Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects

This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure,control and decision-making, and perception and interaction, a holistic overview of the current state of humanoid robot research is presented. Furthermore, emerging challenges in the field are identified, emphasizing the necessity for a deeper understanding of biological motion mechanisms, improved structural design,enhanced material applications, advanced drive and control methods, and efficient energy utilization. The integration of bionics, brain-inspired intelligence, mechanics, and control is underscored as a promising direction for the development of advanced humanoid robotic systems. This paper serves as an invaluable resource, offering insightful guidance to researchers in the field,while contributing to the ongoing evolution and potential of humanoid robots across diverse domains.

基于Robotstudio的工业机器人课程教学设计与实践

阐述工业机器人实训课程教学实践,将Robotstudio软件融入课程教学中,探讨以实例项目驱动的教学内容设计,以虚实结合手段提升课堂教学效果的教学模式,从而实现教学与实践的统一。

Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems

Traditional proportional-integral-derivative(PID)controllers have achieved widespread success in industrial applications.However,the nonlinearity and uncertainty of practical systems cannot be ignored,even though most of the existing research on PID controllers is focused on linear systems.Therefore,developing a PID controller with learning ability is of great significance for complex nonlinear systems.This article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with uncertainties.The introduction of neural networks(NNs)overcomes the upper limit of the traditional PID feedback mechanism’s capability.The proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional coefficients.Under the partial persistent excitation(PE)condition,the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by NNs.Based on the acquired knowledge from the stable control process,a learning PID controller is developed to further improve overall control performance,while overcoming the problem of repeated online weight updates.Simulation studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.

Simulation Analysis of Deformation Control for Magnetic Soft Medical Robots

Dear Editor,This letter presents a biocompatible cross-shaped magnetic soft robot and investigates its deformation mode control strategy through COMSOL modeling and simulation.Magnetic soft robots offer novel avenues for precise treatment within intricate regions of the human body.

Oncologic outcomes with and without amniotic membranes in robotic-assisted radical prostatectomy:A propensity score matched analysis

Objective:Placement of human placenta derived grafts during robotic-assisted radical prostatectomy(RARP)hastens the return of continence and potency.The long-term impact on the oncologic outcomes remains to be investigated.Our objective was to determine the oncologic outcomes of patients with dehydrated human amnion chorion membrane(dHACM)at RARP compared to a matched cohort.Methods:In a referral centre,from August 2013 to October 2019,599 patients used dHACM in bilateral nerve-sparing RARP.We excluded patients with less than 12 months follow-up,simple prostatectomy,and unilateral nerve-sparing.Patients with dHACM(amnio group)were 529,and were propensity score matched 1:1 to 2465 patients without dHACM(non-amnio group)and a minimum follow-up of 36 months.At the time of RARP,dHACM was placed around the neurovascular bundle in the amnio group.Continuous and categorical variables in matched groups was tested by two-sample Kolmogorov-Smirnov test and Fisher's exact test respectively.Outcomes measured were biochemical recurrence(BCR),adjuvant and salvage therapy rates.Results:Propensity score matching resulted in two groups of 444 patients.Cumulative incidence functions for BCR did not show a difference between the groups(p=0.3).Patients in the non-amnio group required salvage therapy more frequently than the amnio group,particularly after partial nerve-sparing RARP(6.3%vs.2.3%,p=0.001).Limitations are the absence of prospective randomization.Conclusion:The data suggest that using dHACM does not have a negative impact on BCR in patients.Outcomes of cancer specific and overall survival will require follow-up study to increase our understanding of these grafts’impact on prostate cancer biology.

