN FOLLOWING ROBOT

A rapidly developing subject of study, humanoid robots has drawn a lot of interest recently and will be crucial to robotics research and numerous applications in the twenty-first century and beyond. A "Human Following Robot" that can communicate and coexist with humans is necessary in our fastpaced environment. These robots may aid people in a variety of scenarios and gather or keep track of specific data related to the human subject thanks to their capacity to follow humans. This article describes a prototype that combines an Arduino Uno with common sensors such an infrared and ultrasonic sensor. The controller is responsible for controlling the motors, while the microprocessor handles all processing. There are several ways to further customize this robot, including via the use of Bluetooth and PixyCamera.

An Open-Source UAV Platform for Swarm Robotics Research: Using Cooperative Sensor Fusion for Inter-Robot Tracking

An Open-Source UAV Platform for Swarm Robotics Research: Using Cooperative Sensor Fusion for Inter-Robot Tracking

Microcontroller-Based Design of Self-Balancing Two-Wheeler Robotic Vehicles Using Gyroscopic Sensor

Robots have seamlessly integrated into our daily lives, serving a multitude of functions from lifesaving tasks to providing entertainment, highlighting their indispensable role in modern society.As their popularity continues to soar, an abundance of research initiatives has been taken all over the world. One particularly captivating field of robotics research that has captured widespread attention focuses on unstable systems, exemplified by endeavours like the development of self-balancing electric vehicles, such as the inverted pendulum, stimulating innovation and curiosity on a global scale. Self-balancing electric vehicles are a fascinating innovation in the field of personal transportation. These vehicles utilize advanced technologies such as gyroscopes, accelerometers, and sophisticated control algorithms to maintain balance without the need for a rider to actively stabilize the vehicle. One of the most well-known examples of this technology is the Segway, which popularized the concept of self-balancing transportation. The objective of thiswork is to make a functioning prototype model which is used to learn the principles of control theories behind it. Gyroscopic sensors serve the critical function of detecting any deviation from optimal balance by sensing the tilting movements of the device, ensuring prompt corrective actions to maintain stability. It offers several potential benefits, including increased stability and safety for riders, particularly in urban environments where navigating through crowded streets can be challenging.

Research on application of intelligent robot based on AI technology in agriculture

With the continuous progress of science and technology, artificial intelligence has profoundly changed various industries, and the agricultural field is no exception. As an important part of modern agriculture, smart agriculture is rapidly emerging, creating brand-new formats and opportunities. At present, the development of agricultural industry is also developing in the direction of high efficiency, intensification, precision and intelligence. As an important branch of modern computer science, AI plays an increasingly high role in the development of modern agriculture. The research and development of intelligent robots based on AI technology is becoming more and more mature, and the application fields are becoming more and more extensive. Under this background, this paper analyzes the development of intelligent robots in the field of agricultural automation and its main applications.

Construction and Optimization of a Collaborative Harvesting System for Multiple Robotic Arms and an End-Picker in a Trellised Pear Orchard Environment

In order to meet the needs of intensive mechanized picking in trellised pear orchards, this paper designed a pick-place integrated end-picker based on the analysis of agronomic characteristics of trellised pear gardens and fruit. In order to realize the accurate positioning of pears in picking, based on the kinematic analysis of robot arms and the construction of a private dataset, the YOLOv5s object detection algorithm was used in conjunction with a depth camera to achieve fruit positioning. The hand–eye system calibration was carried out. Aiming at solving the problems of redundancy, inefficiency, and uneven distribution of task volume in the conventional multiple robot arms algorithm, a simulated annealing algorithm was introduced to optimize the picking sequence, and a task allocation method was proposed. On the basis of studying several key parameters affecting the performance of the algorithm, the picking efficiency was greatly optimized. And the effectiveness of the proposed multi-robot collaborative picking method in a trellised pear orchard environment was demonstrated through experiments and simulation verification. The experiments showed that the picking efficiency of the integrated end-picker was increased by about 30%, and the success rate was significantly higher than that of the flexible grippers. The results of this study can be utilized to advance robotic pear-picking research and development.

