Position Of Robot Research Articles

Optimising Robot Swarm Formations by Using Surrogate Models and Simulations

Optimising a swarm of many robots can be computationally demanding, especially when accurate simulations are required to evaluate the proposed robot configurations. Consequentially, the size of the instances and swarms must be limited, reducing the number of problems that can be addressed. In this article, we study the viability of using surrogate models based on Gaussian processes and artificial neural networks as predictors of the robots’ behaviour when arranged in formations surrounding a central point of interest. We have trained the surrogate models and tested them in terms of accuracy and execution time on five different case studies comprising three, five, ten, fifteen, and thirty robots. Then, the best performing predictors combined with ARGoS simulations have been used to obtain optimal configurations for the robot swarm by using our proposed hybrid evolutionary algorithm, based on a genetic algorithm and a local search. Finally, the best swarm configurations obtained have been tested on a number of unseen scenarios comprising different initial robot positions to evaluate the robustness and stability of the achieved robot formations. The best performing predictors exhibited speed increases of up to 3604 with respect to the ARGoS simulations. The optimisation algorithm converged in 91% of runs and stable robot formations were achieved in 79% of the unseen testing scenarios.

Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy

This paper addresses the problem of building an occupancy grid map of an unknown environment using a swarm comprising resource-constrained robots, i.e., robots with limited exteroceptive and inter-robot sensing capabilities. Past approaches have, commonly, used random-motion models to disperse the swarm and explore the environment randomly, which do not necessarily consider prior information already contained in the map. Herein, we present a collaborative, effective exploration strategy that directs the swarm toward ‘promising’ frontiers by dividing the swarm into two teams: landmark robots and mapper robots, respectively. The former direct the latter, toward promising frontiers, to collect proximity measurements to be incorporated into the map. The positions of the landmark robots are optimized to maximize new information added to the map while also adhering to connectivity constraints. The proposed strategy is novel as it decouples the problem of directing the resource-constrained swarm from the problem of mapping to build an occupancy grid map. The performance of the proposed strategy was validated through extensive simulated experiments.

Localization on a-priori information of plane extraction.

Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall's surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.

Creepage Distance Estimation of Hairpin Stators Using 3D Feature Extraction

The increasing demand for electric drives challenges conventional powertrain designs and requires new technologies to increase production efficiency. Hairpin stator manufacturing technology enables full automation, and quality control within the process is particularly important for increasing the process capacity, avoiding rejects and for safety-related aspects. Due to the complex, free-form geometries of hairpin stators and the required short inspection times, inline reconstruction and accurate quantification of relevant features is of particular importance. In this study, we propose a novel method to estimate the creepage distance, a feature that is crucial regarding the safety standards of hairpin stators and that could be determined neither automatically nor accurately until now. The data acquisition is based on fringe projection profilometry and a robot positioning system for a highly complete surface reconstruction. After alignment, the wire pairs are density-based clustered so that computations can be parallelized for each cluster, and an analysis of partial geometries is enabled. In several further steps, stripping edges are segmented automatically using a novel approach of spatially asymmetric windowed local surface normal variation, and the creepage distances are subsequently estimated using a geodesic path algorithm. Finally, the approach is examined and discussed for an entire stator, and a methodology is presented that enables the identification of implausible estimated creepage distances.

TMG: A topology-based motion generalization method with spatial relationship preservation

As an important requirement, human-friendly motion in human–robot interaction (HRI) is increasingly attracting attention. In many scenarios, the ability to generalize a similar motion from demonstration is essential for a robot, and the similarity is generally captured by the spatial relationship between the different joints of the robot. Though a lot of investigation for motion generalization has been conducted and good progress achieved, they share limitations in leaving the relationship out of consideration and being difficult to apply the generalization between different robots. In this paper, we propose a novel topology-based motion generalization (TMG) method that abstracts the motion generalization problem to a mesh deformation optimization, and the spatial relationship between different parts of the robot is captured with a topology-based representation. Instead of only taking into account a single joint position, the relationship semantic with Laplacian coordinates is modeled, and the motion generalization from demonstration to reproduction is realized by preserving the semantic as a Laplacian deformation, and even the robot or target position is changed. Furthermore, motion generalization between single or multiple different robots can be achieved with spatial relationship preservation and transfer. Our experimental results show that the reproduction based on topology-based representation outperforms the mapping methods by training with end-effector pose or joint angles, and ensures robust motion with spatial relationship preservation.

Prescribing Cartesian Stiffness of Soft Robots by Co-Optimization of Shape and Segment-Level Stiffness.

