Efficient workspace generation for binary manipulators with many actuators

Binary actuators have only two discrete states, both of which are stable without feedback. As a result, manipulators with binary actuators have a finite number of states. Major benefits of binary actuation are that extensive feedback control is not required, task repeatability can be very high, and two-state actuators are generally very inexpensive, thus resulting in low-cost robotic mechanisms. Determining the workspace of a binary manipulator is of great practical importance for a variety of applications. For instance, a representation of the workspace is essential for trajectory tracking, motion planning, and the optimal design of binary manipulators. Given that the number of configurations attainable by binary manipulators grows exponentially in the number of actuated degrees of freedom, 0(2), brute force representation of binary manipulator workspaces is not feasible in the highly actuated case. This article describes an algorithm that performs recursive calculations starting at the end-effector and terminating at the base. The implementation of these recursive calculations is based on the macroscopically serial structure and the discrete nature of the manipulator. As a result, the method is capable of approximating the workspace in linear time, O(n), where the slope depends on the acceptable error. © 1995 John Wiley b Sons, Inc.     

Task-oriented approaches to the inverse kinematics problem for a reclaimer excavating and transporting raw material

The inverse kinematics problem of the reclaimer that excavates and transports raw materials in a raw yard is investigated. Link coordinates are introduced by the Denavit-Hartenberg representation. The middle bucket, among the buckets which are in contact with the raw material pile, is treated as the end-effector of a reclaimer. Two task-oriented approaches are investigated. The first approach assumes the complete removal of a pile via a level plan on the pile. In this case, the end-effector is assumed to be a particle moving in the three-dimensional (3D) space. A closedform solution is provided. The second approach assumes the reclamation of an arbitrary pile. The end-effector is regarded as a rigid body which requires both position and orientation information. Because there is no solution for the second approach in general, an approximate solution is provided by exploiting a geometric constraint. The 3D information near the excavation point is approximated as a plane and the orientation of the end-effector is given in the normal direction of the plane.     

A repeatable inverse kinematics algorithm with linear invariant subspaces for mobile manipulators.

On the basis of a geometric characterization of repeatability we present a repeatable extended Jacobian inverse kinematics algorithm for mobile manipulators. The algorithm's dynamics have linear invariant subspaces in the configuration space. A standard Ritz approximation of platform controls results in a band-limited version of this algorithm. Computer simulations involving an RTR manipulator mounted on a kinematic car-type mobile platform are used in order to illustrate repeatability and performance of the algorithm.     

Human-like redundancy resolution: An integrated inverse kinematics scheme for anthropomorphic manipulators with radial elbow offset

As a mimic of the human arm structure, anthropomorphic manipulators with radial elbow offset (AMREO) are often deployed on humanoid service robots. However, the unique offset leads to difficulties in solving the analytical inverse kinematics (IK), which poses a challenge for further anthropomorphic control. This paper presents an integrated scheme for solving the path-wise IK problem of a 7-DoF AMREO in the position domain. Unlike other approaches, special attention is paid to the naturalness of the arm configuration, with the aim of making AMREO exhibit human-like behavior in human-centered environments. First, an analytical IK solution of AMREO for a single end-effector pose is derived based on the arm angle parameterization. Then, inspired by the habitual arm configurations in human reaching movements, the natural arm configuration mapped to wrist position is proposed for AMREO. To learn the patterns implied therein, a LSTM-based natural arm angle prediction network (NAPN) is designed and trained based on a human demonstration dataset. Finally, a redundancy resolution framework embedded with NAPN is built to generate smooth and natural joint configurations in the path-wise IK tasks. Comparative experiments show that the proposed analytical IK algorithm has better computational efficiency and precision than conventional methods, and can give complete results for one IK call within 4 μs. In addition, continuous path tracking experiments on a real robot validate the effectiveness and anthropomorphism of the redundancy resolution scheme based on NAPN.     

A new decode algorithm for binary bar codes

Distortion is the major obstacle to more aggressive decoding in current bar code decoders. The new decoder introduced in this paper is one of the solutions to this problem for binary bar codes (e.g. CODE 39). The new decoder is basically a minimum distance decoder that also utilizes distortion information from the start/stop characters. Security and execution time issues are also addressed in the paper.     

DisABC: A new artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm is one of the recently proposed swarm intelligence based algorithms for continuous optimization. Therefore it is not possible to use the original ABC algorithm directly to optimize binary structured problems. In this paper we introduce a new version of ABC, called DisABC, which is particularly designed for binary optimization. DisABC uses a new differential expression, which employs a measure of dissimilarity between binary vectors in place of the vector subtraction operator typically used in the original ABC algorithm. Such an expression helps to maintain the major characteristics of the original one and is respondent to the structure of binary optimization problems, too. Similar to original ABC algorithm, DisABC's differential expression works in continuous space while its consequence is used in a two-phase heuristic to construct a complete solution in binary space. Effectiveness of DisABC algorithm is tested on solving the uncapacitated facility location problem (UFLP). A set of 15 benchmark test problem instances of UFLP are adopted from OR-Library and solved by the proposed algorithm. Results are compared with two other state of the art binary optimization algorithms, i.e., binDE and PSO algorithms, in terms of three quality indices. Comparisons indicate that DisABC performs very well and can be regarded as a promising method for solving wide class of binary optimization problems.     

