Volume 10, Number 5, 2013
Special Issue on Robotics and Biomimetics: Part I (pp.375-454)
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Articulated movements are fundamental in many human and robotic tasks. While humans can learn and generalise arbitrarily long sequences of movements, and particularly can optimise them to fit the constraints and features of their body, robots are often programmed to execute point-to-point precise but fixed patterns. This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives. Instead of achieving accurate reproductions, the proposed approach aims at interpreting data in an agent-centred fashion, according to an agent's primitive movements. The method improves the accuracy of a reproduction with an incremental process that seeks first a rough approximation by capturing the most essential features of a demonstrated trajectory. Observing the discrepancy between the demonstrated and reproduced trajectories, the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory. The aim is to achieve an agent-centred interpretation and progressive learning that fits in the first place the robots' capability, as opposed to a data-centred decomposition analysis. Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method. In particular, because trajectories are understood and abstracted by means of agent-optimised primitives, the method has two main features: 1) Reproduced trajectories are general and represent an abstraction of the data. 2) The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection. This study suggests a novel bio-inspired approach to interpreting, learning and reproducing articulated movements and trajectories. Possible applications include drawing, writing, movement generation, object manipulation, and other tasks where the performance requires human-like interpretation and generalisation capabilities.
This paper presents a hierarchical simultaneous localization and mapping (SLAM) system for a small unmanned aerial vehicle (UAV) using the output of an inertial measurement unit (IMU) and the bearing-only observations from an onboard monocular camera. A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match. This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter (EKF) for attitude and velocity estimation. Then, another EKF is employed to estimate the position of the vehicle and the locations of the features in the map. Both simulations and experiments are carried out to test the performance of the proposed system. The result of the comparison with the referential global positioning system/inertial navigation system (GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.
In this paper, we present a novel algorithm for odometry estimation based on ceiling vision. The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches. The principal direction is defined based on the fact that our ceiling is filled with artificial vertical and horizontal lines which can be used as reference for the current robot's heading direction. The proposed approach can be operated in real-time and it performs well even with camera's disturbance. A moving low-cost RGB-D camera (Kinect), mounted on a robot, is used to continuously acquire point clouds. Iterative closest point (ICP) is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one. However, its performance suffers from data association problem or it requires pre-alignment information. The performance of the proposed principal direction detection approach does not rely on data association knowledge. Using this method, two point clouds are properly pre-aligned. Hence, we can use ICP to fine-tune the transformation parameters and minimize registration error. Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time. Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to significantly improve the accuracy of simultaneous localization and mapping (SLAM).
This paper describes the analysis and design of an assistive device for elderly people under development at the Egypt-Japan University of Science and Technology (E-JUST) named E-JUST assistive device (EJAD). Several experiments were carried out using a motion capture system (VICON) and inertial sensors to identify the human posture during the sit-to-stand motion. The EJAD uses only two inertial measurement units (IMUs) fused through an adaptive neuro-fuzzy inference systems (ANFIS) algorithm to imitate the real motion of the caregiver. The EJAD consists of two main parts, a robot arm and an active walker. The robot arm is a 2-degree-of-freedom (2-DOF) planar manipulator. In addition, a back support with a passive joint is used to support the patient's back. The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture. The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient. A control scheme is proposed to control the system motion based on practical measurements taken from the experiments. A computer simulation showed a relatively good performance of the EJAD in assisting the patient.
The skill of robotic hand-eye coordination not only helps robots to deal with real time environment, but also affects the fundamental framework of robotic cognition. A number of approaches have been developed in the literature for construction of the robotic hand-eye coordination. However, several important features within infant developmental procedure have not been introduced into such approaches. This paper proposes a new method for robotic hand-eye coordination by imitating the developmental progress of human infants. The work employs a brain-like neural network system inspired by infant brain structure to learn hand-eye coordination, and adopts a developmental mechanism from psychology to drive the robot. The entire learning procedure is driven by developmental constraint: The robot starts to act under fully constrained conditions, when the robot learning system becomes stable, a new constraint is assigned to the robot. After that, the robot needs to act with this new condition again. When all the contained conditions have been overcome, the robot is able to obtain hand-eye coordination ability. The work is supported by experimental evaluation, which shows that the new approach is able to drive the robot to learn autonomously, and make the robot also exhibit developmental progress similar to human infants.
This paper presents a novel design of minimalist bipedal walking robot with flexible ankle and split-mass balancing systems. The proposed approach implements a novel strategy to achieve stable bipedal walk by decoupling the walking motion control from the sideway balancing control. This strategy allows the walking controller to execute the walking task independently while the sideway balancing controller continuously maintains the balance of the robot. The hip-mass carry approach and selected stages of walk implemented in the control strategy can minimize the effect of major hip mass of the robot on the stability of its walk. In addition, the developed smooth joint trajectory planning eliminates the impacts of feet during the landing. In this paper, the new design of mechanism for locomotion systems and balancing systems are introduced. An additional degree of freedom introduced at the ankle joint increases the sensitivity of the system and response time to the sideway disturbances. The effectiveness of the proposed strategy is experimentally tested on a bipedal robot prototype. The experimental results provide evidence that the proposed strategy is feasible and advantageous.