Kinematic analysis of flexible bipedal robotic systems

In spite of its intrinsic complexities,the passive gait of bipedal robots on a sloping ramp is a subject of interest for numerous researchers.What distinguishes the present research from similar works is the consideration of flexibility in the constituent links of this type of robotic systems.This is not a far-fetched assumption because in the transient(impact)phase,due to the impulsive forces which are applied to the system,the likelihood of exciting the vibration modes increases considerably.Moreover,the human leg bones that are involved in walking are supported by viscoelastic muscles and ligaments.Therefore,for achieving more exact results,it is essential to model the robot links with viscoelastic properties.To this end,the Gibbs-Appell formulation and Newton's kinematic impact law are used to derive the most general form of the system's dynamic equations in the swing and transient phases of motion.The most important issue in the passive walking motion of bipedal robots is the determination of the initial robot configuration with which the system could accomplish a periodic and stable gait solely under the effect of gravitational force.The extremely unstable nature of the system studied in this paper and the vibrations caused by the impulsive forces induced by the impact of robot feet with the inclined surface are some of the very serious challenges encountered for achieving the above-mentioned goal.To overcome such challenges,an innovative method that uses a combination of the linearized equations of motion in the swing phase and the algebraic motion equations in the transition phase is presented in this paper to obtain an eigenvalue problem.By solving this problem,the suitable initial conditions that are necessary for the passive gait of this bipedal robot on a sloping surface are determined.The effects of the characteristic parameters of elastic links including the modulus of elasticity and the Kelvin-Voigt coefficient on the walking stability of this type of robotic systems are also studied.The findings of this parametric study reveal that the increase in the Kelvin-Voigt coefficient enhances the stability of the robotic system,while the increase in the modulus of elasticity has an opposite effect.

An Efficient Optimization Design Framework for Low-Resistance Shape of Bionic Amphibious Robot

To realize the low-resistance shape optimization design of amphibious robots,an efficient optimization design framework is proposed to improve the geometric deformation flexibility and optimization efficiency.In the proposed framework,the free-form deformation parametric model of the flat slender body is established and an analytical calculation method for the height constraints is derived.CFD method is introduced to carry out the high-precision resistance calculation and a constrained Kriging-based optimization method is built to improve the optimization efficiency by circularly infilling the new sample points which satisfying the constraints.Finally,the shape of an amphibious robot example is optimized to get the low-resistance shape and the results demonstrate that the presented optimization design framework has the advantages of simplicity,flexibility and high efficiency.

Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators.First,a fixed-time disturbance observer(FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems.Then an adaptive scheme is used and the adaptive FTDO(AFTDO) is developed,so that the priori knowledge of the lumped disturbance is not required.Further,a new non-singular fast terminal sliding mode(NFTSM) surface is designed by using an arctan function,which helps to overcome the singularity problem and enhance the robustness of the system.Based on the estimation of the lumped disturbance by the AFTDO,a fixed-time non-singular fast terminal sliding mode controller(FTNFTSMC)is developed to guarantee the trajectory tracking errors converge to zero within a fixed time.The settling time is independent of the initial state of the system.In addition,the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov method.Finally,the fixed-time NFESM(FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers(SMCs) are performed.The comparative results confirm that the FTNFTSMC has superior control performance.

Adaptive admittance tracking control for interactive robot with prescribed performance

An adaptive control approach is presented in this paper for tracking desired trajectories in interactive manipulators. The controller design incorporates prescribed performance functions (PPFs) to improve dynamic performance. Notably, the performance of the output error is confined in an envelope characterized by exponential convergence, leading to convergence to zero. This feature ensures a prompt response from admittance control and establishes a reliable safety framework for interactions. Simulation results provide practical insights,demonstrating the viability of the control scheme proposed in this paper.

Bionic lightweight design of limb leg units for hydraulic quadruped robots by additive manufacturing and topology optimization

Galloping cheetahs,climbing mountain goats,and load hauling horses all show desirable locomotion capability,which motivates the development of quadruped robots.Among various quadruped robots,hydraulically driven quadruped robots show great potential in unstructured environments due to their discrete landing positions and large payloads.As the most critical movement unit of a quadruped robot,the limb leg unit(LLU)directly affects movement speed and reliability,and requires a compact and lightweight design.Inspired by the dexterous skeleton–muscle systems of cheetahs and humans,this paper proposes a highly integrated bionic actuator system for a better dynamic performance of an LLU.We propose that a cylinder barrel with multiple element interfaces and internal smooth channels is realized using metal additive manufacturing,and hybrid lattice structures are introduced into the lightweight design of the piston rod.In addition,additive manufacturing and topology optimization are incorporated to reduce the redundant material of the structural parts of the LLU.The mechanical properties of the actuator system are verified by numerical simulation and experiments,and the power density of the actuators is far greater than that of cheetah muscle.The mass of the optimized LLU is reduced by 24.5%,and the optimized LLU shows better response time performance when given a step signal,and presents a good trajectory tracking ability with the increase in motion frequency.

