Automation

2024

Jump to: 2023, 2022, 2021

14 pages, 4702 KiB

Open AccessArticle

Decision-Making Policy for Autonomous Vehicles on Highways Using Deep Reinforcement Learning (DRL) Method

by Ali Rizehvandi, Shahram Azadi and Arno Eichberger

Automation 2024, 5(4), 564-577; https://doi.org/10.3390/automation5040032 - 8 Nov 2024

Viewed by 539

Abstract

Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. The first step is to [...] Read more.

Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. The first step is to create a highway traffic environment where the agent can be guided safely through surrounding vehicles. A hierarchical control framework is then provided to manage high-level driving decisions and low-level control commands, such as speed and acceleration. Next, a special DRL-based method called deep deterministic policy gradient (DDPG) is used to derive decision strategies for use on the highway. The performance of the DDPG algorithm is compared with that of the DQN and PPO algorithms, and the results are evaluated. The simulation results show that the DDPG algorithm can effectively and safely handle highway traffic tasks. Full article

► Show Figures

Figure 1

23 pages, 22827 KiB

Open AccessArticle

World Modeling for Autonomous Wheel Loaders

by Koji Aoshima, Arvid Fälldin, Eddie Wadbro and Martin Servin

Automation 2024, 5(3), 259-281; https://doi.org/10.3390/automation5030016 - 6 Jul 2024

Cited by 1 | Viewed by 1462

Abstract

This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs [...] Read more.

This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from a 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions. Full article

► Show Figures

Figure 1

22 pages, 2334 KiB

Open AccessArticle

Vision-Based Object Manipulation for Activities of Daily Living Assistance Using Assistive Robot

by Md Tanzil Shahria, Jawhar Ghommam, Raouf Fareh and Mohammad Habibur Rahman

Automation 2024, 5(2), 68-89; https://doi.org/10.3390/automation5020006 - 15 Apr 2024

Cited by 2 | Viewed by 1615

Abstract

The increasing prevalence of upper and lower extremity (ULE) functional deficiencies presents a significant challenge, as it restricts individuals’ ability to perform daily tasks independently. Robotic devices are emerging as assistive devices to assist individuals with limited ULE functionalities in activities of daily [...] Read more.

The increasing prevalence of upper and lower extremity (ULE) functional deficiencies presents a significant challenge, as it restricts individuals’ ability to perform daily tasks independently. Robotic devices are emerging as assistive devices to assist individuals with limited ULE functionalities in activities of daily living (ADLs). While assistive manipulators are available, manual control through traditional methods like joysticks can be cumbersome, particularly for individuals with severe hand impairments and vision limitations. Therefore, autonomous/semi-autonomous control of a robotic assistive device to perform any ADL task is open to research. This study addresses the necessity of fostering independence in ADLs by proposing a creative approach. We present a vision-based control system for a six-degrees-of-freedom (DoF) robotic manipulator designed for semi-autonomous “pick-and-place” tasks, one of the most common activities among ADLs. Our approach involves selecting and training a deep-learning-based object detection model with a dataset of 47 ADL objects, forming the base for a 3D ADL object localization algorithm. The proposed vision-based control system integrates this localization technique to identify and manipulate ADL objects (e.g., apples, oranges, capsicums, and cups) in real time, returning them to specific locations to complete the “pick-and-place” task. Experimental validation involving an xArm6 (six DoF) robot from UFACTORY in diverse settings demonstrates the system’s adaptability and effectiveness, achieving an overall 72.9% success rate in detecting, localizing, and executing ADL tasks. This research contributes to the growing field of autonomous assistive devices, enhancing independence for individuals with functional impairments. Full article

► Show Figures

Figure 1

22 pages, 5448 KiB

Open AccessArticle

Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot

by Khaled H. Mahmoud, G. T. Abdel-Jaber and Abdel-Nasser Sharkawy

Automation 2024, 5(1), 13-34; https://doi.org/10.3390/automation5010002 - 14 Mar 2024

Cited by 2 | Viewed by 1340

Abstract

In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no [...] Read more.

