multi-DOF Manipulator Research Articles

Single Spring Gravity Compensator for a Multi-DOF Manipulator

Single Spring Gravity Compensator for a Multi-DOF Manipulator

High-Order Derivatives of Serial Manipulator Jacobians Using Multidual Differentiation Transform

Abstract The use of robots is continuously growing, from heavy-duty industries to nanotechnology. Exact multilink robot end effector control is required to withstand this tendency in modern robotics. Mapping between joint variables in joint-space coordinate and end effector configuration in task-space coordinate are provided by serial manipulator kinematics. A computation of higher-order Jacobian matrix derivatives is required for accurate trajectory tracking. With conventional numerical derivation, only approximate results can be obtained. Still, the computation of high-order derivatives of multi-DoF manipulators with high accuracy requires long time intervals and it is difficult. This paper investigates a novel derivation method for a multilink robot Jacobian. According to this method, an exact value of higher-order acceleration can be obtained using a multidual differentiation transform. Multidual functions for sine and cosine will be used to get the exact value of acceleration, jerk, and hyper-jerk (jounce) expressions, commonly used for accurate trajectory-tracking.

Automation of Intraoperative Tool Changing for Robot-Assisted Laparoscopic Surgery

Intraoperative tool change is a time-consuming and labor-intensive task for robot-assisted laparoscopic surgery. Serial multi-DOF manipulators are potential devices for realizing automatic intraoperative tool changes because of the layout flexibility and motion range, and multi-DOF makes it feasible for the manipulator to access and fetch the surgical tools by itself. However, the direction of the trocar may change because of the soft abdomen, and the lack of a fixed RCM makes it difficult for manipulators to reinsert a new surgical tool through the trocar. This study proposes a system prototype using a 7-DOF manipulator to automatically conduct the intraoperative tool-changing task. The newly designed surgical tool docking station facilitates surgical tool coupling/decoupling by rotating the manipulator’s end effector once. The proposed trocar recognition method with position error compensation is reliable for aligning a new surgical tool to the trocar port, even when the direction of the trocar is changed. The experimental results confirms that the manipulator can accomplish an intraoperative tool changing task without additional assistance or correction from the human.

A Multi-DOF Manipulator Joint Trajectory Tracking and Monitoring Method Based on Decision Tree

Understanding trajectory tracking control concerns is crucial for industrial-grade manipulators to provide precise and risk-free operations for the safe environment. Consequently, the robot arms need precise aim for tracking a given target trajectory by the trajectory control input driving torque which can use smart AI-based techniques for precision. Similarly, a decision tree is a soft computing-based method of feature space partitioning which can certainly allow the movement of robots in an accurate manner. The control of robot arms is an important aspect for automating the process of sustainable development. Aiming at the problem of poor tracking accuracy of traditional Multi-Degree-of-Freedom Manipulator Joint Trajectory Monitoring (Multi-DoF MJTM) and long monitoring delay, this article proposes a Multi-DOF manipulator joint trajectory tracking method based on decision tree. The Multi-DoF manipulator is developed for the adaptive control object of the working machine, and it is combined with the output response feature to construct the kinematics model of the Multi-DoF manipulator mechanism of the walking machine. The joint trajectory reconstruction of the running trajectory is used to obtain the joint trajectory deviation of the Multi-DoF manipulator’s running trajectory through the multi-measurement system. Based on this, the Multi-DoF manipulator’s running trajectory joint trajectory tracking control equation is obtained to realize the joint trajectory tracking and monitoring of the manipulator. The features for a safe environment are also integrated. The experimental results show that the proposed method has high accuracy in tracking the trajectory of Multi-DoF manipulator joints, and the time delay for tracking and monitoring the trajectory of manipulator’s joints is also optimized.

Design and Modelling of Multi-DOF Manipulator Driven by Hysteresis-Attenuated Pneumatic Artificial Muscles

In this article, a multi-DOF manipulator driven by novel hysteresis-attenuated pneumatic artificial muscles (PAMs) is designed, and the multi-DOF manipulator is composed of three manipulator units assembled in cascade, and each manipulator is driven by hysteresis-attenuated PAMs. Compared with the conventional McKibben PAMs, the proposed hysteresis-attenuated PAMs is improved structurally to attenuate the undesired hysteresis by implanting a compression spring. Moreover, the mathematical models of both the hysteresis-attenuated PAMs and the multi-DOF manipulator are established. Finally, comparison test results show that the hysteresis is attenuated effectively for the hysteresis-attenuated PAMs, and the experimental results show that good control performances are achieved for the multi-DOF manipulator under different loads and pressures.