Soft robots aim to revolutionize how robotic systems interact with the environment thanks to their inherent compliance. Some of these systems are even able to modulate their physical softness. However, simply equipping a robot with softness will not generate intelligent behaviors. Indeed, most interaction tasks require careful specification of the compliance at the interaction point; some directions must be soft and others firm (e.g., while drawing, entering a hole, tracing a surface, assembling components). On the contrary, without careful planning, the preferential directions of deformation of a soft robot are not aligned with the task. With this work, we propose a strategy to prescribe variations of the physical stiffness and the robot's posture so to implement a desired Cartesian stiffness and location of the contact point. We validate the algorithm in simulation and with experiments. To perform the latter, we also present a new tendon-driven soft manipulator, equipped with variable-stiffness segments and proprioceptive sensing and capable to move in three dimensional. We show that, combining the intelligent hardware with the proposed algorithm, we can obtain the desired stiffness at the end-effector over the workspace.

Robot positioning and navigation technology is based on Integration of the Global Navigation Satellite System and real-time kinematics

This paper provides a new robot positioning and navigation technique that combines GNSS-RTK and visual SLAM to address the shortcomings of existing single robot positioning and navigation techniques, such as limited adaptation to scenarios and susceptibility to environmental influences. Real-time kinematics (RTK) is a real-time dynamic observation technique based on carrier phase observations, which can provide real-time 3D positioning results of the measurement site in a specified coordinate system, and can be positioned with centimetre-level accuracy, which is suitable for It is suitable for outdoor positioning of mobile robots. In this paper, a dataset is studied to compare and analyse the advantages and disadvantages of different SLAMs in order to determine the most suitable robot positioning and navigation technique for autonomous indoor and outdoor mobility. These sensors were well calibrated and the composition was applied to the RK3588. The results show good performance in indoor-outdoor switching scenarios, obtaining a mapping technique that cannot be achieved with a single visual SLAM, effectively improving the ability to adapt to multiple scenarios and enhancing the operational range of the transportable robot.

Novel cartographer using an OAK-D smart camera for indoor robots location and navigation

In recent years, service robots have been widely used in people’s daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cloud matching. Then a software system is realized by integrating each module. The experiment proves that the system is practical and effective, and has practical value.

Design and Research of Robot Image Perception System Based on Artificial Intelligence

In recent years, with the rapid development of science and technology, the performance and functions of traditional robots have gradually failed to meet people’s needs for production and life, and the development of robots has gradually become intelligent and networked. Among them, as the core technology of mobile robot positioning and navigation, environment perception technology has always been the focus and hot research issue in the field of robotics. This paper mainly studies the design of a robot image perception system based on artificial intelligence. In this paper, a tetrahedral robot image-sensing system based on a panoramic camera is proposed and designed, and a U-net convolutional image semantic segmentation network is designed.

Robotic Positioning for Quality Assurance of Feature-Sparse Components Using a Depth-Sensing Camera

In this paper, a novel method for crawler positioning is presented utilizing an onboard depth-sensing camera which can operate in semi-structured, self-similar environments, using only measurements of the sample under inspection to navigate. Non-destructive evaluation at manufacture is a vital aspect of assuring the fitness for purpose of high-value marine assets, moreover, it is almost always a regulatory requirement to ensure build quality standards have been met. Traditionally these inspections were deployed manually by a trained operator in a laborious and time-consuming manner. More recently, robotic crawler-based solutions have become available on the marketplace, however, these solutions are limited in their capabilities and still require significant manual intervention and set-up for each application. Additionally, GPS or prior knowledge of their surroundings which are critical to their operation are often unavailable in an active work environment. An autonomous, self-localizing system would provide significant benefits in these situations, but certain challenges arise from limited situational awareness and poor positional accuracy. The accuracy and robustness of the novel method were assessed and experimentally validated through ground truth readings from a Vicon motion capture system. The localization algorithm’s ability to function on different materials and under various lighting conditions was also explored. Using the example of the receipt inspection of steel plate under 240 lux lighting, the system proved capable of positioning the crawler at the desired position within 5.7 mm.