In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links “Link 1, Link 2, and Link 3” was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature. Full article

► Show Figures

Figure 1

2023

Jump to: 2024, 2022, 2021

19 pages, 2098 KiB

Open AccessArticle

Optimisation of Product Recovery Options in End-of-Life Product Disassembly by Robots

by Natalia Hartono, F. Javier Ramírez and Duc Truong Pham

Automation 2023, 4(4), 359-377; https://doi.org/10.3390/automation4040021 - 29 Nov 2023

Cited by 1 | Viewed by 1878

Abstract

In a circular economy, strategies for product recovery, such as reuse, recycling, and remanufacturing, play an important role at the end of a product’s life. A sustainability model was developed to solve the problem of sequence-dependent robotic disassembly line balancing. This research aimed [...] Read more.

In a circular economy, strategies for product recovery, such as reuse, recycling, and remanufacturing, play an important role at the end of a product’s life. A sustainability model was developed to solve the problem of sequence-dependent robotic disassembly line balancing. This research aimed to assess the viability of the model, which was optimised using the Multi-Objective Bees Algorithm in a robotic disassembly setting. Two industrial gear pumps were used as case studies. Four objectives (maximising profit, energy savings, emissions reductions and minimising line imbalance) were set. Several product recovery scenarios were developed to find the best recovery plans for each component. An efficient metaheuristic, the Bees Algorithm, was used to find the best solution. The robotic disassembly plans were generated and assigned to robotic workstations simultaneously. Using the proposed sustainability model on end-of-life industrial gear pumps shows the applicability of the model to real-world problems. The Multi-Objective Bees Algorithm was able to find the best scenario for product recovery by assigning each component to recycling, reuse, remanufacturing, or disposal. The performance of the algorithm is consistent, producing a similar performance for all sustainable strategies. This study addresses issues that arise with product recovery options for end-of-life products and provides optimal solutions through case studies. Full article

► Show Figures

Figure 1

28 pages, 10914 KiB

Open AccessArticle

Task Location to Improve Human–Robot Cooperation: A Condition Number-Based Approach

by Abdel-Nasser Sharkawy

Automation 2023, 4(3), 263-290; https://doi.org/10.3390/automation4030016 - 6 Sep 2023

Viewed by 2260

Abstract

This paper proposes and implements an approach to evaluate human–robot cooperation aimed at achieving high performance. Both the human arm and the manipulator are modeled as a closed kinematic chain. The proposed task performance criterion is based on the condition number of this [...] Read more.

This paper proposes and implements an approach to evaluate human–robot cooperation aimed at achieving high performance. Both the human arm and the manipulator are modeled as a closed kinematic chain. The proposed task performance criterion is based on the condition number of this closed kinematic chain. The robot end-effector is guided by the human operator via an admittance controller to complete a straight-line segment motion, which is the desired task. The best location of the selected task is determined by maximizing the minimum of the condition number along the path. The performance of the proposed approach is evaluated using a criterion related to ergonomics. The experiments are executed with several subjects using a KUKA LWR robot to repeat the specified motion to evaluate the introduced approach. A comparison is presented between the current proposed approach and our previously implemented approach where the task performance criterion was based on the manipulability index of the closed kinematic chain. The results reveal that the condition number-based approach improves the human–robot cooperation in terms of the achieved accuracy, stability, and human comfort, but at the expense of task speed and completion time. On the other hand, the manipulability-index-based approach improves the human–robot cooperation in terms of task speed and human comfort, but at the cost of the achieved accuracy. Full article

► Show Figures

Figure 1

27 pages, 21777 KiB

Open AccessArticle

Trajectory Control in Discrete-Time Nonlinear Coupling Dynamics of a Soft Exo-Digit and a Human Finger Using Input–Output Feedback Linearization

by Umme Kawsar Alam, Kassidy Shedd and Mahdi Haghshenas-Jaryani

Automation 2023, 4(2), 164-190; https://doi.org/10.3390/automation4020011 - 31 May 2023

Cited by 4 | Viewed by 2560

Abstract

This paper presents a quasi-static model-based control algorithm for controlling the motion of a soft robotic exo-digit with three independent actuation joints physically interacting with the human finger. A quasi-static analytical model of physical interaction between the soft exo-digit and a human finger [...] Read more.

