Decentralized adaptive control of robotic manipulators†

We develop a self-tuning-type adaptive control for robotic manipulators with six joints, and analyse the stability of the control system. In order to reduce the computational effort for obtaining the control input in real time, a decentralized adaptive control system is designed in which each joint of the manipulator is regarded as a subsystem and controlled independently in parallel. In this control system we regard the interaction among the multiple joints as an unknown input in each subsystem. The influence of the interaction on the stability of the overall control system is analysed using a Lyapunov function. The theoretical result obtained is that the control system is always stable in the sense that the error between the output of the adaptive predictor and the real output (angular velocity of each joint) will not exceed the amount of interaction. This result is verified by some simulation studies.     

Towards an Esox lucius inspired multimodal robotic fish

In this paper, we further explore multimodal locomotion via an updated robotic fish model based on Esox lucius. Besides the improved actuation properties like higher torque servomotors and powerful electronics,the robotic fish has some innovative mechanical design to pursue diverse swimming modes and superior performance. Specifically, we introduced a ±50° yawing head joint that functions as the neck for enhancing turning ability. A pair of pectoral mechanisms with two DOFs per fin is constructed to achieve 3-D swimming and to enrich multiple pectoral motions. At the control level, an improved central pattern generator(CPG) model allowing for free adjustment of the phase relationship among outputs is employed to produce rhythmic signals of multimodal swimming. Extensive experiments were carried out to examine how characteristic parameters in CPGs including amplitude, frequency, and phase lag affect the swimming performance. As a result, the robotic fish successfully performed various locomotion actions such as forward swimming, backward swimming, turning, diving, surfacing, as well as three pectoral motions in the form of pitching, heaving, and heaving-pitching.We found that small phase lag between oscillating joints which means large propulsive body wave length and undulation width could lead to a faster swimming in body and/or caudal fin(BCF) locomotion.     

Control of robotic manipulators—a neural network approach

A neural-network-based scheme is used for the control of a robotic manipulator. The main idea is that, by using a neural network to learn the characteristics of the robot system (or specifically its inverse dynamics), accurate trajectory following and good performance results are obtained. However, the traditional back-propagation algorithm commonly used for control and identification of nonlinear systems suffers from a slow rate of convergence. We investigate the effect of adusting the slope of the activation function (the node nonlinearity) on the performance of a back-propagation algorithm. It is shown that learning speed is increased significantly by making the slope of non-linearity adaptive. The results demonstrate that the proposed method gives better error minimization and faster convergence. The suggested method is applied to a two-link robotic manipulator. The resulting controller is sufficiently robust with respect to the changing conditions.     

Contact identification for assembly–disassembly simulation with a haptic device

Assembly/Disassembly (A/D) simulations using haptic devices are facing difficulties while simulating insertion/extraction operations, such as removing cylinders from holes. In order to address this configuration as well as others, an approach based on contact identification between components is presented in this paper. This approach can efficiently contribute either to a new A/D simulation preparation process relying on two types of shape representations (mesh and CAD NURBS models), or directly to the real time simulation process when it is performed with 6D haptic devices. The model processing pipeline is described and illustrated to show how information can be propagated and used for contact detection. Then, the contact identification process is introduced and illustrated through an example.

Human–robot collaborative partial destruction disassembly sequence planning method for end-of-life product driven by multi-failures

Partial destructive disassembly (PDD) of large complex products often requires human–robot collaborative disassembly (HRCD). Therefore, a human–robot collaboration partial destructive disassembly sequence planning (HRCPDDSP) method of end-of-life products driven by multiple failures is proposed to obtain the optimal disassembly sequence to improve disassembly efficiency and degree of automation. Based on the product disassembly priority and part failure characteristics, an information model of the HRCPDDSP method is constructed. Furthermore, the model is expressed as a constraint matrix and recycling decision matrix. A multi-layer chromosome coding method, including HRCD, destructive constraint, and node layers, is proposed of the characteristics of the HRCPDDSP method. The approximate optimal sequence of human–robot collaboration PDD is achieved by improving evolutionary mechanisms such as selection, crossing, and mutation. Finally, the model and algorithm are applied to solve a case of HRCPDDSP, and the feasibility and effectiveness of the proposed method are further verified by comparison with other disassembly modes.     

