Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

Nonconvex Noise-Tolerant Neural Model for Repetitive Motion of Omnidirectional Mobile Manipulators

Dear Editor,Quadratic programming problems(QPs)receive a lot of attention in various fields of science computing and engineering applications,such as manipulator control[1].Recursive neural network(RNN)is considered to be a powerful QPs solver due to its parallel processing capability and feasibility of hardware implementation[2].

A Survey of Underwater Multi-Robot Systems

As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent decades.In this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new functions.More specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their combination.Further,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of MRS.To conclude,we have discussed related promising directions for future research.This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.

Fault-Tolerant Control of a CPG-Governed Robotic Fish

Fault tolerance is essential for the maneuverability of self-propelled biomimetic robotic fish in real-world aquatic applications.This paper explores the fault-tolerance control problem of a free-swimming robotic fish with multiple moving joints and a stuck tail joint.The created control system is composed of two main components:a feedback controller and a feedforward compensator.Specifically,the bio-inspired central pattern generator-based feedback controller is designed to make the robotic fish robust to external disturbances,while the feedforward compensator speeds up the convergence of the overall control system.Simulations are performed for control system analysis and performance validation of the faulty robotic fish.The experimental results demonstrate that the proposed fault-tolerant control method is able to effectively regulate the faulty robotic fish,allowing it to complete the desired motion in the presence of damage and thereby improving both the stability and the lifetime of the real robotic system.

Development and Control of Underwater Gliding Robots:A Review

As one of the most effective vehicles for ocean development and exploration,underwater gliding robots(UGRs)have the unique characteristics of low energy consumption and strong endurance.Moreover,by borrowing the motion principles of current underwater robots,a variety of novel UGRs have emerged with improving their maneuverability,concealment,and environmental friendliness,which significantly broadens the ocean applications.In this paper,we provide a comprehensive review of underwater gliding robots,including prototype design and their key technologies.From the perspective of motion characteristics,we categorize the underwater gliding robots in terms of traditional underwater gliders(UGs),hybrid-driven UGs,bio-inspired UGs,thermal UGs,and others.Correspondingly,their buoyancy driven system,dynamic and energy model,and motion control are concluded with detailed analysis.Finally,we have discussed the current critical issues and future development.This review offers valuable insight into the development of next-generation underwater robots well-suited for various oceanic applications,and aims to gain more attention of researchers and engineers to this growing field.

TibetanGoTinyNet:a lightweight U-Net style network for zero learning of Tibetan Go

The game of Tibetan Go faces the scarcity of expert knowledge and research literature.Therefore,we study the zero learning model of Tibetan Go under limited computing power resources and propose a novel scaleinvariant U-Net style two-headed output lightweight network TibetanGoTinyNet.The lightweight convolutional neural networks and capsule structure are applied to the encoder and decoder of TibetanGoTinyNet to reduce computational burden and achieve better feature extraction results.Several autonomous self-attention mechanisms are integrated into TibetanGoTinyNet to capture the Tibetan Go board’s spatial and global information and select important channels.The training data are generated entirely from self-play games.TibetanGoTinyNet achieves 62%–78%winning rate against other four U-Net style models including Res-UNet,Res-UNet Attention,Ghost-UNet,and Ghost Capsule-UNet.It also achieves 75%winning rate in the ablation experiments on the attention mechanism with embedded positional information.The model saves about 33%of the training time with 45%–50%winning rate for different Monte–Carlo tree search(MCTS)simulation counts when migrated from 9×9 to 11×11 boards.Code for our model is available at https://github.com/paulzyy/TibetanGoTinyNet.

Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

Grasp detection plays a critical role for robot manipulation.Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency.However,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are neglected.It is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains unsolved.Meanwhile,the channel information in high-level feature is also not well mined.Inevitably,the performance of grasp detection is degraded.To solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle residual.Both low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature fusion.Such a hierarchical fusion guarantees the quality of grasp prediction.Furthermore,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective branches.By such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the network.Besides,an information enhancement module is added before the encoder to reinforce input information.The proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

A survey of the pursuit-evasion problem in swarm intelligence

For complex functions to emerge in artificial systems,it is important to understand the intrinsic mechanisms of biological swarm behaviors in nature.In this paper,we present a comprehensive survey of pursuit–evasion,which is a critical problem in biological groups.First,we review the problem of pursuit–evasion from three different perspectives:game theory,control theory and artificial intelligence,and bio-inspired perspectives.Then we provide an overview of the research on pursuit–evasion problems in biological systems and artificial systems.We summarize predator pursuit behavior and prey evasion behavior as predator–prey behavior.Next,we analyze the application of pursuit–evasion in artificial systems from three perspectives,i.e.,strong pursuer group vs.weak evader group,weak pursuer group vs.strong evader group,and equal-ability group.Finally,relevant prospects for future pursuit–evasion challenges are discussed.This survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios.

Motion Control and Motion Coordination of Bionic Robotic Fish： A Review

Fish＇s outstanding motion and coordination performance make it an excellent source of inspiration for scientists and engineers aiming to design and control next-generation autonomous underwater vehicles within the framework of bionics. This paper offers a general review of the current status of bionic robotic fish, with particular emphasis on the hydrodynamic modeling and testing, kinematic modeling and control, learning and optimization, as well as motion coordination control. Among these aspects, representative studies based on ideas and concepts inspired from fish motion and coordination are discussed. At last, the major challenges and the future research directions are addressed in the context of integration of various research streams from ichthyologic, hydrodynamic, mechanical, electronic, control, and artificial intelligence. Further development of bionic robotic fish can be utilized to execute some specific missions in complex underwater environments, where operations are unsafe or impractical for divers or conventional underwater vehicles.

Kinematic Comparison of Forward and Backward Swimming and Maneuvering in a Self-Propelled Sub-Carangiform Robotic Fish

We make a thorough kinematic comparison of forward and backward swimming and maneuvering on a self-propelled robot platform that uses sub-carangifbrm swimming as the primary propulsor. An improved Central Pattern Generator （CPG） model allowing free adjustment of phase relationship and directional bias is employed to achieve flexible swimming and smooth transition. Considering the characteristics of forward swimming in carangiform fish and backward swimming in anguilliform fish, various backward swimming patterns for the sub-carangiform robotic fish are suitably created by reversing the direction of propagating propulsive waves. Through a combined use of the CPG control and closed-loop swimming direction control strategy, flexible and precise turning maneuvers in both forward and backward swimming are implemented and compared. By contrast with forward swimming, backward swimming requires a higher frequency or an increased lateral displacement to reach the same relative swimming speed. Noticeably, the phase difference shows a greater impact on forward swimming than on backward swimming. Our observations also indicate that the robotic fish achieves a larger turning rate in forward maneuvering than in backward maneuvering, yet these two maneuvers display comparable turning precision.

A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot

To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory.We propose a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object detection.Specifically,the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection.With the purpose of further improvement on the detection accuracy,YOLOv4 is transformed into a four-scale detection method.To improve the detection speed,model pruning is applied to the new model.By virtue of the improved detection methods,the robot can collect garbage autonomously.The detection speed is up to 66.67 frames/s with a mean average precision(mAP)of 95.099%,and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

Energy Analysis of a CPG-controlled Miniature Robotic Fish

Bionic robotic fish has a significant impact on design and control of innovative underwater robots capable of both rapid swimming and high maneuverability. This paper explores the relationship between Central Pattern Generator （CPG） based locomotion control and energy consumption of a miniature self-propelled robotic fish. To this end, a real-time energy measurement system compatible with the CPG-based locomotion control is firstly built on an embedded system. Then, tests are conducted on the untethered actual robot. The results indicate that different CPG feature parameters involving amplitude, frequency, and phase lag play distinct roles in energy consumption under different swimming gaits. Specifically, energy consumption is positively correlated with the changes in the amplitude and frequency of CPGs, whereas the phase lag of CPGs has little influence on the energy consumption. It may offer important inspiration for improving energy efficiency and locomotion performance of versatile swimming gaits.