This paper presents a quasi-static model-based control algorithm for controlling the motion of a soft robotic exo-digit with three independent actuation joints physically interacting with the human finger. A quasi-static analytical model of physical interaction between the soft exo-digit and a human finger model was developed. Then, the model was presented as a nonlinear discrete-time multiple-input multiple-output (MIMO) state-space representation for the control system design. Input–output feedback linearization was utilized and a control input was designed to linearize the input–output, where the input is the actuation pressure of an individual soft actuator, and the output is the pose of the human fingertip. The asymptotic stability of the nonlinear discrete-time system for trajectory tracking control is discussed. A soft robotic exoskeleton digit (exo-digit) and a 3D-printed human-finger model integrated with IMU sensors were used for the experimental test setup. An Arduino-based electro-pneumatic control hardware was developed to control the actuation pressure of the soft exo-digit. The effectiveness of the controller was examined through simulation studies and experimental testing for following different pose trajectories corresponding to the human finger pose during the activities of daily living. The model-based controller was able to follow the desired trajectories with a very low average root-mean-square error of 2.27 mm in the x-direction, 2.75 mm in the y-direction, and 3.90 degrees in the orientation of the human finger distal link about the z-axis. Full article

► Show Figures

Figure 1

2022

Jump to: 2024, 2023, 2021

14 pages, 7358 KiB

Open AccessArticle

Experimental Examination of Automatic Tether Winding Method Using Kinetic Energy in Tether Space Mobility Device

by Kazuki Nirayama, Shoichiro Takehara, Satoshi Takayama and Yusuke Ito

Automation 2022, 3(3), 364-377; https://doi.org/10.3390/automation3030019 - 19 Jul 2022

Viewed by 2562

Abstract

Tethers (strings and wires) are used in various mechanical systems because they are lightweight and have excellent storability. Examples of such systems include elevators and cranes. In recent years, the use of tethers in special environments, such as outer space, is expected, and [...] Read more.

Tethers (strings and wires) are used in various mechanical systems because they are lightweight and have excellent storability. Examples of such systems include elevators and cranes. In recent years, the use of tethers in special environments, such as outer space, is expected, and various systems have been proposed. In this study, we propose a mobility system using a tether that moves a human by winding a tether attached to a wall. However, the method has a problem whereby the attitude of the human can lack stability during the winding of the tether. We developed the attitude control method of the Tether Space Mobility Device during tether winding while focusing on fluctuations in the rotational kinetic energy of systems. The effectiveness of the control method was shown using numerical simulation. In this paper, the proposed control system is installed in the experimental device for validating the numerical simulation model. Then, we verified the effectiveness of the proposed control method through experiments using an actual system. The experimental results confirm that the angular velocity of the Tether Space Mobility Device converges to 0 deg/s when control is applied. In addition, it was shown that the proposed control method is effective for automatically winding the tether. Full article

► Show Figures

Figure 1

17 pages, 2491 KiB

Open AccessArticle

A Combined Homing Trajectory Optimization Method of the Parafoil System Considering Intricate Constraints

by Weichao He, Jiayan Wen, Jin Tao and Qinglin Sun

Automation 2022, 3(2), 269-285; https://doi.org/10.3390/automation3020014 - 17 May 2022

Cited by 1 | Viewed by 2283

Abstract

In order to achieve an accurate airdrop in the actual environment, the influence of complex interferences, such as wind field and the terrain of the environment, must be taken into account. Aiming at this problem, a combined trajectory planning strategy of a parafoil [...] Read more.

In order to achieve an accurate airdrop in the actual environment, the influence of complex interferences, such as wind field and the terrain of the environment, must be taken into account. Aiming at this problem, a combined trajectory planning strategy of a parafoil system subjected to intricate conditions is proposed in this paper. This method divides the parafoil airdrop area into an obstacle area and a landing area, then, considering the terrain environment surface, a model for the parafoil system in the wind field is built in the obstacle area. The Gauss pseudo-spectral method is used to transform the complex terrain environment constraint into a series of nonlinear optimal control problems with complex constraints. Finally, the trajectory of the landing area is designed by means of multiphase homing, and the target parameters are solved by the improved marine predator algorithm. The simulation results show that the proposed method has better realizability than a single homing strategy, and the optimization results of the improved marine predator algorithm have higher accuracy. Full article

► Show Figures

Figure 1

11 pages, 1662 KiB

Open AccessArticle

Inspection Application in an Industrial Environment with Collaborative Robots

by Paulo Magalhaes and Nuno Ferreira

Automation 2022, 3(2), 258-268; https://doi.org/10.3390/automation3020013 - 3 Apr 2022

Cited by 5 | Viewed by 3914

Abstract

In this study, we analyze the potential of collaborative robotics in automated quality inspections in the industry. The development of a solution integrating an industrial vision system allowed evaluating the performance of collaborative robots in a real case. The use of these tools [...] Read more.