Swarm robotic network using Lévy flight in target detection problem

One approach in swarm robotics is homogeneous system which is embedded with sensing, computing, mobile and communication components. In this study, a target detection problem, which is one of navigation problems, was employed. Once a robot detects a target, robots immediately communicate with a base station via intermediate relay robots due to the multi-hop transmission of wireless communication. Therefore, this control task is completed with connectivity of the network. In a target detection problem, we must improve the performance of exploration as well as connectivity of the network. This study investigates the performances of the two types of random walk algorithm in navigation while loosely ensuring connectivity of the robotic network based on our previous study.     

Comments on ‘A new terminal sliding mode control for robotic manipulators’

In this article, we give some comments on the article ‘A new terminal sliding mode control for robotic manipulators’. The article presents a new terminal sliding mode control approach for global finite-time tracking of robotic manipulators. We point out a serious error occurred through the article, leading to the ineffectiveness of the proposed approach. A correction is proposed. Comparisons are presented.     

Hybrid position–admittance realization of an adaptive output super-twisting controller for a robotic scalpel

This study proposes the design and implementation of a hybrid robust automatic controller based on the application of a high order sliding mode algorithm for a robotic scalpel prototype (RS). Two fully actuated arms with three degrees of freedom constitute the RS, one arm holds the sample and the second one has the scalpel to exert the cutting task. Each arm is attached to its corresponding cartesian robotic platform. The available measurements are the angular displacements, the linear displacement and the force vector describing the interaction between the scalpel and the biological sample. A hybrid position–admittance controller implements an output-based adaptive distributed super-twisting algorithm to mobilize the RS. A high order sliding mode observer estimates the unknown angular and linear velocities that were used in the hybrid controller. Once the end-effector of each arm reaches the desired cutting position, the designed controller switches to the admittance controller to avoid damaging the surrounding tissue. Numerical simulations show the advantages of the suggested controller in comparison with classical algorithms. The hybrid sliding mode admittance controller has been successfully evaluated on an self-constructed platform. The experimental results show a precise cut and efficient mobilization of the RS compared to other classical controllers such as proportional-differentiator, proportional-integral and first order sliding mode controllers.     

Infinity-norm acceleration minimization of robotic redundant manipulators using the LVI-based primal–dual neural network

Kinematically redundant manipulators admit an infinite number of inverse kinematic solutions and hence the optimization of different performance measures corresponding to various task requirements must be considered. Joint accelerations of these mechanisms are usually computed by optimizing various criteria defined using the two-norm of acceleration vectors in the joint space. However, in formulating the optimization measures for computing the inverse kinematics of redundant arms, this paper investigates the use of the infinity norm of joint acceleration (INAM) (also known as the minimum-effort solution). The infinity norm of a vector is its maximum absolute value component and hence its minimization implies the determination of a minimum-effort solution as opposed to the minimum-energy criterion associated with the two-norm. Moreover, the new scheme reformulates the task as the online solution to a quadratic programming problem and incorporates three levels of joint physical limits, thus keeping the acceleration within a given range and avoiding the torque-instability problem. In addition, since the new scheme adopts the LVI-based primal–dual neural network, it does not entail any matrix inversion or matrix–matrix multiplication, which was embodied in other's researches with expensive O(n³)$O(n^3)$ operations. This new proposed QP-based dynamic system scheme is simulated based on the PUMA560 robot arm.     

Going into the wild in child–robot interaction studies: issues in social robotic development

As robots move into more human centric environments we require methods to develop robots that can naturally interact with humans. Doing so requires testing in the real-world and addressing multidisciplinary challenges. Our research is focused on child–robot interaction which includes very young children, for example toddlers, and children diagnosed with autism. More traditional forms of human–robot communication, such as speech or gesture recognition, may not be appropriate with these users, where as touch may help to provide a more natural and appropriate means of communication for such instances. In this paper, we present our findings on these topics obtained from a project involving a spherical robot that acquires information regarding natural touch from analysing sensory patterns over-time to characterize the information. More specifically, from this project we have derived important factors for future consideration, we describe our iterative experimental methodology of testing in and out of the ‘wild’ (lab based and real world), and outline discoveries that were made by doing so.     

Adaptive H2/H∞ tracking control for a class of uncertain robotic systems

This paper addresses the problem of designing mixed H₂/H_∞ tracking control for a large class of uncertain robotic systems. Nonlinear H_∞ control theory, H₂ control theory and intelligent adaptive control algorithm are combined to construct a hybrid adaptive/robust H₂/H_∞ tracking control scheme. One adaptive neural network system is constructed to approximate the behaviour of uncertain robot dynamics, and the other adaptive control algorithm is designed to estimate the behaviour of the modelled disturbance. Moreover, a robust H_∞ control algorithm is designed to attenuate the effects of the unmodelled disturbance. Only a set of algebraic matrix Riccati-like equations is required to implement the proposed mixed H₂/H_∞ tracking controller, and so an explicit and closed-form solution is obtained. Consequently, the mixed H₂/H_∞ adaptive/robust tracking controller developed here can be analytically computed and easily implemented. Finally, simulations are presented to illustrate the effectiveness of the proposed control algorithm.     