VDC-based admittance control of multi-DOF manipulators considering joint flexibility via hierarchical control framework

Multi-degree-of-freedom (Multi-DOF) manipulators have shown a high potential for enhancing the flexibility and performance of robotic manipulations. However, the presence of unknown disturbance, including uncertain dynamics and external forces/torques, makes the control of a multi-DOF manipulator rather complicated, and the stability of the robotic system is hard to be guaranteed. In this paper, a virtual decomposition control (VDC)-based admittance control approach for multi-DOF manipulators has been proposed considering joint flexibility via hierarchical control framework. The joint flexibility is solved by a separate adaptive controller different from the manipulator’s links. The high-level admittance controller is built upon a low-level VDC-based inner control loop, which can deal with the complicated system dynamics (including the joint friction and joint flexibility) and modeling uncertainty. The external force/torque (F/T) is estimated with a generalized momentum-based interaction force estimation technique; thereby avoiding the cost of wrist F/T sensors. The robotic system’s stability has been guaranteed in both free-space motions and constrained motions using the specific features of VDC (proof of each subsystem’s virtual stability). The advantages and effectiveness of the proposed method in tuning the robot–environment dynamic behavior are demonstrated through experiments.

A Deep Reinforcement Learning Strategy Combining Expert Experience Guidance for a Fruit-Picking Manipulator

When using deep reinforcement learning algorithms for path planning of a multi-DOF fruit-picking manipulator in unstructured environments, it is much too difficult for the multi-DOF manipulator to obtain high-value samples at the beginning of training, resulting in low learning and convergence efficiency. Aiming to reduce the inefficient exploration in unstructured environments, a reinforcement learning strategy combining expert experience guidance was first proposed in this paper. The ratios of expert experience to newly generated samples and the frequency of return visits to expert experience were studied by the simulation experiments. Some conclusions were that the ratio of expert experience, which declined from 0.45 to 0.35, was more effective in improving learning efficiency of the model than the constant ratio. Compared to an expert experience ratio of 0.35, the success rate increased by 1.26%, and compared to an expert experience ratio of 0.45, the success rate increased by 20.37%. The highest success rate was achieved when the frequency of return visits was 15 in 50 episodes, an improvement of 31.77%. The results showed that the proposed method can effectively improve the model performance and enhance the learning efficiency at the beginning of training in unstructured environments. This training method has implications for the training process of reinforcement learning in other domains.

Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning

Many real-world tasks require multiple agents to work together. When talking about multiple agents in robotics, it is usually referenced to multiple manipulators in collaboration to solve a given task, where each one is controlled by a single agent. However, due to the increasing development of modular and re-configurable robots, it is also important to investigate the possibility of implementing multi-agent controllers that learn how to manage the manipulator’s degrees of freedom (DoF) in separated clusters for the execution of a given application (e.g., being able to face faults or, partially, new kinematics configurations). Within this context, this paper focuses on the decentralization of the robot control action learning and (re)execution considering a generic multi-DoF manipulator. Indeed, the proposed framework employs a multi-agent paradigm and investigates how such a framework impacts the control action learning process. Multiple variations of the multi-agent framework have been proposed and tested in this research, comparing the achieved performance w.r.t. a centralized (i.e., single-agent) control action learning framework, previously proposed by some of the authors. As a case study, a manipulation task (i.e., grasping and lifting) of an unknown object (to the robot controller) has been considered for validation, employing a Franka EMIKA panda robot. The MuJoCo environment has been employed to implement and test the proposed multi-agent framework. The achieved results show that the proposed decentralized approach is capable of accelerating the learning process at the beginning with respect to the single-agent framework while also reducing the computational effort. In fact, when decentralizing the controller, it is shown that the number of variables involved in the action space can be efficiently separated into several groups and several agents. This simplifies the original complex problem into multiple ones, efficiently improving the task learning process.

A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator

Deep Reinforcement Learning (DRL) has been an active research area in view of its capability in solving large-scale control problems. Until presently, many algorithms have been developed, such as Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and so on. However, the converging achievement of DRL often requires extensive collected data sets and training episodes, which is data inefficient and computing resource consuming. Motivated by the above problem, in this paper, we propose a Twin-Delayed Deep Deterministic Policy Gradient algorithm with a Rebirth Mechanism, Tetanic Stimulation and Amnesic Mechanisms (ATRTD3), for continuous control of a multi-DOF manipulator. In the training process of the proposed algorithm, the weighting parameters of the neural network are learned using Tetanic stimulation and Amnesia mechanism. The main contribution of this paper is that we show a biomimetic view to speed up the converging process by biochemical reactions generated by neurons in the biological brain during memory and forgetting. The effectiveness of the proposed algorithm is validated by a simulation example including the comparisons with previously developed DRL algorithms. The results indicate that our approach shows performance improvement in terms of convergence speed and precision.