In this study, we analyze the potential of collaborative robotics in automated quality inspections in the industry. The development of a solution integrating an industrial vision system allowed evaluating the performance of collaborative robots in a real case. The use of these tools allows reducing quality defects as well as costs in the manufacturing process. In this system, image processing methods use resources based on depth and surface measurements with high precision. The system fully processes a panel, observing the state of the surface to detect any potential defect in the panels produced to increase the quality of production. Full article

► Show Figures

Figure 1

19 pages, 5312 KiB

Open AccessArticle

Reinforcement Learning for Collaborative Robots Pick-and-Place Applications: A Case Study

by Natanael Magno Gomes, Felipe Nascimento Martins, José Lima and Heinrich Wörtche

Automation 2022, 3(1), 223-241; https://doi.org/10.3390/automation3010011 - 21 Mar 2022

Cited by 16 | Viewed by 8516

Abstract

The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature [...] Read more.

The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an

ϵ

-greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet. Full article

► Show Figures

Figure 1

16 pages, 8120 KiB

Open AccessArticle

Predictive Performance of Mobile Vis–NIR Spectroscopy for Mapping Key Fertility Attributes in Tropical Soils through Local Models Using PLS and ANN

by Mateus Tonini Eitelwein, Tiago Rodrigues Tavares, José Paulo Molin, Rodrigo Gonçalves Trevisan, Rafael Vieira de Sousa and José Alexandre Melo Demattê

Automation 2022, 3(1), 116-131; https://doi.org/10.3390/automation3010006 - 3 Feb 2022

Cited by 3 | Viewed by 3907

Abstract

Mapping soil fertility attributes at fine spatial resolution is crucial for site-specific management in precision agriculture. This paper evaluated the performance of mobile measurements using visible and near-infrared spectroscopy (vis–NIR) to predict and map key fertility attributes in tropical soils through local data [...] Read more.

Mapping soil fertility attributes at fine spatial resolution is crucial for site-specific management in precision agriculture. This paper evaluated the performance of mobile measurements using visible and near-infrared spectroscopy (vis–NIR) to predict and map key fertility attributes in tropical soils through local data modeling with partial least squares regression (PLS) and artificial neural network (ANN). Models were calibrated and tested in a calibration area (18-ha) with high spatial variability of soil attributes and then extrapolated in the entire field (138-ha). The models calibrated with ANN obtained superior performance for all attributes. Although ANN models obtained satisfactory predictions in the calibration area (ratio of performance to interquartile range (RPIQ) ≥ 1.7) for clay, organic matter (OM), cation exchange capacity (CEC), base saturation (V), and exchangeable (ex-) Ca, it was not repeated for some of them when extrapolated into the entire field. In conclusion, robust mappings (RPIQ = 2.49) were obtained for clay and OM, indicating that these attributes can be successfully mapped on tropical soils using mobile vis–NIR spectroscopy and local calibrations using ANN. This study highlights the need to implement an independent test to assess reliability and extrapolability of previously calibrated models, even when extrapolating the models to neighboring areas. Full article

► Show Figures

Figure 1

2021

Jump to: 2024, 2023, 2022

14 pages, 18303 KiB

Open AccessCommunication

Design and Implementation of a Robotic Arm Assistant with Voice Interaction Using Machine Vision

by George Nantzios, Nikolaos Baras and Minas Dasygenis

Automation 2021, 2(4), 238-251; https://doi.org/10.3390/automation2040015 - 31 Oct 2021

Cited by 4 | Viewed by 8784

Abstract

It is evident that the technological growth of the last few decades has signaled the development of several application domains. One application domain that has expanded massively in recent years is robotics. The usage and spread of robotic systems in commercial and non-commercial [...] Read more.

