Robotic Control Research Articles

Supporting AI Planning and Collaboration for Robotic Applications Using TAEMS

This paper describes a general approach to integrating higher-level reasoning mechanisms including planning and scheduling methods with lower-level robotic control processes. We adopt a domain-independent task representation language TAEMS to describe the knowledge of tasks, resources, and their interrelationships. This TAEMS representation language serves as the input of the reasoning functions, which generate a schedule of executable methods to be executed by the robot in the physical world. In the execution process of this goal-directed plan, the robot also needs to attend to basic functions. The potential interactions between the plan and these basic functions would lead to interesting challenges that will be discussed. An integrated development platform with a simulator that supports real-world physics is also presented.

A Semi-Autonomous Hierarchical Control Framework for Prosthetic Hands Inspired by Dual Streams of Human.

The routine use of prosthetic hands significantly enhances amputees' daily lives, yet it often introduces cognitive load and reduces reaction speed. To address this issue, we introduce a wearable semi-autonomous hierarchical control framework tailored for amputees. Drawing inspiration from the visual processing stream in humans, a fully autonomous bionic controller is integrated into the prosthetic hand control system to offload cognitive burden, complemented by a Human-in-the-Loop (HIL) control method. In the ventral-stream phase, the controller integrates multi-modal information from the user's hand-eye coordination and biological instincts to analyze the user's movement intention and manipulate primitive switches in the variable domain of view. Transitioning to the dorsal-stream phase, precise force control is attained through the HIL control strategy, combining feedback from the prosthetic hand's sensors and the user's electromyographic (EMG) signals. The effectiveness of the proposed interface is demonstrated by the experimental results. Our approach presents a more effective method of interaction between a robotic control system and the human.

Application of Fuzzy Logic for Robotic Arm Joint Control of an Explosive Ordnance Disposal Unit

Precise luffing of payload in mobile cranes is a crucial part of material handling and safety in industrial operations. Explosive ordnance disposal uses a bomb disposal robot to safely disable explosive devices at a safe distance. This robot is a mobile jib crane type with a gripper as an end effector instead of the typical suspension cable with a hook. The common challenge of this crane type is the arm/jib movement sensitivity with respect to tip-over stability of the crane body. This is directly influenced by the payload and constrained by the degree of freedom. This paper presents a control strategy of the joint for luffing such that the back wheels remain in contact with the surface ground and avoid bucking at any given instance of weight change in the payload. Fuzzy logic was applied to control the motor torque and luffing angle of the arm in response to the load and gripper opening size to maintain the tip-over stability margin at the highest value possible. The response curves at different configurations of the two input signals were also analyzed based on the rules set to determine its precision with respect to the expected response curve.

On the Stability of a 3DOF Vibrating System Close to Resonances

Abstract Purpose In the current work, the motion of a three degrees-of-freedom (DOF) dynamical system as a vibrating model is examined. The proposed system is of high importance in vibration engineering applications, such as the analysis of the control of flexible arm robotics, flexible arm vibrational motion as a dynamic system, pump compressors, transportation devices, rotor dynamics, shipboard cranes, and human or walking analysis robotics. Methods Lagrange's equations (LE) are used to derive the equations of motion of the controlling system. The analytic solutions (AS) are obtained utilizing the multiple-scales method (MSM) up to the third order. Results The framework for removing secular terms provides the requirements for the solvability of this problem. Various resonance scenarios are categorized and the modulation equations (ME) are constructed. To graphically demonstrate the beneficial impacts of the distinct parameters of the problem, the time histories (TH) of the approximate solutions as well as the resonance curves (RC) are depicted. The Runge-Kutta algorithm (RKA) is employed to obtain the numerical solutions (NS) of the regulating system. Conclusion A comparison of the AS and NS reveals the accuracy of the perturbation approach. The stability/instability zones are studied using Routh-Hurwitz criteria (RHC), and then they are examined using a steady-state situation. Basically, the used perturbation method is considered a traditional method that is applied to solve a new dynamical system. Then, the achieved results are considered new because they weren’t obtained previously, which indicates the novelty of this work.

Effectiveness of Robotic Intervention on Improving Social Development and Participation of Children with Autism Spectrum Disorder - A Randomised Controlled Trial.