This paper describes a person identification method for a mobile robot which performs specific person following under dynamic complicated environments like a school canteen where many persons exist. We propose a distance-dependent appearance model which is based on scale-invariant feature transform (SIFT) feature. SIFT is a powerful image feature that is invariant to scale and rotation in the image plane and also robust to changes of lighting condition. However, the feature is weak against affine transformations and the identification power will thus be degraded when the pose of a person changes largely. We therefore use a set of images taken from various directions to cope with pose changes. Moreover, the number of SIFT feature matches between the model and an input image will decrease as the person becomes farther away from the camera. Therefore, we also use a distance-dependent threshold. The person following experiment was conducted using an actual mobile robot, and the quality assessment of person identification was performed.
For the existing problems of walking chair robot such as simple function, lower bearing capacity and not walking in complex environment, a novel varistructured quadruped/biped human-carrying walking chair robot is proposed. The proposed robot could be used as biped and quadruped walking chair robots. Considering the conversion of the walking chair robot from the quadruped to the biped or vice versa, 6-UPS and 2-UPS+UP (U, P and S are universal joint, the prismatic pair, and sphere joint, respectively) parallel mechanisms are selected as the leg mechanism of the biped walking robot and quadruped walking robot, respectively. Combining the screw theory and theory of mechanism, the degrees of freedom of the leg mechanism and the body mechanism in different motion states are computed so as to meet the requirements of mechanism design. The motion characteristics of the 2-UPS+UP parallel mechanism which is the key part of the walking chair robot are analyzed. Then, the workspace of the moving platform is drawn and the effect of the structural parameters on the workspace volume is studied. Finally, it is found that the volume of the workspace of the moving platform is bigger when the side length ratio and the vertex angle ratio of the fixed platform and the moving platform which are isosceles triangles are close to 1. This study provides a theoretical foundation for the prototype development.
This paper investigates the finite-time consensus problem of multi-agent systems with single and double integrator dynamics, respectively. Some novel nonlinear protocols are constructed for first-order and second-order leader-follower multi-agent systems, respectively. Based on the finite-time control technique, the graph theory and Lyapunov direct method, some theoretical results are proposed to ensure that the states of all the follower agents can converge to its leader agent's state in finite time. Finally, some simulation results are presented to illustrate the effectiveness of our theoretical results.
The problem of linear systems subject to actuator faults (outage, loss of effectiveness and stuck), parameter uncertainties and external disturbances is considered. An active fault compensation control law is designed which utilizes compensation in such a way that uncertainties, disturbances and the occurrence of actuator faults are account for. The main idea is designing a robust adaptive output feedback controller by automatically compensating the fault dynamics to render the close-loop stability. According to the information from the adaptive mechanism, the updating control law is derived such that all the parameters of the unknown input signal are bounded. Furthermore, a disturbance decoupled fault reconstruction scheme is presented to evaluate the severity of the fault and to indicate how fault accommodation should be implemented. The advantage of fault compensation is that the dynamics caused by faults can be accommodated online. The proposed design method is illustrated on a rocket fairing structural-acoustic model.
Discrete linear quadratic control has been efficiently applied to linear systems as an optimal control. However, a robotic system is highly nonlinear, heavily coupled and uncertain. To overcome the problem, the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks. This modeling motivates us to develop an optimal repetitive control. The contribution of this paper is twofold. For the first time, it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model. The proposed control approach is based on the voltage control strategy. Second, uncertainty is effectively compensated by employing a robust time-delay controller. The uncertainty can include parametric uncertainty, unmodeled dynamics and external disturbances. To highlight its ability in overcoming the uncertainty, the dynamic equation of an articulated robot is introduced and used for the simulation, modeling and control purposes. Stability analysis verifies the proposed control approach and simulation results show its effectiveness.
Linear matrix equations are encountered in many systems and control applications. In this paper, we consider the general coupled matrix equations (including the generalized coupled Sylvester matrix equations as a special case) t=1l EstYtFst=Gs, s=1, 2,, l over the generalized reflexive matrix group (Y1, Y2,, Yl). We derive an efficient gradient-iterative (GI) algorithm for finding the generalized reflexive solution group of the general coupled matrix equations. Convergence analysis indicates that the algorithm always converges to the generalized reflexive solution group for any initial generalized reflexive matrix group (Y1(1), Y2(1),, Yl(1)). Finally, numerical results are presented to test and illustrate the performance of the algorithm in terms of convergence, accuracy as well as the efficiency.
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