It is evident that the technological growth of the last few decades has signaled the development of several application domains. One application domain that has expanded massively in recent years is robotics. The usage and spread of robotic systems in commercial and non-commercial environments resulted in increased productivity, efficiency, and higher quality of life. Many researchers have developed systems that improve many aspects of people’s lives, based on robotics. Most of the engineers use high-cost robotic arms, which are usually out of the reach of typical consumers. We fill this gap by presenting a low-cost and high-accuracy project to be used as a robotic assistant for every consumer. Our project aims to further improve people’s quality of life, and more specifically people with physical and mobility impairments. The robotic system is based on the Niryo-One robotic arm, equipped with a USB (Universal Serial Bus) HD (High Definition) camera on the end-effector. To achieve high accuracy, we modified the YOLO algorithm by adding novel features and additional computations to be used in the kinematic model. We evaluated the proposed system by conducting experiments using PhD students of our laboratory and demonstrated its effectiveness. The experimental results indicate that the robotic arm can detect and deliver the requested object in a timely manner with a 96.66% accuracy. Full article

► Show Figures

Figure 1

15 pages, 3496 KiB

Open AccessArticle

Design of Tendon-Actuated Robotic Glove Integrated with Optical Fiber Force Myography Sensor

by Antonio Ribas Neto, Julio Fajardo, Willian Hideak Arita da Silva, Matheus Kaue Gomes, Maria Claudia Ferrari de Castro, Eric Fujiwara and Eric Rohmer

Automation 2021, 2(3), 187-201; https://doi.org/10.3390/automation2030012 - 3 Sep 2021

Cited by 11 | Viewed by 5507

Abstract

People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems [...] Read more.

People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation. Full article

► Show Figures

Figure 1

12 pages, 9007 KiB

Open AccessArticle

UAV Thrust Model Identification Using Spectrogram Analysis

by Igor Henrique Beloti Pizetta, Alexandre Santos Brandão and Mário Sarcinelli-Filho

Automation 2021, 2(3), 141-152; https://doi.org/10.3390/automation2030009 - 1 Aug 2021

Cited by 3 | Viewed by 3624

Abstract

This paper deals with a non-contact method to identify the aerodynamic propeller constants of the Parrot AR.Drone quadrotor. The experimental setup consists of a microphone installed in the flight arena to record audio data. In terms of methodology, a spectrogram analysis is adopted [...] Read more.

This paper deals with a non-contact method to identify the aerodynamic propeller constants of the Parrot AR.Drone quadrotor. The experimental setup consists of a microphone installed in the flight arena to record audio data. In terms of methodology, a spectrogram analysis is adopted to estimate the propeller velocity based on the filtered sound signal. It is known that, in a hovering maneuver, when the UAV mass increases, the propellers rotate faster to produce the necessary thrust increment. In this work, the rotorcraft takes off with its factory settings, first with no hull, corresponding to a mass of 413 g, and after with a small hull, corresponding to a mass of 444 g, and a bigger hull, corresponding to a mass of 462 g. In the sequence, the velocity of the propellers are estimated for each of these three cases using spectrograms of audio recorded by a microphone, corresponding to the sound generated by the four rotors. Finally, the estimated velocity is used to identify the aerodynamic parameters, thus validating the proposal. Full article

► Show Figures

Figure 1

14 pages, 1593 KiB

Open AccessArticle

Adaptive 3D Visual Servoing of a Scara Robot Manipulator with Unknown Dynamic and Vision System Parameters

by Jorge Antonio Sarapura, Flavio Roberti and Ricardo Carelli

Automation 2021, 2(3), 127-140; https://doi.org/10.3390/automation2030008 - 27 Jul 2021

Cited by 5 | Viewed by 4589

Abstract

In the present work, we develop an adaptive dynamic controller based on monocular vision for the tracking of objects with a three-degrees of freedom (DOF) Scara robot manipulator. The main characteristic of the proposed control scheme is that it considers the robot dynamics, [...] Read more.

In the present work, we develop an adaptive dynamic controller based on monocular vision for the tracking of objects with a three-degrees of freedom (DOF) Scara robot manipulator. The main characteristic of the proposed control scheme is that it considers the robot dynamics, the depth of the moving object, and the mounting of the fixed camera to be unknown. The design of the control algorithm is based on an adaptive kinematic visual servo controller whose objective is the tracking of moving objects even with uncertainties in the parameters of the camera and its mounting. The design also includes a dynamic controller in cascade with the former one whose objective is to compensate the dynamics of the manipulator by generating the final control actions to the robot even with uncertainties in the parameters of its dynamic model. Using Lyapunov’s theory, we analyze the two proposed adaptive controllers for stability properties, and, through simulations, the performance of the complete control scheme is shown. Full article

