Many object surfaces involve a number of pieces, expressed by different equations. Previous methods of optimal grasp planning can hardly cope with such cases. Ding et al. solve this problem by characterizing the object surface with convex facets and discrete points, then selecting the eligible ones for force-closure, and finally seeking the optimal contact positions on the selected elements. So far, however, no point contact with friction (PCwF) but only frictionless point contacts (FPC) can be used on the facets, while soft finger contacts (SFC) are excluded at all. In this paper, to the above two surface elements we add line segments. Moreover, the limitations on the contact types are completely removed. A general condition and a quantitative criterion of eligibility are presented, followed by a heuristic algorithm and an iterative algorithm for finding the better eligible elements. Three common examples show: the new advances make the formerly tough problems smoothly solvable.

This paper proposed a novel three degree of freedom (DOF) parallel manipulator—two translations and one rotation. The mobility study and inverse kinematic analysis are conducted, and a CAD model is presented showing the design features. The optimization techniques based on artificial intelligence approaches are investigated to improve the system stiffness of the proposed 3-DOF parallel manipulator. Genetic algorithms and artificial neural networks are implemented as the intelligent optimization methods for the stiffness synthesis. The mean value and the standard deviation of the global stiffness distribution are proposed as the design indices. Both the single objective and multi-objective optimization issues are addressed. The effectiveness of this methodology is validated with Matlab.

The paper presents a self-contained approach for the dynamics identification of six degrees of freedom (DOF) parallel robots. Major feature is the consequent consideration of structural properties of such machines to provide an experimentally adequate identification method. The known periodic excitation is modified and enhanced to take the actuator coupling as well as the numerical solution of the direct kinematics into account. The benefits of explicit frequency-domain data filtering are demonstrated. Additionally, a new implementation of the maximum-likelihood estimator allows for automatic tuning of the data filter. The issue of optimal input experiment design is also discussed and substantiated with extensive experiments.

We have developed an underwater robotic fish using a unique three-layer electrostatic film motor. In the robotic fish, the unique motor actuates a flexible caudal fin to propel the robot via an elaborate power transmission system. In the present study, we describe the major disadvantages of the previous prototype of the robotic fish and improvements of the prototype. In addition, we present experimental evaluations related to the control parameters and locomotion performance of the robotic fish. These control parameters include the frequency and initial phase of AC voltage, and the amplitude and period of frequency sweeping. A simple theoretical model concerning the power transmission system of the robotic fish is also analyzed to provide a possible explanation for the unique swimming control. By appropriately adjusting these control parameters, we achieve cruising, emerging, submerging, and turning of the robotic fish even though only the caudal fin is active. Finally, we show smooth human-operated turn-around motion similar to that seen in real fish. Based on these experimental results, we further clarify the relationships between the open-loop motor pattern and motion parameters.

A robot must have high positioning accuracy and repeatability for precise applications. However, variations in performance are observed due to the effect of uncertainty in design and process parameters. So far, there has been no attempt to optimize the design parameters of manipulator by which performance variations will be minimum. A modification in differential evolution optimization technique is proposed to incorporate the effect of noises in the optimization process and obtain the optimal design of manipulator, which is insensitive to noises. This approach has been illustrated by selecting optimal parameter of 2-DOF RR planar manipulator and 4-DOF SCARA manipulator. The performance of proposed approach has been compared with genetic algorithm with similar modifications. It is observed that the optimal results are obtained with lesser computations in case of differential evolution technique. This approach is a viable alternative for costly prototype testing, where only kinematic and dynamic models of manipulator are dealt with.

This paper proposes methodologies and techniques for multi-block navigation of an indoor localization system with active beacon sensors. As service robots and ubiquitous technology have evolved, there is an increasing need for autonomous indoor navigation of mobile robots. In a large number of indoor localization schemes, the absolute position estimation method, relying on navigation beacons or landmarks, has been widely used due to its low cost and high accuracy. However, few of these schemes have managed to expand the applications for use in complicated workspaces involving many rooms or blocks that cover a wide region, such as airports and stations. Since the precise and safe navigation of mobile robots in complicated workspaces is vital for the ubiquitous technology, it is necessary to develop a multi-block navigation scheme. This new design of an indoor localization system includes ultrasonic attenuation compensation, dilution of both the precision analysis and fault detection, and an isolation algorithm using redundant measurements. These ideas are implemented on actual mobile robot platforms and beacon sensors, and experimental results are presented to test and demonstrate the new methods.