Cooperative object transportation with differential-drive mobile robots: Control and experimentation

Non-prehensile cooperative object transportation is a challenging model problem for distributed control and organization methods but also has practical applications. Therefore, it is widely studied in distributed robotics research. This paper describes and evaluates a novel transportation scheme for differential-drive mobile robots that is, to the authors’ best knowledge, the most versatile scheme of its kind successfully evaluated with real-world hardware. The proposed scheme can conceptually deal with any number of robots and arbitrary polygonal objects, including non-convex ones, without having to retune, retrain, or reconfigure any of the control parameters between different scenarios. This is achieved by splitting the task into a formation control and a formation finding task, both of which are tackled with model-based approaches using distributed optimization. Formation control and formation finding are complicated by the robots’ non-holonomic kinematic constraints. Therefore, a tailored distributed model predictive controller is used for formation control. Finding formations relies on a multibody-dynamics representation of the robots-object system to properly account for contact and non-holonomic constraints. Due to these measures, the transportation scheme achieves a very satisfactory performance and dexterity in real-world hardware experiments utilizing network communication and distributed computation.

Method for planning the way of UGV using a modification of dynamic bi-directional RRT algorithm.

The path planning problem of unmanned autonomous ground vehicles has always been an acute problem in the field of autonomous ground robotic systems research. From the point of view of the complexity of the tasks during the conduct of combat operations in the urbanized space of the densely built-up city with a constantly changing landscape, the tasks entrusted to the UGV are constantly becoming more difficult, and the scenarios of the use of the UGV demonstrate a diversified trend in the development of new management and control systems. The rapidity of conducting modern military operations in an urbanized space appears as a complex and multifaceted task. Considering the complexity of the UGV movement process, at the current stage of the development of robotic systems, the general trend is to abandon remote control of robotic complexes with the transition to automatic modes, which requires the development and implementation of algorithms for automatic interaction and movement of military mobile robotic systems. To solve the problems of the Rapidly-Exploring Random Tree *Fixed Nodes algorithm regarding its low speed for obtaining track junctions and the impossibility of using it in a dynamic environment when planning a UGV path. To solve the problem of accelerating the acquisition of collision-free paths in real time in two-dimensional space, it is proposed to apply a modified dynamic bidirectional RRT* algorithm with reference nodes. The algorithm is a modification of Rapidly-Exploring Random Tree *Fixed Nodes using a bidirectional greedy search method to speed up and solve the problem of the unidirectional Rapidly-Exploring Random Tree algorithm regarding its slow search speed, as well as the difficulties of decision making in a narrow environment caused by blind random sampling. In the case of dynamic disturbance movement, taking advantage that reference nodes do not require much calculation in planning, in the process of iterative path optimization, the proposed algorithm updates the map information in real time and repairs the damaged output path to complete the dynamic path planning.

Navit(oo)n: Open Source Mobile Robot Project for Nakanoshima Robot Challenge

Recently, the legislation regarding autonomous mobile robots for outdoor pedestrian areas have been advancing, leading to increased expectations for task automation such as transportation and cleaning. Outdoor environments like parks, where vehicles cannot enter, present many three-dimensional terrains such as stairs and inclined surfaces, causing difficulty in achieving accurate environment recognition and autonomous movement. Furthermore, robots that navigate pedestrian walkways must be smaller and lighter than cars and also have a robust system capable of traversing steps and uneven surfaces and withstanding rainy weather. Currently, robots designed for paved roads are commercially available; however, robots capable of navigating park walkways are still in the research and development stage. Therefore, to accelerate the research and development of outdoor autonomous mobile robots, this study proposes the Navit(oo)n platform, designed for use in outdoor environments. This robot can be manufactured using easily obtainable parts, and all CAD data, circuit design data, and autonomous movement software are provided as open source. This paper introduces an overview of Navit(oo)n that successfully completed the course and achieved all tasks in the recent Nakanoshima Robot Challenge.

TransCNNLoc: End-to-end pixel-level learning for 2D-to-3D pose estimation in dynamic indoor scenes

Accurate localization in GPS-denied environments has always been a core issue in computer vision and robotics research. In indoor environments, vision-based localization methods are susceptible to changes in lighting conditions, viewing angles, and environmental factors, resulting in localization failures or limited generalization capabilities. In this paper, we propose the TransCNNLoc framework, which consists of an encoding–decoding network designed to learn more robust image features for camera pose estimation. In the image feature encoding stage, CNN and Swin Transformer are integrated to construct the image feature encoding module, enabling the network to fully extract global context and local features from images. In the decoding stage, multi-level image features are decoded through cross-layer connections while computing per-pixel feature weight maps. To enhance the framework’s robustness to dynamic objects, a dynamic object recognition network is introduced to optimize the feature weights. Finally, a multi-level iterative optimization from coarse to fine levels is performed to recover six degrees of freedom camera pose. Experiments were conducted on the publicly available 7scenes dataset as well as a dataset collected under changing lighting conditions and dynamic scenes for accuracy validation and analysis. The experimental results demonstrate that the proposed TransCNNLoc framework exhibits superior adaptability to dynamic scenes and lighting changes. In the context of static environments within publicly available datasets, the localization technique introduced in this study attains a maximal precision of up to 5 centimeters, consistently achieving superior outcomes across a majority of the scenarios. Under the conditions of dynamic scenes and fluctuating illumination, this approach demonstrates an enhanced precision capability, reaching up to 3 centimeters. This represents a substantial refinement from the decimeter scale to a centimeter scale in precision, marking a significant advancement over the existing state-of-the-art (SOTA) algorithms. The open-source repository for the method proposed in this paper can be found at the following URL: github.com/Geelooo/TransCNNloc.