Design and performance evaluation of a biomimetic microrobot for the father–son underwater intervention robotic system

Underwater intervention is a favorite and difficult task for AUVs. To realize the underwater manipulation for the small size spherical underwater robot SUR-II, a father–son underwater intervention robotic system (FUIRS) is proposed in our group. The FUIRS employs a novel biomimetic microrobot to realize an underwater manipulation task. This paper describes the biomimetic microrobot which is inspired by an octopus. The son robot can realize basic underwater motion, i.e. grasping motion, object detection and swimming motion. To enhance the payload, a novel buoyancy force adjustment method was proposed which can provides 11.8 mN additional buoyancy force to overcome the weight of the object in water. Finally, three underwater manipulation experiments are carried out to verify the performance of the son robot. One is carried by swimming motion and buoyancy adjustment; the other two are only carried by buoyancy adjustment. And the experimental results show that the son robot can realize the underwater manipulation of different shape and size objects successfully. The swimming motion can reduce the time cost of underwater manipulation remarkably.     

Design of robotic arm’s action to imitate the mechanism of an animal’s consciousness

We are attempting to develop a system so that a user is able to let robots perform an intellectual action that has a healing and friendly feeling. Based on the development process of the actions and consciousness of animals, we constructed a structure model which connects consciousness and action hierarchically, built a valuation function for action selection, and developed software to control the action of a robot. This software is called Consciousness-Based Architecture (CBA). With it, our aim is to connect a user and robot as closely as possible and to allow smooth communications between them by developing an emotional system that takes notice of consciousness. In our system, the robotic arm’s finger is outfitted with a small Web camera, which allows the arm to recognize external information so that the robot can select various actions that comply with certain factors in the outside environment. Furthermore, by using the actuator of the robotic arm, the system we have built provides a correspondence between the robot’s internal states, such as the degree of rotation angle, and the outside temperature. In the present study, a motivation model which considers the outside environment and the internal states has been built into the CBA, and the behavior of the robotic arm has been verified.     

Autonomous robotic exploration using a utility function based on Rényi’s general theory of entropy

In this paper we present a novel information-theoretic utility function for selecting actions in a robot-based autonomous exploration task. The robot’s goal in an autonomous exploration task is to create a complete, high-quality map of an unknown environment as quickly as possible. This implicitly requires the robot to maintain an accurate estimate of its pose as it explores both unknown and previously observed terrain in order to correctly incorporate new information into the map. Our utility function simultaneously considers uncertainty in both the robot pose and the map in a novel way and is computed as the difference between the Shannon and the Rényi entropy of the current distribution over maps. Rényi’s entropy is a family of functions parameterized by a scalar, with Shannon’s entropy being the limit as this scalar approaches unity. We link the value of this scalar parameter to the predicted future uncertainty in the robot’s pose after taking an exploratory action. This effectively decreases the expected information gain of the action, with higher uncertainty in the robot’s pose leading to a smaller expected information gain. Our objective function allows the robot to automatically trade off between exploration and exploitation in a way that does not require manually tuning parameter values, a significant advantage over many competing methods that only use Shannon’s definition of entropy. We use simulated experiments to compare the performance of our proposed utility function to these state-of-the-art utility functions. We show that robots that use our proposed utility function generate maps with less uncertainty and fewer visible artifacts and that the robots have less uncertainty in their pose during exploration. Finally, we demonstrate that a real-world robot using our proposed utility function is able to successfully create a high-quality map of an indoor office environment.     

Recent progress in tactile sensing and sensors for robotic manipulation: can we turn tactile sensing into vision?1

This paper surveys recently published literature on tactile sensing in robotic manipulation to understand effective strategies for using tactile sensing and the issues involved in tactile sensing. It consists of a brief review of existing tactile sensors for robotic grippers and hands, review of modalities available from tactile sensing, review of the applications of tactile sensing in robotic manipulations, and discussion of the issues of tactile sensing and an approach to make tactile sensors more useful. We emphasize vision-based tactile sensing because of its potential to be a good tactile sensor for robots.     