In this paper we are concerned with control of rigid robots equipped with brushless DC-motors (BLDC) when the electric dynamics of these actuators is taken into account. We show for the first time that a saturated PD controller suffices to achieve global asymptotic stability. Our controller is the simplest controller proposed until now to solve this problem: it only requires position measurements and linear feedback of electric current.

This paper presents a bipedal locomotion model for passive dynamic walking with flat feet and compliant ankles. The two-dimensional seven-link model extends the simplest walking model with the addition of hip actuation, knee joints, flat feet and torsional springs based compliance on ankle joints, concerning heel-strike and toe-strike transitions, to achieve adaptive bipedal locomotion on level ground with controllable walking speed. We investigate the effects of foot geometric parameters and ankles stiffness on bipedal walking. The model achieves satisfactory walking results not only on even ground but also on uneven terrain with no active control and on different walking velocities. In addition, from the view of stability, there is an optimal foot-ankle ratio of the passivity-based walker. The results can be used to explore further understanding of bipedal walking, and help the design of future intelligent ankle-foot prosthesis and passivity-based robot prototypes towards more practical uses.

This paper proposes a new approach for solving a generalization of the task scheduling problem for articulated robots (either redundant or non-redundant), where the robot's 2D environment is cluttered with obstacles of arbitrary size, shape and location, while a set of task-points are located in the robot's free-space. The objective is to determine the optimum collision-free robot's tip tour through all task-points passing from each one exactly once and returning to the initial task-point. This scheduling problem combines two computationally NP-hard problems: the optimal scheduling of robot tasks and the collision-free motion planning between the task-points.

The proposed approach employs the bump-surface (B-Surface) concept for the representation of the 2D robot's environment by a B-Spline surface embedded in 3D Euclidean space. The time-optimal task scheduling is being searched on the generated B-Surface using a genetic algorithm (GA) with a special encoding in order to take into consideration the infinite configurations corresponding to each task-point. The result of the GA's searching constitutes the solution to the task scheduling problem and satisfies optimally the task scheduling criteria and objectives. Extensive experimental results show the efficiency and the effectiveness of the proposed method to determine the collision-free motion among obstacles.

This paper describes a new approach for estimating the vehicle motion trajectory in complex urban environments by means of visual odometry. A new strategy for robust feature extraction and data post-processing is developed and tested on-road. Images from scale-invariant feature transform (SIFT) features are used in order to cope with the complexity of urban environments. The obtained results are discussed and compared to previous works. In the prototype system, the ego-motion of the vehicle is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. The distance between estimations is dynamically adapted based on re-projection and estimation errors. Vehicle motion is estimated using the non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The final goal is to provide on-board driver assistance in navigation tasks, or to provide a means of autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene or the vehicle motion. An example of how to estimate a vehicle's trajectory is provided along with suggestions for possible further improvement of the proposed odometry algorithm.

Hull cleaning before repainting is a key operation in the maintenance of ships. For more than a decade, a means to improve this operation has been sought through robotization and the use of different techniques such as grit blasting and ultra high pressure water jetting. Despite this, it continues to be standard practice in shipyards that this process is carried out manually. This paper presents a family of robots that aims to offer important improvements to the process as well as satisfying, to a great extent, all the operative requirements of efficiency, security, and respect for the environment that shipyards nowadays demand. It is described the family of devices with emphasis on the mechanical design. This set consists of two vertical robotic towers and a robot climber. In addition, it is shown the control architecture of the global system. Finally, operative results are presented together with a comparison between the performance achieved in shipyards through the use of these robots and those obtained with a manual process.

Biomimetic applications play an important role in informing the field of robotics. One aspect is navigation – a skill automobile robots require to perform useful tasks. A sub-area of this is search strategies, e.g. for search and rescue, demining, exploring surfaces of other planets or as a default strategy when other navigation mechanisms fail. Despite that, only a few approaches have been made to transfer biological knowledge of search mechanisms on surfaces along the ground into biomimetic applications. To provide insight for robot navigation strategies, this study describes the paths a crayfish used to explore terrain. We tracked movement when different sets of sensory input were available. We then tested this algorithm with a computer model crayfish and concluded that the movement of C. destructor has a specialised walking strategy that could provide a suitable baseline algorithm for autonomous mobile robots during navigation.