Research on the Measurement Method of Position Error of Series Robot Based on Laser Tracking Apparatus

With today’s industrial upgrading in the world, cutting-edge technologies such as induction measurement technology, bionic technology, virtual reality technology, etc., are gradually combined with the development and design of series robots. Governments of various countries have served the social and economic development, implemented not only innovation-driven but also vigorously promoted the development of the series robots industry. At this time, it is particularly important to provide metering, standards and verification support for cutting-edge technologies and disruptive technologies to meet the new needs of metering put forward by the industrial and technological revolution. In this paper, by trying to detect the accuracy and repeatability of position with a non-assembling detection method, the needs of robot position detection can be met. At the same time, it also further optimizes the detection link and reduces enterprise and detection costs. The end-setting of the detection method has strong versatility and can be applied to industrial robot systems with no loading or various actuators at the end.

Kinematic modeling and motion characterization of an articulated mobile robot

In response to the outstanding problems of poor motion flexibility and poor terrain adaptability of conventional mobile robots in unstructured environments, a multi-vehicle modular articulated mobile robot capable of adapting to complex and variable obstacle terrain was developed by designing and introducing a flexible articulation device. By constructing a kinematic model of the single body module of the robot, integrating the motion constraint relations among the modules, and establishing the overall robot attitude equations, we propose a robot soft motion control method. Based on the analysis of robot motion characteristics under special obstacle terrain, the relationship model between obstacle parameters and robot pose is constructed, and the correction strategy under unilateral unstable motion state is proposed through the analysis of robot motion state under unilateral unstable condition. Theoretical analysis and experimental results show that the robot has good motion flexibility and complex terrain adaptability.

Multi-Planar Jump Performance in Speed Skating Athletes: Investigating Interlimb Differences in an Asymmetrical Sport

Elite speed skaters are exposed to asymmetric lower limb loading consequent to the unidirectional turns inherent to the sport. This presents a unique model to study the effects of sport-specific loading on interlimb differences in mechanical muscle function. This study, therefore, examined baseline interlimb asymmetries in multi-directional jump tests in elite speed skaters using a cross-sectional design. Thereafter, participants were monitored longitudinally using the bilateral countermovement jump (CMJ) to quantify interlimb differences in mechanical muscle function throughout a competitive season. Pre-season baseline testing included a single leg lateral jump (JumpLat) and a single leg forward horizontal jump (JumpHorz) attached to a robotic linear position encoder, along with a bilateral CMJ on a dual force plate system. From baseline, CMJ monitoring was conducted throughout the 24-week competitive season. Within-limb changes in right vs left CMJ concentric impulse (CMJCon) and eccentric deceleration impulse (CMJEcc) were assessed using a linear mixed effects model. No systematic interlimb differences were found at baseline (p = 0.33–0.98) and the between-test agreement in limb dominance was poor (Kappa = −0.17–0.33). Furthermore, there were no time effects observed for interlimb differences in CMJCon (fixed effect = 0.01 N*s) and a small decrease in CMJEcc (fixed effects = −0.35 N*s, p = 0.01). These data suggest that even in a sport with asymmetrical loading, interlimb differences in mechanical output remain stable at the group level. However, changes occurring at the individual athlete level may be occurring that are meaningful for performance and injury.

Inverse Kinematic Algorithm with Newton-Raphson Method iteration to Control Robot Position and Orientation based on R programming language

The homogeneous transform program is a function used to calculate the homogeneous transformation matrix at a specific position and orientation of a three-link manipulator. The homogeneous transformation matrix is a 4x4 matrix used to represent the position and orientation of an object in three-dimensional space. In the program, the rotation matrix R is calculated using the Euler formula and stored in a 4x4 matrix along with the position coordinates. The Jacobian matrix function calculates the Jacobian matrix at a specific position and orientation of a three-link manipulator using the homogeneous transformation matrix. The Euler formula used in the program is based on the rotation matrices for rotations around the x, y, and z-axes. The output of these functions can be useful for future research in developing advanced manipulators with improved accuracy and flexibility. Research gaps in exploring the limitations of these functions in real-world applications, particularly in scenarios involving complex manipulator configurations and environmental factors.

Design of a decentralized Internet-based control system for industrial robot robotic arms

Abstract To meet the needs of small and medium-sized processing plants by establishing a software and hardware platform for a robotic arm control system with a hierarchical structure and designing a robot robotic arm motion control system that is adaptable to multiple environments and cost-effective. In this paper, the industrial robot robotic arm motion control system is designed based on the consistent fusion tracking algorithm under the decentralized Internet. While using the VisualC++ development environment for writing human-machine interface programs using the MFC framework, the device controls the robotic arm body to complete predetermined movements or operational tasks based on command information, sensing information, and the robot controller. The AMS1086CD-3.3 low-voltage differential linear regulator chip used in this design effectively implements the circuit design from 4.8V to 3.4V and successfully meets the 3.4V voltage supply required by the controller. The industrial robot robotic arm control system adopts an adaptive backstepping algorithm controller, and the error of the tracking system at 0 seconds in the simulation results is always kept within the range of [-1~1] that meets the requirements, which effectively reduces the control error of the robotic arm position. Therefore, the robotic arm control studied in this paper is composed of a complete control system in hardware and software, and the designed adaptive backstepping algorithm controller achieves the demand for good control performance of the robotic arm proper.