Model identification and parametric adaptive control of hydraulic manipulator with neighborhood field optimization

The model identification and uncertain parameters estimation are common control problems in multi-DOF manipulator system, since both model parametric uncertainties and unknown external disturbance often degrade the output performance and the close-loop system stability. In this study, by using the joint angle and the torque information, a neighborhood field optimization is adopted to identify the Lagrange model parameters of two-DOF hydraulic manipulator. The fitness function of the neighborhood field optimization is designed to optimize the minimal squared error of the torque estimation and to obtain the lumped estimated parameters with high accuracy. Then based on the identification model, a parametric adaptive controller is designed to address uncertain electro-hydraulic parameters and external disturbances. Furthermore, a new stable control variable is redesigned to avoid the redundant input saturation of electro-hydraulic actuator. The effectiveness of the proposed controller is verified by the comparative experimental results with the general backstepping controller.

The Control Method of Twin Delayed Deep Deterministic Policy Gradient with Rebirth Mechanism to Multi-DOF Manipulator

As a research hotspot in the field of artificial intelligence, the application of deep reinforcement learning to the learning of the motion ability of a manipulator can help to improve the learning of the motion ability of a manipulator without a kinematic model. To suppress the overestimation bias of values in Deep Deterministic Policy Gradient (DDPG) networks, the Twin Delayed Deep Deterministic Policy Gradient (TD3) was proposed. This paper further suppresses the overestimation bias of values for multi-degree of freedom (DOF) manipulator learning based on deep reinforcement learning. Twin Delayed Deep Deterministic Policy Gradient with Rebirth Mechanism (RTD3) was proposed. The experimental results show that RTD3 applied to multi degree freedom manipulators is in place, with an improved learning ability by 29.15% on the basis of TD3. In this paper, a step-by-step reward function is proposed specifically for the learning and innovation of the multi degree of freedom manipulator’s motion ability. The view of continuous decision-making and process problem is used to guide the learning of the manipulator, and the learning efficiency is improved by optimizing the playback of experience. In order to measure the point-to-point position motion ability of a manipulator, a new evaluation index based on the characteristics of the continuous decision process problem, energy efficiency distance, is presented in this paper, which can evaluate the learning quality of the manipulator motion ability by a more comprehensive and fair evaluation algorithm.

Laparoscopic surgical device with modular end tools for real-time endomicroscopy and therapy.

In recent years, laparoscopic surgery attracts considerable attention for its distinct advantages owing to minimized invasiveness. However, traditional surgical instruments have a single function and lack the ability to provide high-resolution images for real-time tissue assessment. Thus, it remains a challenge to perform complex procedures with the traditional surgical instruments. In this paper, a novel laparoscopic surgical device for real-time endomicroscopy and therapy is proposed. The device is integrated with a visualization unit to provide endoscopic images in a constrained cavity for real-time observation. Moreover, modular end tools can be assembled and switched with the multi-DOF manipulator. The experimental results demonstrate that the surface-scanning tool can perform smooth scanning and obtain a large area mosaic of 6.99mm2 in 40.2s for tissue assessment. The spring-loaded forceps can generate an output force of 1.47N to manipulate tissue. These experimental results demonstrate the potential value of the device for intraoperative endomicroscopy-guided surgery and robot-assisted surgery.

Development of Multi-DOF Load Compensation Manipulator with Capability to Adjust the Compensation Force in Arbitrary Heights

Development of Multi-DOF Load Compensation Manipulator with Capability to Adjust the Compensation Force in Arbitrary Heights

Virtual Decomposition Based Modeling for Multi-DOF Manipulator With Flexible Joint

This paper discusses a problem that has plagued researchers for a long time regarding the dynamic modeling of a multiple degree-of-freedom (multi-DOF) manipulator such that its manipulation exhibits a higher computational efficiency and accuracy. Unknown friction, unknown gravitational torque, an uncertain moment of inertia, and severe joint coupling are the primary disturbing factors in multi-DOF manipulator modeling. In addition, joint flexible problems caused by the integration of harmonic drives increase the modeling complexity. Hitherto, no effective method has been found to address these problems. The virtual decomposition (VD)-based method exhibits the advantages of joint dynamics decoupling and minor computation compared with the traditional Lagrangian formulation or Newton-Euler formulation. In this study, an estimation method for the deformation-related torque of harmonic drives is established based on a novel experimental model; subsequently, this method is utilized in the VD-based model for the multi-DOF manipulator. Hence, the decoupling dynamic model for the manipulator considering joint flexibility is established. The performance of this new method has been evaluated by a contrast simulation with the Newton-Euler formulation, and the multi-DOF manipulator control simulation and experiment have been conducted with a VD-based model as a feedforward compensator to verify its performance in real-time control. The results demonstrated the validity and efficiency of e proposed approach.