Intuitive Human-Robot-Environment Interaction With EMG Signals:A Review

A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive interaction.However,it still has a gap between scientific research and real-life applications.Previous studies mainly focused on EMG decoding algorithms,leaving a dynamic relationship between the human,robot,and uncertain environment in real-life scenarios seldomly concerned.To fill this gap,this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction(HREI)systems.The general processing framework is summarized,and three interaction paradigms,including direct control,sensory feedback,and partial autonomous control,are introduced.EMG-based intention decoding is treated as a module of the proposed paradigms.Five key issues involving precision,stability,user attention,compliance,and environmental awareness in this field are discussed.Several important directions,including EMG decomposition,robust algorithms,HREI dataset,proprioception feedback,reinforcement learning,and embodied intelligence,are proposed to pave the way for future research.To the best of what we know,this is the first time that a review of EMG-based methods in the HREI system is summarized.It provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems,in which factors in human-robot interaction,robot-environment interaction,and state perception by human sensations are considered,which has never been done by previous studies.

Comment on“Robotic surgery and liver transplantation:A single-center experience of 501 robotic donor hepatectomies”

To the Editor:We read with great interest the article by Schulze et al.entitled“Robotic surgery and liver transplantation:A single-center experience of 501 robotic donor hepatectomies”[1].It is the first single-center report including over 500 fully robotic donor hepatectomies.For the donors,the overall complication rate was 6.4%(n=32).Postoperative self-limiting bleeding(0.4%)and bile leakage from the resection plane(1.8%)were rare.

Colorectal resections for malignancy: A pilot study comparing conventional vs freehand robot-assisted laparoscopic colectomy

BACKGROUND Laparoscopic colectomy is widely accepted as a safe operation for colorectal cancer,but we have experienced resistance to the introduction of the FreeHandffrobotic camera holder to augment laparoscopic colorectal surgery.AIM To compare the initial results between conventional and FreeHandffrobot-assisted laparoscopic colectomy in Trinidad and Tobago.METHODS This was a prospective study of outcomes from all laparoscopic colectomies per-formed for colorectal carcinoma from November 29,2021 to May 30,2022.The following data were recorded:Operating time,conversions,estimated blood loss,hospitalization,morbidity,surgical resection margins and number of nodes har-vested.All data were entered into an excel database and the data were analyzed using SPSS ver 20.0.RESULTS There were 23 patients undergoing colectomies for malignant disease:8(35%)FreeHandff-assisted and 15(65%)conventional laparoscopic colectomies.There were no conversions.Operating time was significantly lower in patients under-going robot-assisted laparoscopic colectomy(95.13±9.22 vs 105.67±11.48 min;P=0.045).Otherwise,there was no difference in estimated blood loss,nodal harvest,hospitalization,morbidity or mortality.CONCLUSION The FreeHandffrobot for colectomies is safe,provides some advantages over conventional laparoscopy and does not compromise oncologic standards in the resource-poor Caribbean setting.

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Robot calligraphy visually reflects the motion capability of robotic manipulators.While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works.The key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data hierarchically.In this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module.The robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with GANs.Then the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is evaluated.Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

Robotic-assisted retroperitoneal lymph node dissection for stage II testicular cancer

Objective:To evaluate the perioperative as well as early oncological outcomes of patients undergoing robotic retroperitoneal lymph node dissection for treatment of testicular cancer.Methods:We conducted a prospective consecutive case series of patients undergoing robotic assisted retroperitoneal lymph node dissection for metastatic testicular cancer between May 2018 and July 2021 at our institution.Data were collected on patient and tumour characteristics,intraoperative and postoperative parameters,and functional and oncological outcomes.Descriptive statistics are presented.Results:Nineteen patients were identified;18(94.7%)completed the procedure robotically and one was converted to open surgery;78.9%of patients had stage≥IIB and 12(63.2%)patients had undergone prior chemotherapy.The median operative time was 300(interquartile range[IQR]240-315)min.Median blood loss was 100(IQR 50-175)mL.Median length of stay was 2(range 1-11)days.All robotically completed patients commenced diet and passed flatus on Day 1 and were discharged by Day 3.The median lymph node yield was 40.5(IQR 38-51)nodes.All patients undergoing nerve-sparing procedures recovered antegrade ejaculatory function.One patient had a Clavien-Dindo III complication(chylous ascites requiring drainage).At a median follow-up of 22.3(IQR 16.3-24.9)months,one patient developed retroperitoneal recurrence,which was successfully treated with second-line chemotherapy;no other patients have had recurrences.Conclusion:Robotic retroperitoneal lymph node dissection is a safe and feasible alternative to open surgery in appropriately selected patients,offering low morbidity.Early oncological outcomes are promising.Larger cohorts and longer follow-ups are required to validate our institution's findings.

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.