Research on dynamic path planning method of electric inspection robot based on fuzzy neural network

In order to better address the issue of the coverage rate about power inspection robot to the working area, solving the difficulties in the navigation modeling and path planning of the intelligent control program is the focus of this paper. This paper first analyzes a multi-information fusion SLAM robot positioning and navigation algorithm, then uses the Kalman filter technology to locate through Voronoi mapping. Second, this paper proposes a five-layer neural network approach to how robots can plan and control routes in high-density scenarios. Third, the robot takes the surrounding obstacle distance, the target angle and the target distance as input parameters then the output parameters of the fuzzy neural network are the wheel speed and the steering angle. The inspection robot uses the sensors on the body to detect the surrounding environment’s parameter information. The dynamic path planning algorithm changes the path accordingly, prevents collision, and finally reaches the preset target. Finally, the paper collects the features obtained by Voronoi mapping in a large-scale simulation environment. The fuzzy neural network algorithm for detecting robots is validated in a complex environment, proving the effectiveness and accuracy of the algorithm.

Position and Speed Control of the Tractor-Trailer Robot by Considering the Dynamics of the Tractor Wheels Using the PID Controller

Position and Speed Control of the Tractor-Trailer Robot by Considering the Dynamics of the Tractor Wheels Using the PID Controller

Intelligent Control System Design of Ring Light via IOT Mobile Robot

In the Internet of Things (IoT) era, the combination of mobile robots and IoT technology has realized the complementary advantages of the IoT and robots, which has brought new challenges and opportunities for the development and application of the IoT and mobile robots. In this study, a mobile robot positioning system based on the wireless positioning technology of the IoT is first designed, and an adaptive federated filtering algorithm based on the confidence of the sensor measurement data is given, and the data fusion processing is carried out for the positioning system to achieve the accurate positioning of the mobile robot. Then, we embed the intelligent control system of the ring lamp and propose an adaptive fuzzy PI double closed loop equalization control strategy, which can automatically identify the number of interlaced parallel and ensure its equalization effect. The experimental results show that the equalization strategy designed in this study has a good equalization effect in the loop interleaving parallel topology, strong adaptive parameter adjustment ability, good robustness, and anti-interference.

Development of Autonomous Moving Robot Using Appropriate Technology for Tsukuba Challenge

We have been participating in the Tsukuba Challenge, an open experiment involving autonomous robots, since 2014. The technology of our robot has stabilized, and our robot has continued to win the Tsukuba Mayor Prize from 2018 to 2021 without changing the basic configuration of the body and navigation software. Here, we report the robot’s structure as the project’s current completed form. Our robot is designed with the policy of selecting the most rational technology (appropriate technology) to achieve the purpose, even if it is not the latest. For example, we used image-like two-dimensional data instead of a three-dimensional point cloud in map matching for robot positioning. For pedestrian signal recognition, which was required to perform an optional task, we did not use deep learning but rather conventional color image processing. These techniques are advantageous for balancing the execution time and accuracy required in the challenge.

Modeling of swimming posture dynamics for a beaver-like robot

A bionic underwater robot swims to generate its propulsion in water, which in turn directly determines its movement stability. Exploring the relationship between the swimming force and the posture is important. This study aims to analyze and model the swimming posture dynamics of a beaver-like robot. The posture dynamics is decomposed into three parts: leg dynamics, body hydrodynamics, and body posture dynamics. First, the leg dynamic model of the beaver-like robot is established using the rigid-fluid integration method. Then, the overall fluid dynamics of the robot is modeled via numerical calculation methods to obtain the forces of the water on the robot body during swimming. Lastly, the swimming posture dynamic model of the robot is constructed to describe the relationship between the leg movement and body posture. The swimming process of the beaver-like robot with bionic alternating and synchronous trajectory is simulated with ADAMS 2019. The proposed modeling method and the swimming posture dynamic model are verified by comparing the simulation and theoretical calculation results of robot posture, which could be used for the swimming posture control of a bionic underwater robot.