2021· International Journal of Intelligent Automation & Robotics Engineering· Vol 4, pp. 01-15· 0 citations
TL;DR
The proposed AFCS-CRM significantly improves force tracking accuracy, manipulation stability, grasp reliability, response time, energy efficiency, and human safety, and demonstrates strong potential for next-generation smart manufacturing, robotic assembly, precision surgery, warehouse automation, and assistive robotics.
Abstract
Collaborative robotic manipulation has become a critical technology in modern industrial automation, healthcare, logistics, precision manufacturing, and service robotics by enabling safe human–robot collaboration within shared workspaces. Unlike conventional industrial robots operating with fixed, pre-programmed motions, collaborative robots (cobots) require adaptive force control to ensure stable interaction, precise manipulation, and human safety under dynamic and uncertain environments. This paper proposes an Adaptive Force Control Strategy for Collaborative Robotic Manipulation (AFCS-CRM) that integrates multi-modal sensing, sensor fusion, intelligent feature engineering, adaptive impedance control, machine learning-based force prediction, and reinforcement learning into a unified control framework. The system continuously acquires force, torque, tactile, vision, position, and velocity data, applies advanced preprocessing and feature extraction, predicts optimal interaction forces, and adaptively updates control parameters in real time. By combining predictive learning with impedance-based force control and continuous feedback optimization, AFCS-CRM maintains stable contact forces despite uncertainties, varying loads, and object deformations. Compared with conventional PID and fixed impedance controllers, the proposed framework significantly improves force tracking accuracy, manipulation stability, grasp reliability, response time, energy efficiency, and human safety. The scalable and intelligent architecture demonstrates strong potential for next-generation smart manufacturing, robotic assembly, precision surgery, warehouse automation, and assistive robotics, providing a robust foundation for safe, adaptive, and autonomous human–robot collaboration.
Safe and intuitive human robot interaction (HRI) requires precise regulation of contact forces and torques while adapting to dynamic and uncertain human behavior. Traditional impedance and admittance control strategies rely on fixed parameters and accurate system modeling, which often limit their performance in unstructured or collaborative environments. This paper presents an AI-enabled force and torque control framework that integrates machine learning techniques with conventional control methods to enhance adaptability, compliance, and safety in physical human robot interaction. The proposed approach employs deep neural networks and reinforcement learning to learn human intent and interaction dynamics directly from multi-modal sensor data, including force torque sensors, joint encoders, and inertial measurements. By continuously adjusting control gains in real time, the system achieves stable interaction while minimizing excessive contact forces and undesired torques. Experimental evaluations conducted on a collaborative robotic platform demonstrate significant improvements over classical control schemes, including reduced interaction force peaks, smoother torque profiles, and improved task execution efficiency during cooperative manipulation tasks. The results indicate that AI-driven force and torque control can substantially improve robustness, adaptability, and user comfort in human robot collaboration, making it a promising solution for applications in rehabilitation robotics, assistive devices, and industrial cobots.
Vishal Khanna· i-manager's Journal on Augme...· 0 citations
Robotic manipulators are essential in modern fields such as industrial automation, precision manufacturing, medical robotics, and aerospace. While traditional control methods like PID, computed torque, and adaptive control perform well in structured environments, they struggle with nonlinearities, uncertainties, payload variations, and disturbances. To address these limitations, hybrid control strategies combining conventional and intelligent techniques—such as fuzzy logic, neural networks, and sliding mode control—have emerged as effective solutions. This work focuses on hybrid control approaches that enhance accuracy, robustness, and adaptability. It begins with dynamic modeling using the Euler-Lagrange formulation, highlighting the nonlinear and complex nature of robotic systems. The proposed method integrates computed torque control for trajectory tracking, fuzzy logic for handling uncertainties, and neural networks for adaptive tuning and parameter estimation. Literature indicates that hybrid methods like fuzzy-PID and neural-based adaptive control significantly improve tracking performance, reduce steady-state error, and enhance robustness, though challenges like computational complexity remain. Simulation and experimental results demonstrate that the proposed hybrid controller outperforms traditional methods in terms of tracking accuracy, settling time, and disturbance rejection. In conclusion, hybrid control strategies offer a powerful framework for high-precision robotic manipulation by effectively addressing nonlinearities and uncertainties. Future work will focus on real-time implementation, optimization of hybrid designs, and integration with advanced sensing technologies.