Evidence-based robotic intervention programmes for children with autism spectrum disorder (ASD) have been limited. As yet, there is insufficient evidence to inform therapists, teachers, and service providers on effectiveness of robotic intervention to enhance social development and participation of children with ASD in a real context. This study used a randomised controlled trial to test the efficacy of robotic intervention programmes in enhancing the social development and participation of children with ASD. 60 children with ASD were included. The participants were randomly assigned to the following groups: (1) robotic intervention programme (n = 20), (2) human-instructed programme (n = 20), and (3) control group (n = 20). Both the performance-based behavioural change in social communication and parent-reported change in social responsiveness were evaluated. The participants in the robotic intervention group demonstrated statistically significant changes in both the performance-based assessment and parent-reported change in social participation. Significant differences were found in the communication and reciprocal social interactions scores between the experimental group and the control and comparison groups in the performance-based assessment (p < 0.01). The effectiveness of robotic intervention programme to enhance the social communication and participation was confirmed. Future studies may also consider adding a maintenance phase to document how the effects of the intervention carry over to the participants over a longer period. (Clinical trial number: NCT04879303; Date of registration: 10 May 2021).

What weeding robots need to know about ecology

In weed control the aims of securing crop productivity and protecting biodiversity are often difficult to reconcile. Currently, the development of autonomous in-field intervention technology, such as field robots, is creating new potential for minimizing trade-offs between these two aims. To exploit this potential, weed management strategies need to adapt. However, it is currently unclear which kind of input information (e.g. weed cover, number of weeds, weed species identity) is required for such a targeted approach, and which impacts the robotic application has on the trade-off between crop yield and biodiversity. Here, we used a dataset from organically farmed fields to assess several weed management strategies, simulating robot-supported weed control. Specifically, we used within-field heterogeneity of several weed and crop productivity variables to model effects of different kinds of input information for a hypothetical, spatially selective robotic weed control system. The results showed that, at a defined yield loss, gamma diversity (number of weed species on the entire investigated area) is maintainable to a large degree, even without information on weed or crop heterogeneity within the field being used to decide where to weed. However, to maintain alpha diversity (average number of weed species per plot), more spatially explicit input information is required, such as on the number of species per plot, weed quantity (weed cover per species), and weed competitiveness. Consequently, a weeding robot would have to be technically capable of distinguishing between individual weed species, measuring weed cover, processing captured information in real time and removing weeds at per-plant level. Further, it could be shown that the success of such a complex weed management strategy is independent of the degree of spatial heterogeneity of crop yield and of the present level of weed species richness.

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.

This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

ABSTRACT The control problem for the multivariable and nonlinear dynamics of robotic manipulators and autonomous vehicles is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these robotic systems is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearised flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the first subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a 3-DOF industrial rigid-link robotic manipulator and (b) control of a 3-DOF autonomous underwater vessel. The novel control method can simplify significantly the solution of the nonlinear control problem for robotic manipulators and vehicles. Unlike global linearisation-based control schemes, the proposed flatness-based method in successive loops does not need any changes in state variables of complicated state-space model transformations.

Active Exploration Deep Reinforcement Learning for Continuous Action Space with Forward Prediction

The application of reinforcement learning (RL) to the field of autonomous robotics has high requirements about sample efficiency, since the agent expends for interaction with the environment. One method for sample efficiency is to extract knowledge from existing samples and used to exploration. Typical RL algorithms achieve exploration using task-specific knowledge or adding exploration noise. These methods are limited to current policy improvement level and lack of long-term planning. We propose a novel active exploration deep RL algorithm for the continuous action space problem named active exploration deep reinforcement learning (AEDRL). Our method uses the Gaussian process to model dynamic model, enabling the probability description of prediction sample. Action selection is formulated as the solution of the optimization problem. Thus, the optimization objective is specifically designed for selecting samples that can minimize the uncertainty of the dynamic model. Active exploration is achieved through long-term optimized action selection. This long-term considered action exploration method is more guidance for learning. Enable intelligent agents to explore more interesting action spaces. The proposed AEDRL algorithm is evaluated on several robotic control task including classic pendulum problem and five complex articulated robots. The AEDRL can learn a controller using fewer episodes and demonstrates performance and sample efficiency.

Adaptive Parallel Iterative Learning Control with A Time-Varying Sign Gain Approach Empowered by Expert System

This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.