Hydrodynamic Analysis and Verification of an Innovative Whale Shark-like Underwater Glider

This paper presents an innovative design for a biomimetic whale shark-like underwater glider aiming at the combination of high maneuverability and long duration.As a hybrid of the underwater glider and the robotic fish,its pectoral fins and tail can serve as not only the external control surfaces for attitude regulation during gliding but also the propellers for agile fish-like swimming mode.To verify the gliding capability of the whale shark-like glider and prepare for future dynamic analysis,the hydrodynamic coefficients,including drag,lift,sliding force,and corresponding moments are estimated through computational fluid dynamics method.In addition,the hydrodynamic analyses of the proposed glider and an equivalent conventional glider during steady gliding motion are executed for comparison.Extended experiments are performed to verify the downward gliding performance.The results reveal that the whale shark-like glider has less drag as well as higher lift-to-drag ratio and a markable gliding capability in practice.It may offer important inspiration for improving the gliding efficiency and performance of an underwater glider in biomimetic shape design.

A novel robotic visual perception framework for underwater operation

Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual perception.In addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across time.In response to these two problems,we present a design for a novel robotic visual perception framework.First,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection performance.As a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain shift.Moreover,non-reference assessments are proposed for detection continuity and stability based on object tracklets.Further,online tracklet refinement is developed to improve the temporal performance of detectors.Finally,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is established.Small-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and tracking.Extensive experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed approaches.The codes are available at https://github.com/yrqs/VisPerception.

Dynamic Modeling and Hybrid Fireworks Algorithm-Based Path Planning of an Amphibious Robot

The task of path planning in amphibious environments requires additional consideration due to the complexity of the amphibious environments.This paper presents a path planning method for an amphibious robot named\AmphiRobot"with its dynamic constraints considered.First,an explicit dynamic model using Kane's method is presented.The hydrodynamic parameters are obtained through computational°uid dynamics simulations.Furthermore,a path planning method based on a hybrid¯reworks algorithm is proposed,combining the¯reworks algorithm and bare bones¯reworks algorithm,aiming at the amphibious robot's characteristics of multiple motion modes and working environments.The initially planned path is then smoothed using Dubins path under constraints determined by the dynamic model.Simulation reveals that the performance of the hybrid¯reworks algorithm approach is better than the¯reworks algorithm and bare bones¯reworks algorithm is applied separately in the amphibious environment scenarios.

NA-CPG: A robust and stable rhythm generator for robot motion control

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic rhythms.However,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological relationships.Moreover,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot motion.Therefore,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are proposed.NA-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output waveforms.Thus,it can effectively cope with large parameter changes to avoid network oscillations and divergence.To optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further proposed.Meanwhile,a biomimetic robotic fish is illustrated to realize the whole optimization process.Simulations demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,respectively.The proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

Editorial for the Special Issue on Biomimetic Multi-domain Rhythmic Motions

Nature is always an unfailing source of inspiration.Biomimetic intelligence and robotics are increasingly changing our daily lives.With recent advances in biology,material,microelectronics,pattern recognition,and control theory,biomimetic robots are gaining unprecedented mobility,perception,and collaboration ability,which significantly expands their working domains.In particular,pioneer researches on multi-domain rhythmic motions are leading a brand-new technological revolution.Since multi-domain operation requires the robots to agilely move,adaptively perceive,and widely learn in various environments,advances in actuators,sensors,communication and computation provide a solid foundation for the implementation of multi-domain rhythmic motions of biomimetic robots.In continuous endeavor to nurture the robotics innovation,researches on biomimetic multi-domain rhythmic motions will offer ample prospects for industrial robots,service robots as well as special robots.