Anita Verma· International Journal of Int...· 0 citations
The autonomous robotic manipulators have become inevitable in the contemporary industrial automation, medical robotics, space exploration, and service robots. But it is an inherent challenge to have accurate, active and strong control of robotic arms under dynamic and uncertain conditions. Conventional control techniques utilizing only forward or inverse kinematics have drawbacks of singularities, local minima, sluggish convergence and lack of computational efficiency. In order to solve these problems, the current paper will cover a new autonomous robotic arm control system (ARCs) by relying on a Hybrid Kinematic Optimization(HKO) approach that combines analytical inverse kinematics, numerical optimization, intelligent constraint management. The suggested framework will integrate classical DenavitHartenberg (D-H) kinematic modeling with the use of the gradient-based and evolutionary optimization to produce the optimal joint trajectories in real-time. There is the introduction of a hybrid cost function that involves position accuracy, orientation error, joint smoothness, and energy efficiency. Collision avoidance and workspace constraints are also factored in the control architecture to be used to ensure safe and reliable operation. The hybrid optimizer is a dynamical algorithm that changes between fast analytical solvers and the global numerical optimizers based on the complexity of the task and the environmental conditions. Experiments involving simulation experiments on a 6-DOF model of industrial robotic arm on different task settings, such as pick-and-place settings, obstacle avoidance, and tracking in a trajectory are carried out. Convergence rate, tracking accuracy, joint torque efficiency and computational load are among the performance metrics considered and compared to the more traditional inverse kinematics and pure optimization-based methods. Findings indicate that the suggested hybrid structure attains a maximum speed of convergence is 35 percent, trajectory error is 28 percent and energy use is 22 percent. The Hybrid Kinematic Optimization framework presented provides a robust, scalable and intelligent framework applicable to solve the needs of next generation autonomous robotic manipulators to work within dynamic environments.
Hyeon Woo-Lee, Ken Seok-Park· International Journal of Int...· 0 citations
(English) Autonomous and collaborative mobile manipulators are expected to play a central role in future Smart Factories by enabling flexible, human-centered production. Achieving this vision requires robotic systems that can safely react to dynamic human behavior while efficiently executing complex manipulation tasks. These requirements impose high computational demands on perception, planning, and control, which can exceed the capabilities of mobile robotic platforms. Distributed control architectures leveraging edge computing and wireless communication offer a promising solution by enabling computational offloading. However, their effectiveness depends on tightly coupled and interdisciplinary factors spanning communication, computer vision, and control. Beyond computational offloading, distributed control systems also enable distributed perception by integrating external visual sensors, extending the robot’s perceptual field beyond onboard sensing limitations.
This thesis investigates how distributed control architectures for autonomous and collaborative mobile manipulators can be designed to safely and efficiently exploit wireless communication and edge computing in industrial environments. Two complementary architectures are developed and evaluated, differing fundamentally in perception placement, control strategy, and the role of communication latency.
The first architecture, an Edge-Enabled system, targets safety-critical collaborative scenarios using onboard RGB-D sensing with optional edge offloading. A complete distributed perception-control loop is implemented over private 5G networks, integrating wireless communication, edge-based image processing, and closed-loop Cartesian velocity control. Extensive simulated and real experiments analyze how sensing rate, image resolution and compression, computation latency, communication technology, and quality-of-service settings jointly affect end-to-end reaction time. The results show that edge computing reduces latency only under specific conditions and that robust safety behavior requires explicit mechanisms to handle jitter and perception failures.
The second architecture, an Edge-Dependent system, addresses deliberative manipulation under fixed distributed constraints, where perception and planning are performed externally. A learning-based motion planning framework is introduced that generates smooth, near-optimal, collision-free 3D trajectories with low online computation time using external visual data. Simulation and real-robot experiments demonstrate generalization across obstacle configurations and competitive performance compared to established planning baselines.
Together, these contributions clarify when wireless edge computing enhances robotic performance and when it introduces fundamental constraints, providing practical system designs and conceptual insights for distributed robotic control in next-generation industrial environments.