Systems, control models, and codec for collaborative observation of remote environments with an autonomous networked robotic camera

Networked robotic cameras are becoming popular in remote observation applications such as natural observation, surveillance, and distance learning. Equipped with a high optical zoom lens and agile pan-tilt mechanisms, a networked robotic camera can cover a large region with various resolutions. The optimal selection of camera control parameters for competing observation requests and the on-demand delivery of video content for various spatiotemporal queries are two challenges in the design of such autonomous systems. For camera control, we introduce memoryless and temporal frame selection models that effectively enable collaborative control of the camera based on the competing inputs from in-situ sensors and users. For content delivery, we design a patch-based motion panorama representation and coding/decoding algorithms (codec) to allow efficient storage and computation. We present system architecture, frame selection models, user interface, and codec algorithms. We have implemented the system and extensively tested our design in real world applications including natural observation, public surveillance, distance learning, and building construction monitoring. Experiment results show that our frame selection models are robust and effective and our on-demand content delivery codec can satisfy a variety of spatiotemporal queries efficiently in terms of computation time communications bandwidth.

Precise planar motion measurement of a swimming multi-joint robotic fish一种面向多关节机器鱼平面运动的精确测量方法

This paper presents a method for planar motion measurement of a swimming multi-joint robotic fish. The motion of the robotic fish is captured via image sequences and a proposed tracking scheme is employed to continuously detect and track the robotic fish. The tracking scheme initially acquires a rough scope of the robotic fish and thereafter precisely locates it. Historical motion information is utilized to determine the rough scope, which can speed up the tracking process and avoid possible ambient interference. A combination of adaptive bilateral filtering and k-means clustering is then applied to segment out color markers accurately. The pose of the robotic fish is calculated in accordance with the centers of these markers. Further, we address the problem of time synchronization between the on-board motion control system of the robotic fish and the motion measurement system. To the best of our knowledge, this problem has not been tackled in previous research on robotic fish. With information about both the multi-link structure and motion law of the robotic fish, we convert the problem to a nonlinear optimization problem, which we then solve using the particle swarm optimization (PSO) algorithm. Further, smoothing splines are adopted to fit curves of poses versus time, in order to obtain a continuous motion state and alleviate the impact of noise. Velocity is acquired via a temporal derivative operation. The results of experiments conducted verify the efficacy of the proposed method.     

Infiltrating the zebrafish swarm: design,implementation and experimental tests of a miniature robotic fish lure for fish–robot interaction studies

Robotic fish are nowadays developed for various types of research, such as bio-inspiredrobotics, biomimetics and animal behavior studies. In the context of our research on the social interactions of the zebrafish Danio Rerio, we developed a miniature robotic fish lure for direct underwater interaction with the living fish. This remotely controlled and waterproof device has a total length of 7.5 cm with the same size ratio as zebrafish and is able to beat its tail with different frequencies and amplitudes, while following the group of living animals using a mobile robot moving outside water that is coupled with the robotic lure using magnets. The robotic lure is also equipped with a rechargeable battery and can be used autonomously underwater for experiments of up to 1 h. We performed experiments with the robot moving inside an aquarium with living fish to analyze its impact on the zebrafish behavior. We found that the beating rate of the tail increased the attractiveness of the lure among the zebrafish shoal. We also demonstrated that the lure could influence a collective decision of the zebrafish shoal, the swimming direction, when moving with a constant linear speed inside a circular corridor. This new robotic fish design and the experimental results are promising for the field of fish–robot interaction.     

Dynamic modeling of tree-type robotic systems by combining $3\times3$ rotation and 4×4$4\times4$ transformation matrices

The objective of this article is to present a systematic method of deriving the mathematical formulation for the oblique impact of a tree-type robotic manipulator. The dynamic response of this system (confined within a curved-wall environment) is expressed by two diverse models. A set of differential equations is employed to obtain the dynamic behavior of the system when it has no contact with any object in its environment (flying phase), and a set of algebraic equations is used to describe the collision of the system with the curved walls (impact phase). The Gibbs–Appell formulation in recursive form and the Newton’s impact law are utilized to derive the governing equations of this robotic system for the flying and impact phases, respectively. The main innovation of this article is the development of an automatic approach based on the combination of \(3\times3\) rotation and \(4\times4\) transformation matrices. In fact, this is the first time the merits of \(3\times3\) rotation matrices (i.e., improving the computational efficiency of the developed algorithm) have been merged with the capabilities of \(4\times4\) transformation matrices (i.e., deriving more compact motion equations by combining rotations with translations). Finally, a case study involving a tree-type robotic system with 12 degrees of freedom has been simulated to show the efficiency of the proposed dynamic modeling.