► Show Figures

Figure 1

11 pages, 320 KiB

Open AccessCommunication

Improving Automatic Warehouse Throughput by Optimizing Task Allocation and Validating the Algorithm in a Developed Simulation Tool

by Nikolaos Baras, Antonios Chatzisavvas, Dimitris Ziouzios and Minas Dasygenis

Automation 2021, 2(3), 116-126; https://doi.org/10.3390/automation2030007 - 20 Jul 2021

Cited by 9 | Viewed by 4425

Abstract

It is evident that over the last years, the usage of robotics in warehouses has been rapidly increasing. The usage of robot vehicles in storage facilities has resulted in increased efficiency and improved productivity levels. The robots, however, are only as efficient as [...] Read more.

It is evident that over the last years, the usage of robotics in warehouses has been rapidly increasing. The usage of robot vehicles in storage facilities has resulted in increased efficiency and improved productivity levels. The robots, however, are only as efficient as the algorithms that govern them. Many researchers have attempted to improve the efficiency of industrial robots by improving on the internal routing of a warehouse, or by finding the best locations for charging power stations. Because of the popularity of the problem, many research works can be found in the literature regarding warehouse routing. The majority of these algorithms found in the literature, however, are statically designed and cannot handle multi-robot situations, especially when robots have different characteristics. The proposed algorithm of this paper attempts to give the following solution to this issue: utilizing more than one robot simultaneously to allocate tasks and tailor the navigation path of each robot based on its characteristics, such as its speed, type and current location within the warehouse so as to minimize the task delivery timing. Moreover, the algorithm finds the optimal location for the placement of power stations. We evaluated the proposed methodology in a synthetic realistic environment and demonstrated that the algorithm is capable of finding an improved solution within a realistic time frame. Full article

► Show Figures

Figure 1

18 pages, 3873 KiB

Open AccessArticle

Design, Modeling, and Control of a Differential Drive Rimless Wheel That Can Move Straight and Turn

by Sebastian Sanchez and Pranav A. Bhounsule

Automation 2021, 2(3), 98-115; https://doi.org/10.3390/automation2030006 - 19 Jul 2021

Cited by 9 | Viewed by 5557

Abstract

A rimless wheel or a wheel without a rim, is the simplest example of a legged robot and is an ideal testbed to understand the mechanics of locomotion. This paper presents the design, modeling, and control of a differential drive rimless wheel robot [...] Read more.

A rimless wheel or a wheel without a rim, is the simplest example of a legged robot and is an ideal testbed to understand the mechanics of locomotion. This paper presents the design, modeling, and control of a differential drive rimless wheel robot that achieves straight-line movement and turning. The robot design comprises a central axis with two 10-spoked springy rimless wheels on either side and a central body that houses the electronics, motors, transmission, computers, and batteries. To move straight, both motors are commanded to constant pitch control of the central body. To turn while maintaining constant pitch, a differential current is added and subtracted from currents on either motor. In separate tests, the robot achieved the maximum speed of 4.3 m per sec (9.66 miles per hour), the lowest total cost of transport (power per unit weight per unit velocity) of 0.13, and a smallest turning radius of 0.5 m. A kinematics-based model for steering and a dynamics-based sagittal (fore-aft) plane model for forward movement is presented. Finally, parameters studies that influence the speed, torque, power, and energetics of locomotion are performed. A rimless wheel that can move straight and turn can potentially be used to navigate in constrained spaces such as homes and offices. Full article

► Show Figures

Figure 1

14 pages, 892 KiB

Open AccessCommunication

Modelling Software Architecture for Visual Simultaneous Localization and Mapping

by Bhavyansh Mishra, Robert Griffin and Hakki Erhan Sevil

Automation 2021, 2(2), 48-61; https://doi.org/10.3390/automation2020003 - 2 Apr 2021

Cited by 1 | Viewed by 4922

Abstract

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There [...] Read more.

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Smart Robotics for Automation

Share This Topical Collection

Editor

Topical Collection Information

Keywords

Published Papers (19 papers)

2024

Jump to: 2023, 2022, 2021

2023

Jump to: 2024, 2022, 2021

2022

Jump to: 2024, 2023, 2021

2021

Jump to: 2024, 2023, 2022

Further Information

Guidelines

MDPI Initiatives

Follow MDPI