Volume 10, Number 6, 2013
Special Issue on Robotics and Biomimetics: Part II (pp.487-551)
In this paper, we propose a new prediction from expert demonstration (PED) methodology to improve reliability and safety in tele-surgery. Data was collected from expert (clinician) demonstrations for the procedure of trocar insertion. We encoded a set of force, torque and penetration trajectories by using a Gaussian mixture model (GMM). A generalization of these profiles and associated parameters were retrieved by Gaussian mixture regression (GMR). We validated the proposed methodology for tele-robotic placement of the trocar in two stages. First, we tested the efficacy of the proposed PED approach for handling transmission error and latency. Our results showed that for the average case (12% packet error and 10% loss of packet), a 58.8% improvement in performance was obtained in comparison to using an extended Kalman filter. Next, we validated the methodology for surgical assistance on 15 participants. A haptic assistance mode was devised based on the proposed PED model to assist inexperienced operators to perform the procedure. The PED model was tested for instrument deviation, penetration force and penetration depth. Preliminary study results showed that participants with PED assistance performed the task with more consistency and exerted lesser penetration force than subjects without assistance.
Consciousness research has been of great concern to philosophers, psychologists and neuroscientists in recent years. At the same time, consciousness has also attracted more and more interest of artificial intelligence (AI) researchers. In order to make more intelligent machines, many computing models of machine consciousness have been presented. Furthermore, self-consciousness has relevance to the level of intelligent functions. Hence, it is necessary to study self-consciousness in AI. This thesis, starting from biological consciousness, discusses some viewpoints of machine consciousness. Based on the discussions, we present a way to emulate self-consciousness and test this method via simulation experiments. Our results indicate that self-consciousness, which belongs to organisms, can be imitated by machines.
An assistive robot is a novel service robot, playing an important role in the society. For instance, it can amplify human power not only for the elderly and disabled to recover/rehabilitate their lost/impaired musculoskeletal functions but also for healthy people to perform tasks requiring large forces. Consequently, it is required to consider both accurate position control and human safety, which is the compliance. This paper deals with the robot control compliance problem based on the QNX real-time operating system. Firstly, the mechanical structure of a compliant joint on the assistive robot is designed using Solidworks. Then the parameters of the assistive robot system are identified. The software of robot control includes data acquisition and processing, and control to meet the compliance requirement of the joint control. Finally, a Hogan impedance control experiment is carried out. The experimental results prove the effectiveness of the method proposed.
Controlling human-like robots with musculoskeletal structure has been a challenging problem in engineering. In biological studies, motor synergy hypothesis has been proposed as a solution in order to control high degree-of-freedom and complex human body. In this paper, we focus on exploring the applicability of motor synergies in generating goal-directed movements by optimal control in a human-like robotic arm. We focus on three problems: 1) Can motor synergies facilitate the solving of optimal control problem? 2) What properties should motor synergies have in order to achieve tasks? 3) How should motor synergies be utilized better? For the first problem we show that goal-directed movements can be achieved by utilizing motor synergies which have properties of achieving the goals. For the second problem, we testify motor synergies which have different properties and discover that energy efficiency is an important aspect in motor synergies which can also be utilized to achieve goal-directed movements. This discovery also implies that we can obtain motor synergies by other ways rather than from goal-directed optimal control signals. For the third problem, we show that the control complexity can be further reduced by utilizing a subset of motor synergies which are effective to achieve goals.
This paper presents a 3D path planning algorithm for an unmanned aerial vehicle (UAV) in complex environments. In this algorithm, the environments are divided into voxels by octree algorithm. In order to satisfy the safety requirement of the UAV, free space is represented by free voxels, which have enough space margin for the UAV to pass through. A bounding box array is created in the whole 3D space to evaluate the free voxel connectivity. The probabilistic roadmap method (PRM) is improved by random sampling in the bounding box array to ensure a more efficient distribution of roadmap nodes in 3D space. According to the connectivity evaluation, the roadmap is used to plan a feasible path by using A* algorithm. Experimental results indicate that the proposed algorithm is valid in complex 3D environments.
Snake robots are mostly designed based on single mode locomotion. However, single mode gait most likely could not work effectively when the robot is subject to an unstructured working environment with different measures of terrain complexity. As a solution, mixed mode locomotion is proposed in this paper by synchronizing two types of gaits known as serpentine and wriggler gaits used for non-constricted and narrow space environments, respectively, but for straight line locomotion only. A gait transition algorithm is developed to efficiently change the gait from one to another. This study includes the investigation on kinematics analysis followed by dynamics analysis while considering related structural constraints for both gaits. The approach utilizes the speed of the serpentine gait for open area locomotion and exploits the narrow space access capability of the wriggler gait. Hence, it can increase motion flexibility in view of the fact that the robot is able to change its mode of locomotion according to the working environment.