A time controlling neural network for time‐varying QP solving with application to kinematics of mobile manipulators

To obtain the solution for time‐varying quadratic programming (QP), a time controlling neural network (TCNN) is presented and discussed. The traditional recurrent neural networks provide a prospect for real‐time calculations and repeatable trajectory control of the mobile manipulators due to its high executing processing and nonlinear disposal ability. However, the convergent time is still a considerable point for the solution of a dynamic system dealing with synchronism and robustness. In this note, a TCNN model by incorporating an initial rectified term is applied to solve the online calculation problems and the convergent time can be controlled in advance. Theoretical analyses on stability, prespecified time and convergence are rigorously clarified. Finally, effectiveness and precision of the TCNN model for the solution of a QP example have been verified. In addition, a repetitive trajectory planning for a three‐wheel manipulator is introduced to demonstrate the superiority of the TCNN.     

Binary particle swarm optimisation with quadratic transfer function: A new binary optimisation algorithm for optimal scheduling of appliances in smart homes

Demand response (DR) is the response of electricity consumers to time-varying tariffs or incentives awarded by the utility. Home energy management systems are systems whose role is to control the consumption of appliances under DR programs, in a way that electricity bill is minimised. While, most researchers have done optimal scheduling only for non-interruptible appliances, in this paper, the interruptible appliances such as electric water heaters are considered. In optimal scheduling of non-interruptible appliances, the problem is commonly formulated as an optimisation problem with integer decision variables. However, consideration of interruptible appliances leads to a binary optimisation problem which is more difficult than integer optimisation problems. Since, the basic version of binary particle swarm optimisation (PSO) does not perform well in solving binary engineering optimisation problems, in this paper a new binary particle swarm optimisation with quadratic transfer function, named as quadratic binary PSO (QBPSO) is proposed for scheduling shiftable appliances in smart homes. The proposed methodology is applied for optimal scheduling in a smart home with 10 appliances, where the number of decision variables is as high as 264. Optimal scheduling is done for both RTP and TOU tariffs both with and without consideration of consumers’ comfort. The achieved results indicate the drastic effect of optimal scheduling on the reduction of electricity bill, while consumers’ comfort is not much affected. The results testify that the proposed QBPSO outperforms basic binary PSO variant and 9 other binary PSO variants with different transfer functions.     

A new data hiding scheme for binary image authentication with small image distortion

A new data hiding scheme for binary image authentication that has a small distortion of the cover image is proposed in this paper. Using the data-embedding algorithm that is based on Hamming codes, the proposed scheme embeds authentication information into the cover image with flipping only a small number of pixels. A special type of the pixels are selected and flipped by a new algorithm to minimize visual distortion. This new algorithm is based on ELSSM (Edge Line Segment Similarity Measure). Randomly shuffling the bit-order of the authentication information to be embedded, the information can only be extracted by the designated receiver who has the symmetric key. We employ two measurement metrics: miss detection rates for the degree of security and PSNR (Peak Signal-to-Noise Ratio) and ELSSM for the degree of the image distortion to demonstrate the feasibility of the proposed scheme. Using these metrics, we analyze the proposed scheme and the previous schemes. The analysis reveals that the proposed scheme requires less image distortion than the previous schemes whilst achieving the same level of the miss detection rate. Experimental results demonstrate that the proposed scheme is more resilient against recent steganalysis attacks than the previous schemes.     

A new approximation algorithm for labeling points with circle pairs

We study the NP-hard problem of labeling points with maximum-radius circle pairs: given n point sites in the plane, find a placement for 2n interior-disjoint uniform circles, such that each site touches two circles and the circle radius is maximized. We present a new approximation algorithm for this problem that runs in time and O(n) space and achieves an approximation factor of (≈1.486+ε), which improves the previous best bound of 1.491+ε.     

A fast algorithm for determining the linear complexity of a binary sequence with period 2~np~m

An efficient algorithm for determining the linear complexity and the minimal polynomial of a binary sequence with period 2npm is proposed and proved, where 2 is a primitive root modulo p2. The new algorithm generalizes the algorithm for computing the linear complexity of a binary sequence with period 2" and the algorithm for computing the linear complexity of a binary sequence with period pn, where 2 is a primitive root modulo p2.     

A new exact algorithm for concave knapsack problems with integer variables

Concave knapsack problems with integer variables have many applications in real life, and they are NP-hard. In this paper, an exact and efficient algorithm is presented for concave knapsack problems. The algorithm combines the contour cut with a special cut to improve the lower bound and reduce the duality gap gradually in the iterative process. The lower bound of the problem is obtained by solving a linear underestimation problem. A special cut is performed by exploiting the structures of the objective function and the feasible region of the primal problem. The optimal solution can be found in a finite number of iterations, and numerical experiments are also reported for two different types of concave objective functions. The computational results show the algorithm is efficient.     