(Català) S'espera que els manipuladors mòbils autònoms i col·laboratius tinguin un paper central en les fàbriques intel·ligents del futur, en permetre una producció flexible i centrada en l'ésser humà. Per assolir aquesta visió calen sistemes robòtics que puguin reaccionar de manera segura al comportament humà dinàmic, alhora que executen de forma eficient tasques de manipulació complexes. Aquests requisits imposen altes demandes computacionals a la percepció, la planificació i el control, les quals poden superar les capacitats de les plataformes robòtiques mòbils. Les arquitectures de control distribuït que aprofiten el processament en el límit de la xarxa i la comunicació sense fils ofereixen una solució prometedora en permetre la delegació de càrrega computacional. No obstant això, la seva eficàcia depèn de factors estretament vinculats i interdisciplinaris que abasten la comunicació, la visió per computador i el control. Més enllà de la delegació de càlcul, els sistemes de control distribuït també permeten la percepció distribuïda mitjançant la integració de sensors visuals externs, ampliant el camp perceptiu del robot més enllà de les limitacions dels sensors a bord.
Aquesta tesi investiga com es poden dissenyar arquitectures de control distribuït per a manipuladors mòbils autònoms i col·laboratius per aprofitar de manera segura i eficient la comunicació sense fils i el processament en el límit (edge computing) en entorns industrials. Es desenvolupen i avaluen dues arquitectures complementàries que difereixen fonamentalment en la ubicació de la percepció, l'estratègia de control i el paper de la latència de la comunicació.
La primera arquitectura, un sistema habilitat per a la vora (Edge-Enabled), està orientada a escenaris col·laboratius de seguretat crítica que utilitzen sensors RGB-D a bord amb una càrrega lateral opcional a la vora. S'implementa un bucle complet de percepció i control distribuït sobre xarxes 5G privades, que integra comunicació sense fils, processament d'imatges basat en la vora i control de velocitat cartesiana en bucle tancat. Amplis experiments simulats i reals analitzen com la velocitat de captació, la resolució i la compressió d'imatges, la latència de càlcul, la tecnologia de comunicació i la configuració de la qualitat de servei afecten conjuntament el temps de reacció de principi a fi. Els resultats mostren que la computació en el límit només redueix la latència en condicions específiques i que un comportament de seguretat robust requereix mecanismes explícits per gestionar la variància i els errors de percepció.
La segona arquitectura, un sistema dependent de l'edge (Edge-Dependent), aborda la manipulació deliberativa sota restriccions distribuïdes fixes, on la percepció i la planificació es duen a terme externament. Es presenta un marc de planificació de moviments basat en l'aprenentatge que genera trajectòries 3D suaus, gairebé òptimes i lliures de col·lisions amb un temps de computació en línia baix, utilitzant dades visuals externes. Les simulacions i els experiments amb robots reals demostren la generalització a través de configuracions d'obstacles i un rendiment competitiu en comparació amb línies de base de planificació establertes.
En conjunt, aquestes contribucions aclareixen quan la computació edge sense fils millora el rendiment robòtic i quan introdueix limitacions fonamentals, proporcionant dissenys de sistema pràctics i perspectives conceptuals per al control robòtic distribuït en entorns industrials de nova generació.
(Español) Se prevé que los manipuladores móviles autónomos y colaborativos desempeñen un papel fundamental en las futuras fábricas inteligentes, al permitir una producción flexible y centrada en las personas. Para hacer realidad esta visión se necesitan sistemas robóticos capaces de reaccionar de forma segura ante el comportamiento dinámico de las personas, al tiempo que ejecutan con eficiencia tareas de manipulación complejas. Estos requisitos imponen elevadas exigencias computacionales en materia de percepción, planificación y control, que pueden superar las capacidades de las plataformas robóticas móviles. Las arquitecturas de control distribuido que aprovechan la computación periférica y la comunicación inalámbrica ofrecen una solución prometedora al permitir la descarga computacional. Sin embargo, su eficacia depende de factores estrechamente relacionados e interdisciplinarios que abarcan la comunicación, la visión artificial y el control. Más allá de la descarga computacional, los sistemas de control distribuido también permiten la percepción distribuida mediante la integración de sensores visuales externos, ampliando el campo perceptivo del robot más allá de las limitaciones de los sensores integrados.
Esta tesis investiga cómo se pueden diseñar arquitecturas de control distribuido para manipuladores móviles autónomos y colaborativos con el fin de aprovechar de forma segura y eficiente la comunicación inalámbrica y la computación periférica en entornos industriales. Se desarrollan y evalúan dos arquitecturas complementarias, que difieren fundamentalmente en la ubicación de la percepción, la estrategia de control y el papel de la latencia de la comunicación.