An automated approach is proposed for a microassembly task, which is to insert a 10 m diameter glass tube into a 12 m diameter hole in a silicon substrate, and bond them together with ultraviolet (UV) curable adhesive. Two three-degree-of-freedom micromanipulators are used to move the glass tube and the dispensing needle, respectively. Visual feedback is provided by an optical microscope. The angle of the microscope axis is precisely calibrated using an autofocus strategy. Robust image segmentation method and feature extraction algorithm are developed to obtain the features of the hole, the glass tube and the dispensing needle. Visual servo control is employed to achieve accurate aligning for the tube and the hole. Automated adhesive dispensing is used to bond the glass tube and the silicon substrate together after the insertion. On-line monitoring ensures that the diameter of the adhesive spot is within a desired range. Experimental results demonstrate the effectiveness of the proposed strategy.
This paper deals with the dynamics and control of a novel 3-degrees-of-freedom (DOF) parallel manipulator with actuation redundancy. According to the kinematics of the redundant manipulator, the inverse dynamic equation is formulated in the task space by using the Lagrangian formalism, and the driving force is optimized by utilizing the minimal 2-norm method. Based on the dynamic model, a synchronized sliding mode control scheme based on contour error is proposed to implement accurate motion tracking control. Additionally, an adaptive method is introduced to approximate the lumped uncertainty of the system and provide a chattering-free control. The simulation results indicate the effectiveness of the proposed approaches and demonstrate the satisfactory tracking performance compared to the conventional controller in the presence of the parameter uncertainties and un-modelled dynamics for the motion control of manipulators.
This paper deals with the problem of the state estimation and the sensor faults detection for nonlinear perturbed systems described by Takagi-Sugeno (T-S) fuzzy models with unmeasurable premise variables. Indeed, a T-S observer is synthesized, in descriptor form, to estimate both the system states and the sensor faults simultaneously. The idea of the proposed approach is to introduce the sensor fault as an auxiliary variable in the state vector. Besides, the T-S model with unmeasurable premise variables is reduced to a perturbed model with measurable variables. Convergence conditions are established with Lyapunov theory and the H performance in order to guarantee the best robustness to disturbances. These conditions are expressed in terms of linear matrix inequalities (LMIs). The parameters of the observer are computed using the solution of the LMI conditions. Finally, a numerical example is given to illustrate the design procedures. Simulation results show the satisfactory performances.
This paper is concerned with the problem of global output feedback stabilization in probability for a class of switched stochastic nonlinear systems under arbitrary switchings. The subsystems are assumed to be in output feedback form and driven by white noise. By introducing a common Lyapunov function, the common output feedback controller independent of switching signals is constructed based on the backstepping approach. It is proved that the zero solution of the closed-loop system is fourth-moment exponentially stable. An example is given to show the effectiveness of the proposed method.
Encryption techniques ensure security of data during transmission. However, in most cases, this increases the length of the data, thus it increases the cost. When it is desired to transmit data over an insecure and bandwidth-constrained channel, it is customary to compress the data first and then encrypt it. In this paper, a novel algorithm, the new compression with encryption and compression (CEC), is proposed to secure and compress the data. This algorithm compresses the data to reduce its length. The compressed data is encrypted and then further compressed using a new encryption algorithm without compromising the compression efficiency and the information security. This CEC algorithm provides a higher compression ratio and enhanced data security. The CEC provides more confidentiality and authentication between two communication systems.
Off-chip replacement (capacity and conflict) and coherent read misses in a distributed shared memory system cause execution to stall for hundreds of cycles. These off-chip replacement and coherent read misses are recurring and forming sequences of two or more misses called streams. Prior streaming techniques ignored reordering of misses and not-recently-accessed streams while streaming data. In this paper, we present stream prefetcher design that can deal with both problems. Our stream prefetcher design utilizes stream waiting rooms to store not-recently-accessed streams. Stream waiting rooms help remove more off-chip misses. Using trace based simulations, our stream prefetcher design can remove 8% to 66% (on average 40%) and 17% to 63% (on average 39%) replacement and coherent read misses, respectively. Using cycle-accurate full-system simulation, our design gives speedups from 1.00 to 1.17 of princeton application repository for shared-memory computers (PARSEC) workloads running on a distributed shared memory system with the exception of dedup and swaptions workloads.
Cloud computing can provide a great capacity for massive computing, storage as well as processing. The capacity comes from the cloud computing system itself, which can be likened to a virtualized resource pool that supports virtualization applications as well as load migration. Based on the existing technologies, the paper proposes a resource virtualization model (RVM) utilizing a hybrid-graph structure. The hybrid-graph structure can formally represent the critical entities such as private clouds, nodes within the private clouds, and resource including its type and quantity. It also provides a clear description of the logical relationship and the dynamic expansion among them as well. Moreover, based on the RVM, a resource converging algorithm and a maintaining algorithm of the resource pool which can timely reflect the dynamic variation of the private cloud and resource are presented. The algorithms collect resources and put them into the private cloud resource pools and global resource pools, and enable a real-time maintenance for the dynamic variation of resource to ensure the continuity and reliability. Both of the algorithms use a queue structure to accomplish functions of resource converging. Finally, a simulation platform of cloud computing is designed to test the algorithms proposed in the paper. The results show the correctness and the reliability of the algorithms.