A new heuristic algorithm for cuboids packing with no orientation constraints

The three-dimensional cuboids packing is NP-hard and finds many applications in the transportation industry. The problem is to pack a subset of cuboid boxes into a big cuboid container such that the total volume of the packed boxes is maximized. The boxes have no orientation constraints, i.e. they can be rotated by 90^°${90}^{°}$ in any direction. A new heuristic algorithm is presented that defines a conception of caving degree to judge how close a packing box is to those boxes already packed into the container, and always chooses a packing with the largest caving degree to do. The performance is evaluated on all the 47 related benchmarks from the OR-Library. Experiments on a personal computer show a high average volume utilization of 94.6% with an average computation time of 23 min for the strengthened A1 algorithm, which improves current best records by 3.6%. In addition, the top-10 A2 algorithm achieved an average volume utilization of 91.9% with an average computation time of 55 s, which also got higher utilization than current best records reported in the literature.     

A new algorithm based on copulas for VaR valuation with empirical calculations

This paper concerns the application of copula functions in VaR valuation. The copula function is used to model the dependence structure of multivariate assets. After the introduction of the traditional Monte Carlo simulation method and the pure copula method we present a new algorithm based on mixture copula functions and the dependence measure, Spearman’s rho. This new method is used to simulate daily returns of two stock market indices in China, Shanghai Stock Composite Index and Shenzhen Stock Composite Index, and then empirically calculate six risk measures including VaR and conditional VaR. The results are compared with those derived from the traditional Monte Carlo method and the pure copula method. From the comparison we show that the dependence structure between asset returns plays a more important role in valuating risk measures comparing with the form of marginal distributions.     

A new learning algorithm with logarithmic performance index for complex-valued neural networks

In a fully complex-valued feed-forward network, the convergence of the Complex-valued Back Propagation (CBP) learning algorithm depends on the choice of the activation function, learning sample distribution, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing versions of CBP learning algorithm in the literature do not approximate the phase of complex-valued output well in function approximation problems. The phase of a complex-valued output is critical in telecommunication and reconstruction and source localization problems in medical imaging applications. In this paper, the issues related to the convergence of complex-valued neural networks are clearly enumerated using a systematic sensitivity study on existing complex-valued neural networks. In addition, we also compare the performance of different types of split complex-valued neural networks. From the observations in the sensitivity analysis, we propose a new CBP learning algorithm with logarithmic performance index for a complex-valued neural network with exponential activation function. The proposed CBP learning algorithm directly minimizes both the magnitude and phase errors and also provides better convergence characteristics. Performance of the proposed scheme is evaluated using two synthetic complex-valued function approximation problems, the complex XOR problem, and a non-minimum phase equalization problem. Also, a comparative analysis on the convergence of the existing fully complex and split complex networks is presented.     

A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.     

A new heuristic algorithm for the circular packing problem with equilibrium constraints

The circular packing problem with equilibrium constraints is an optimization problem about simplified satellite module layout design.A heuristic algorithm based on tabu search is put forward for solving this problem.The algorithm begins from a random initial configuration and applies the gradient method with an adaptive step length to search for the minimum energy configuration.To jump out of the local minima and avoid the search doing repeated work,the algorithm adopts the strategy of tabu search.In the pr...     

A new algorithm for fitting tracks with energy losses due to Bremsstrahlung

This paper introduces a Fast Bayesian Track Fit (FBTF) — a new algorithm for fitting tracks with Bremsstrahlung-induced energy losses. The FBTF algorithm combines a standard Kalman filter and a Bayesian estimator for fractional energy losses. The estimator uses computationally efficient approximations of the Bethe–Heitler distribution and measurement likelihood which allows analytical calculation of Bayesian integrals. The algorithm performance has been evaluated with simulated data using a simplified tracking scenario. The results regarding track parameter estimation quality and computing time are presented and compared with those obtained with a Gaussian-sum filter.     

A new algorithm for complex product flexible scheduling with constraint between jobs

In order to simplify the complex product flexible scheduling problem with constraint between jobs, a new hierarchical scheduling algorithm based on improved processing operation tree is presented. Aiming at the routing problem, short-time strategy and machine-balance strategy are adopted to assign each operation to a machine out of a set of machines. And in order to solve the sequencing problem, the allied critical path method is first adopted to confirm the scheduling sequence of operations, and then operations are divided into dependent operations and independent ones according to their characteristics. For the dependent operations, forward greedy rule is adopted in order to make the completion time of operation as soon as possible and the scheduling algorithm of shortening idle time is adopted by analyzing the characteristics of the independent operations. Experiment shows that the proposed algorithm solves for the first time the complex product flexible scheduling problem with constraint between jobs.     

A new algorithm for non-linear mapping with applications to dimension and cluster analyses

In this paper, a new non-linear mapping method suitable for dimension and cluster analysis is proposed. In order to obtain a flexible and powerful method, the non-metric multidimensional scaling of Kruskal type is extended by introducing the concept of k-nearest neighbor. Some simulation results supporting the efficiency of our new method are given along with a detailed discussion.