Novel method for six-legged robots turning valves based on force sensing

According to previous studies, usually multi-degree-of-freedom (DOF) manipulators are installed on robots to perform the valve turning task, and vision systems are used to detect the valve data. However, vision systems are limited in the environment with less visibility and the internal force always occurs as the multi-DOF manipulator is compelled to follow the valve exactly by a firm grasp. In this paper, a novel valve turning method based on force sensing is proposed and implemented to the six-legged robot. Firstly, the method can enable the robot to judge the valve position through force sensing in the case of poor vision situation. Moreover, a 0-DOF tool is installed on the body of the robot. By integrating with the body, the tool has enough workspace to perform the operation task. Compared with the multi-DOF manipulator, loose grasp achieved by the 0-DOF tool helps to reduce the internal force during the valve turning process. In addition, the real-time trajectory planning strategy is designed for the robot. Based on the above initiatives, experiments are carried out to demonstrate effectiveness of the method.

Novel Door-opening Method for Six-legged Robots Based on Only Force Sensing

Current door-opening methods are mainly developed on tracked, wheeled and biped robots by applying multi-DOF manipulators and vision systems. However, door-opening methods for six-legged robots are seldom studied, especially using 0-DOF tools to operate and only force sensing to detect. A novel door-opening method for six-legged robots is developed and implemented to the six-parallel-legged robot. The kinematic model of the six-parallel-legged robot is established and the model of measuring the positional relationship between the robot and the door is proposed. The measurement model is completely based on only force sensing. The real-time trajectory planning method and the control strategy are designed. The trajectory planning method allows the maximum angle between the sagittal axis of the robot body and the normal line of the door plane to be 45º. A 0-DOF tool mounted to the robot body is applied to operate. By integrating with the body, the tool has 6 DOFs and enough workspace to operate. The loose grasp achieved by the tool helps release the inner force in the tool. Experiments are carried out to validate the method. The results show that the method is effective and robust in opening doors wider than 1 m. This paper proposes a novel door-opening method for six-legged robots, which notably uses a 0-DOF tool and only force sensing to detect and open the door.

Lightweight Multi-DOF Manipulator With Wire-Driven Gravity Compensation Mechanism

Energy consumption is a problem for robotic manipulators, which are popular in manufacturing, assembly, and process handling. Some of the most significant energy consumption comes from the weight of the large moving mass of the manipulator. This paper proposes a new multi-degree-of-freedom (DOF) manipulator with a wire-driven gravity compensation mechanism. The most significant problem of a wire system is the durability of the wire, which is improved in this study by using a multiwinding mechanism. The tension of the wire is reduced in proportion to the number of windings. Timing belts are used to design a multi-DOF manipulator and the lightweight gravity compensation mechanism reduces inertia forces during operation. Static analysis and design issues are presented. A robotic prototype is assembled and the torque-reduction performance is verified experimentally. The proposed mechanism can be applied to industrial manipulators for efficient robotic operation.

多自由度マニピュレータによる関節負荷に基づく作業空間の学習

多自由度マニピュレータによる関節負荷に基づく作業空間の学習

A Novel Design of Manipulator Arm Control System Based on STM32

This paper presents a novel design of multi dof and multifunctional manipulator arm control system. It uses 32 bit microcontroller STM32 as the core control unit, by controlling the pulse width modulation to control the manipulator arm of the steering gear. It consists of a mechanical model, the control core module, operation module, power supply module. The manipulator arm can greatly improve labor productivity, accelerate the realization of industrial production mechanization and automation, it can replace human labor in order to realize the mechanization and automation of production, and protect the personal safety in harmful environment. It has the advantages of light weight, simple operation, and low production cost etc.

Decoupling Strategy for Position and Force Control Based on Modal Space Disturbance Observer

This paper extends the diagonalization method on the basis of the modal space disturbance observer (MDOB) for application to a multidegree-of-freedom (DOF) system. The aim of this method is to suppress the interference between the position and force control systems and realize a bilateral control system. The utility of the proposed method is experimentally verified by using a multi-DOF manipulator. It is confirmed that the MDOB-based decoupling method has better performance than oblique coordinate control. Conventional oblique coordinate control causes oscillation in cases where the modeling error is large and the cutoff frequency of an observer is not high enough to change the system dynamics. On the other hand, the MDOB-based decoupling method becomes unstable when the difference in mass is large.