Force-based organization and control scheme for the non-prehensile cooperative transportation of objects

AbstractOver decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.

Deep CNN-Based Static Modeling of Soft Robots Utilizing Absolute Nodal Coordinate Formulation.

Soft continuum robots, inspired by the adaptability and agility of natural soft-bodied organisms like octopuses and elephant trunks, present a frontier in robotics research. However, exploiting their full potential necessitates precise modeling and control for specific motion and manipulation tasks. This study introduces an innovative approach using Deep Convolutional Neural Networks (CNN) for the inverse quasi-static modeling of these robots within the Absolute Nodal Coordinate Formulation (ANCF) framework. The ANCF effectively represents the complex non-linear behavior of soft continuum robots, while the CNN-based models are optimized for computational efficiency and precision. This combination is crucial for addressing the complex inverse statics problems associated with ANCF-modeled robots. Extensive numerical experiments were conducted to assess the performance of these Deep CNN-based models, demonstrating their suitability for real-time simulation and control in statics modeling. Additionally, this study includes a detailed cross-validation experiment to identify the most effective model architecture, taking into account factors such as the number of layers, activation functions, and unit configurations. The results highlight the significant benefits of integrating Deep CNN with ANCF models, paving the way for advanced statics modeling in soft continuum robotics.

Opinion: Agricultural Robotics, issues worthy to study

This article presents the author’s opinion on the need to focus on usability and affordability issues in agricultural robotics research. While fundamental studies for robotics are still important, addressing farmers’ concerns on usability and affordability issues will help accelerate the adoption of robots in agriculture. It also offers the author’s view on the challenges and research needs in achieving desired efficiency and usability in agricultural robotics, such as ensuring that robots can perform tasks consistently in different fields and making them operable by farmers with limited training. The author has also emphasized the importance of addressing affordability issues before introducing robotic solutions to farmers.

A Perspective on Cognitive Robot Research and Development

The purpose of this paper is to present my personal journey in the last 30-plus years of research and development efforts on a cognitive robotic system called Intelligent Soft Arm Control (ISAC). In doing so, I will highlight how our team approached solving certain robot intelligence, communication, and control problems.

Artificial Intelligence and Robotics for Young Children: Redeveloping the Five Big Ideas Framework

Purpose To align with the artificial intelligence and robotics (AIR) research and policy agenda, this paper puts forth an adapted five big ideas framework specifically tailored to teaching young children about artificial intelligence (AI) via robotics. Design/Approach/Methods Grounded in early childhood education research, the proposed framework emphasizes the use of robotics and play-based learning to make AI accessible and encourage engagement among young children who have not started formal schooling. Findings We comparatively analyze the commonalities and differences in AI big ideas between the original K-12 framework and the redeveloped early childhood education framework. To pique children's interest, key concepts are presented through interactions with robotics and robot role-play. This paper also provides recommendations for age-appropriate topics, storytelling, and play-based teaching methods. Originality/Value This framework aims to equip researchers and educators with strategies for successfully integrating introductory AI education into early childhood classrooms. Teaching AI in a developmentally responsive manner can help nurture young children's curiosity toward and understanding of an increasingly AI-driven world.

Research progress on lightweight technology of exoskeleton robot

Exoskeleton robots are wearable devices designed to mimic the skeletal mechanisms of insects and humans Enhancing users body functioning. In recent years, exoskeleton robot has become one of the hot spots in military and medical fields. In the process of exoskeleton robot research and development, how to achieve lightweight exoskeleton robot design is the goal of many research and development teams. Lightweight design is of significant importance in the development of exoskeleton robots since it can increase their efficiency and portability. With the improvement of modern processing technology, the lightweight technology of exoskeleton robot is becoming more and more mature. This paper summarizes the mature and promising lightweight technology of exoskeleton robot at present. In this paper, based on existing exoskeleton robots, the lightweight technology of these robots is analysed and reviewed in terms of their drive devices, power supply, structures and materials, and future development trends of the technology are suggested. This paper offers a reference the lightweight design of exoskeleton robot.