Theory of Timely Remote Estimation and Application toSituational Awareness

Brief Biography: Tasmeen Zaman Ornee is a PhD candidate at the Electrical and Computer Engineering Department of Auburn University. She joined the Real-time Networking lab in Fall 2018. Her PhD supervisor is Prof. Yin Sun. She completed her M.Sc. in Electrical Engineering from Auburn University in 2022. She received her B.Sc. degree in Electrical &amp; Electronic Engineering from Bangladesh University of Engineering and Technology in 2017. Her research interests include information freshness, remote estimation, robotic control, and wireless communication networks. She has worked on designing novel sampling and scheduling policies for the remote estimation of diffusion processes involving single and multiple sources. In addition, she has worked on designing low-complexity scheduling algorithms for improving situational awareness in safety-critical systems. She received the IEEE/IFIP WiOpt Best Paper Award in 2019, one of her IEEE/ACM ToN journals was recommended for fast-tracked review, and she had an invited paper at IEEE SPAWC 2021. She is also one of the authors of a book chapter published by CUP. She is a graduate student member of IEEE and ACM.

Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives

This article reviews recent advances in intelligent robotic ultrasound imaging systems. We begin by presenting the commonly employed robotic mechanisms and control techniques in robotic ultrasound imaging, along with their clinical applications. Subsequently, we focus on the deployment of machine learning techniques in the development of robotic sonographers, emphasizing crucial developments aimed at enhancing the intelligence of these systems. The methods for achieving autonomous action reasoning are categorized into two sets of approaches: those relying on implicit environmental data interpretation and those using explicit interpretation. Throughout this exploration, we also discuss practical challenges, including those related to the scarcity of medical data, the need for a deeper understanding of the physical aspects involved, and effective data representation approaches. We conclude by highlighting the open problems in the field and analyzing different possible perspectives on how the community could move forward in this research area.

Robotic Control in Adversarial and Sparse Reward Environments: A Robust Goal-Conditioned Reinforcement Learning Approach

With deep neural networks based function approximators, reinforcement learning holds the promise of learning complex end-to-end robotic controllers that can map high-dimensional sensory information directly to control policies. However, a common challenge, especially for robotics, is sample-efficient learning from sparse rewards, in which an agent is required to find a long sequence of “correct” actions to achieve a desired outcome. Unfortunately, inevitable perturbations on observations may make this task trickier to solve. Here, this paper advances a novel robust goal-conditioned reinforcement learning approach for end-to-end robotic control in adversarial and sparse reward environments. Specifically, a mixed adversarial attack scheme is presented to generate diverse adversarial perturbations on observations by combining white-box and black-box attacks. Meanwhile, a hindsight experience replay technique considering observation perturbations is developed to turn a failed experience into a successful one and generate the policy trajectories perturbed by the mixed adversarial attacks. Additionally, a robust goal-conditioned actor-critic method is proposed to learn goal-conditioned policies and keep the variations of the perturbed policy trajectories within bounds. Finally, the proposed method is evaluated on three tasks with adversarial attacks and sparse reward settings. The results indicate that our scheme can ensure robotic control performance and policy robustness on the adversarial and sparse reward tasks.

Learning Playing Piano with Bionic-Constrained Diffusion Policy for Anthropomorphic Hand.

Anthropomorphic hand manipulation is a quintessential example of embodied intelligence in robotics, presenting a notable challenge due to its high degrees of freedom and complex inter-joint coupling. Though recent advancements in reinforcement learning (RL) have led to substantial progress in this field, existing methods often overlook the detailed structural properties of anthropomorphic hands. To address this, we propose a novel deep RL approach, Bionic-Constrained Diffusion Policy (Bio-CDP), which integrates knowledge of human hand control with a powerful diffusion policy representation. Our bionic constraint modifies the action space of anthropomorphic hand control, while the diffusion policy enhances the expressibility of the policy in high-dimensional continuous control tasks. Bio-CDP has been evaluated in the simulation environment, where it has shown superior performance and data efficiency compared to state-of-the-art RL approaches. Furthermore, our method is resilient to task complexity and robust in performance, making it a promising tool for advanced control in robotics.

Single and Multi-Hand Gesture based Soft Material Robotic Car Control

In this research article, authors have engineered the model to control soft material robotic car with single and multi-hand gestures based on Artificial Intelligence and Machine Learning approach. A user centric representation of this features is obtained by using hand detection and hand gesture recognition mechanism. Authors have assigned the desire hand gestures to move the soft material robotic car in 360 degrees. In this research work, different gestures of human hands are simulated in terms of landmarks using Mediapipe. Each combination of hand landmark performs different actions to control the robotic car in multiple directions. In this research work, the soft material robotic car has been controlled using single hand as well as multi hands gesture-based approach. Our motive of this research work is to reduce human efforts by controlling the soft material robotic car through hand gestures based on AIML techniques. The proposed CNN model has yielded high accuracy as compared to baseline ML algorithms such as KNN, Decision Tree, SVM etc. It is also observed that CNN model remains ahead of other ML algorithms for both single hand and multi-hand gesture based robotic car control. The performance of proposed CNN model for controlling robotic car has been represented in term of ROC curve using validation and test data set. The average performances of single hand gesture and multi hand gestures using proposed CNN are found as 97% and 98% respectively.

Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.

Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.

Hierarchical Optimization for Personalized Hand and Wrist Musculoskeletal Modeling and Motion Estimation.

Surface electromyography (sEMG) driven musculoskeletal models are promising to be applied in the field of human-computer interaction. However, due to the individual-specific physiological characteristics, generic models often fail to provide accurate motion estimation. This study optimized the general model to build a personalized model and improve the accuracy of motion estimation. Inspired by the coupling effect of wrist/hand movement, a hierarchical optimization approach for personalizing musculoskeletal models (HOPE-MM) is proposed, which aligns with the physiological characteristics of the human wrist and hand. To verify the effectiveness of personalized musculoskeletal model, single joint motions and simultaneous joint motions are estimated. In addition, Sobol sensitivity analysis is conducted to identify the key parameters of musculoskeletal model, providing guidance for model simplification. The mean pearson correlation coefficient between the predicted joint angles and the measured joint angles are 0.95 ± 0.03 and 0.93 ± 0.01 for simultaneous wrist and metacarpophalangeal (MCP) joint movements, respectively, which have a significant improvement compared with the stateof- the-art works. By optimizing only the key parameters including tendon slack length, maximal isometric force and optimal fiber length, the performances of simplified model are comparable to the full-parameter model. These results provide insights into the effects of muscletendon parameters on musculoskeletal model, and musculoskeletal models personalized using hierarchical optimization methods can improve the accuracy of motion estimates. These findings facilitate the clinical application of musculoskeletal models in rehabilitation and robotic control.

Neuromorphic Quadratic Programming for Efficient and Scalable Model Predictive Control: Towards Advancing Speed and Energy Efficiency in Robotic Control

Neuromorphic Quadratic Programming for Efficient and Scalable Model Predictive Control: Towards Advancing Speed and Energy Efficiency in Robotic Control

Nonlinear Modeling Analysis of Stabilization Behavior of Robotic Gait Control System Based on Image Processing Techniques

Abstract Existing robot gait control methods have problems such as high gait energy consumption and difficulty in generating optimal gait strategies. This paper designs a robot gait automatic control system based on the X86 platform and robot sensor interface. The main board of the robot controller is designed based on CISC, which ensures high stability and anti-interference capabilities. The information provided by ultrasonic sensors and infrared sensors is used to adjust and execute the robot’s trajectory, action sequence, and gait. To extract the target in the gait image, the frame difference algorithm is employed, and the phase and amplitude factors are collected after the Radon and Fourier-Mellin transform to identify gait characteristics for robot gait recognition. The robot’s two-dimensional spatial dynamics model is constructed continuously, and its dynamics equations are derived. The residual fusion technique is used to combine image data with sensor data. A system for stabilizing gait control has been designed. The tracking error rate of the robot is not more than ±10% in general, and a variety of gait patterns can be used to cross the obstacles, which verifies the effectiveness of the designed system for realizing the gait control and performance of the robot.

Abduction/Adduction Assistance From Powered Hip Exoskeleton Enables Modulation of User Step Width During Walking.

As the first step to show its potential, the objective of this study was to investigate how human's step width reacted to hip exoskeleton's admittance control parameter changes during walking. Ten non-disabled individuals walked on a treadmill at a self-selected speed, while wearing our bilateral robotic hip exoskeleton. We used two equilibrium positions to define the direction of assistance. We studied the influence of multiple stiffness values in the admittance control on the participants' step width, step length, and electromyographic (EMG) activity of the gluteus medius. Step width were significantly modulated by the change of stiffness in exoskeleton control, while step length did not show significant changes. When the stiffness changed from zero to our studied stiffness values, the participants' step width started to modulate immediately. Within 4 consecutive heel strikes right after a stiffness change, the step width showed a significant change. Interestingly, EMG activity of the gluteus medius did not change significantly regardless the applied stiffness and powered direction. Tuning of stiffness in admittance control of a hip exoskeleton, acting in mediolateral direction, can be a viable way for controlling step width in normal walking. Unvaried gluteus medius activity indicates that the increase in step width were mainly caused by the assistive torque applied by the exoskeleton. Our study results pave a new way for future design and control of wearable robotics in enhancing mediolateral walking balance for various rehabilitation applications.