La primera arquitectura, un sistema habilitado para el borde (Edge-Enabled), está orientada a escenarios colaborativos críticos para la seguridad que utilizan sensores RGB-D a bordo con descarga opcional al borde. Se implementa un bucle completo de percepción y control distribuido a través de redes 5G privadas, que integra comunicación inalámbrica, procesamiento de imágenes en el borde y control de velocidad cartesiano en bucle cerrado. Unos extensos experimentos simulados y reales analizan cómo la frecuencia de detección, la resolución y compresión de la imagen, la latencia de cálculo, la tecnología de comunicación y los ajustes de calidad de servicio afectan conjuntamente al tiempo de reacción de extremo a extremo. Los resultados muestran que la computación en el borde reduce la latencia solo en condiciones específicas y que un comportamiento de seguridad robusto requiere mecanismos explícitos para gestionar la fluctuación y los fallos de percepción.
La segunda arquitectura, un sistema dependiente del borde (Edge-Dependent), aborda la manipulación deliberativa bajo restricciones distribuidas fijas, donde la percepción y la planificación se realizan externamente. Se introduce un marco de planificación de movimiento basado en el aprendizaje que genera trayectorias 3D fluidas, casi óptimas y sin colisiones con un tiempo de computación en línea reducido utilizando datos visuales externos. Los experimentos de simulación y con robots reales demuestran la generalización a través de configuraciones de obstáculos y un rendimiento competitivo en comparación con las líneas de base de planificación establecidas.
En conjunto, estas aportaciones aclaran en qué casos la computación periférica inalámbrica mejora el rendimiento de los robots y en cuáles introduce limitaciones fundamentales, aportando di
Soft robots, featuring flexible and adaptable structures, have become an indispensable part of modern industrial production, medical care and infrastructure construction. Their unique flexible design allows them to adapt to various complex and harsh working environments, effectively solving the limitations of traditional rigid robots in special scenarios. High- performance control technology is the core guarantee for their stable operation, which has attracted extensive attention in the field of intelligent robotics and has become a key research direction. This paper systematically sorts out the latest research progress of soft robot control technology, elaborates on the optimized application of traditional PID control and its improved algorithms, details the practical application effect of pneumatic drive technology in complex working conditions, and deeply explores the application of intelligent control technologies such as neural networks and multi-vision control. It also analyzes the existing bottlenecks in nonlinear control systems, puts forward targeted improvement measures, and looks forward to the development trend of parameter optimization, algorithm fusion and lightweight deployment, providing valuable ideas and technical support for the high-precision, intelligent and efficient development of soft robots.
The primary control objective in robotic interaction tasks has shifted from trajectory tracking accuracy to interaction force regulation. Impedance control enables compliant interaction by shaping the robot’s force response through a target impedance model. However, the dynamic response of conventional impedance control is fixed during parameter design, limiting its adaptability to varying interaction environments. To address this issue, a direct force compensation–based adaptive hybrid impedance control (DAHIC) method is proposed. By introducing an adaptive force compensation factor based on the force tracking error, the proposed controller achieves faster response speed while suppressing overshoot and reducing steady-state error, especially in tracking higher-order signals. The allowable range of controller parameters is derived via closed-loop stability analysis, and an adaptive update law is designed accordingly. Furthermore, the proposed adaptive force compensation is integrated with hybrid impedance control to form a six-dimensional task-space compliance controller. The numerical simulations conducted in the single-force subspace, together with the comparative experiments performed in the six-dimensional task space involving constant- and variable-curvature contact surfaces as well as abrupt variations in contact stiffness, collectively validate the improvement in force tracking performance achieved by the proposed algorithm. Note to Practitioners—In robotic interaction tasks, such as assembly, surface contact, and human–robot collaboration, achieving stable and accurate force regulation in uncertain environments remains challenging for conventional impedance controllers with fixed parameters. This paper proposes a direct force compensation–based adaptive hybrid impedance control method to improve force tracking performance under varying interaction conditions. By adaptively adjusting the force compensation according to tracking error, the controller achieves faster response, reduced overshoot, and improved steady-state accuracy, and can be naturally extended to six-dimensional task-space compliance control. The stability of the closed-loop system is ensured through analytical parameter constraints, providing practical guidance for controller design. Practitioners may find this approach useful for enhancing compliant interaction performance in real-world robotic applications, while practical implementation requires appropriate force sensing, real-time computation, and careful parameter tuning.
Hongjun Xing, Yu-Zhe Xu, Yi Xie et al.· IEEE Transactions on Automat...· 0 citations