Dynamic Modeling and Performance Analysis of a Hip Rehabilitation Robot.

The dynamic performance of a 2-DOF hip joint rehabilitation robot configuration for patients with hip joint dyskinesia was analyzed. There were eight revolute pairs on one side of the hip joint rehabilitation robot configuration. The dynamics of the robot configuration were analyzed with the Newton-Euler method, and a dynamic model was developed. On the basis of the solved dynamic model, the dynamic performance index of the hip joint rehabilitation robot configuration is given, and the performance atlas under different parameters is drawn. The performance of the hip joint rehabilitation robot is theoretically verified. This study provides a theoretical basis for the research and development of exoskeleton rehabilitation robots.

Programming 2DoF Robot Arm Based on Forward Kinematics using Arduino

This project's main goal is to use Arduino to program a 2-degree-of-freedom (2Dof) robot arm based on forward kinematics. The suggested system is made to precisely regulate the robot arm's motion for a variety of activities, such as pickand-place duties and assembly procedures. The robot arm can be precisely positioned and controlled thanks to the forward kinematics model, which determines the end effector's position based on the joint angles. The system is put into practice and put through its paces in a real-world setting, and the results demonstrate its ability to conduct precise movements with decent accuracy. The 2DoF Robot Arm can be utilized to automate complex processes and boost productivity in a variety of industries, including manufacturing, healthcare, and robotics research.

Research status of elderly-care robots and safe human-robot interaction methods

Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.

Computer-controlled ultra high voltage amplifier for dielectric elastomer actuators

Soft robotics is a breakthrough technology to support human–robot interactions. The soft structure of a soft robot can increase safety during human and robot interactions. One of the promising soft actuators for soft robotics is dielectric elastomer actuators (DEAs). DEAs can operate silently and have an excellent energy density. The simple structure of DEAs leads to the easy fabrication of soft actuators. The simplicity combined with silent operation and high energy density make DEAs interesting for soft robotics researchers. DEAs actuation follows the Maxwell-pressure principle. The pressure produced in the DEAs actuation depends much on the voltage applied. Common DEAs requires high voltage to gain an actuation. Since the power consumption of DEAs is in the milli-Watt range, the current needed to operate the DEAs can be neglected. Several commercially available DC-DC converters can convert the volt range to the kV range. In order to get a voltage in the 2–3 kV range, the reliable DC-DC converter can be pricy for each device. This problem hinders the education of soft actuators, especially for a newcomer laboratory that works in soft electric actuators. This paper introduces an entirely do-it-yourself (DIY) Ultrahigh voltage amplifier (UHV-Amp) for education in soft robotics. UHV-Amp can amplify 12 V to at a maximum of 4 kV DC. As a demonstration, we used this UHV-Amp to test a single layer of powdered-based DEAs. The strategy to build this educational type UHV-Amp was utilizing a Cockcroft-Walton circuit structure to amplify the voltage range to the kV range. In its current state, the UHV-Amp has the potential to achieve approximately 4 kV. We created a simple platform to control the UHV-Amp from a personal computer. In near future, we expect this easy control of the UHV-Amp can contribute to the education of soft electric actuators.

An improved single short detection method for smart vision-based water garbage cleaning robot

These days, plastic trash is exponentially overwhelming our waterways. The catastrophe has attracted global attention at this point. As a result, protecting the environment on the water's surface has received increasing focus. Currently, manpower can be used to clean up contaminated water bodies like ponds, rivers, and oceans. Using the current cleaning approach results in low efficiency and hazard. The detection, collection, sorting, and removal of plastic trash from such water surfaces has been the subject of relatively little robotic research, despite the dire circumstances. From private sources, there are very few individual efforts to be found. In order to attain great efficiency without human assistance or operation, a fully autonomous water surface cleaning robot is proposed in this study. The robot was created to adapt to any type of water body found in the real world. An efficient object identification machine learning technique can be suggested for the creation of autonomous cleaning robots. This study improved the Single Short Detection (SSD) method to recognise objects accurately. Because of the enhanced detection techniques, the robot is able to collect trash on its own. With a mean average precision (mAP) of 94.099 % and a detection speed of up to 64.67 frames per second, experimental findings show that the enhanced SSD has exceptional detection speed and accuracy.