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Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

The purpose of this paper is to characterise the TWIN-RRT* algorithm which solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory and retrieves feasible, “low cost”, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinematic constraints in a 2D environment.

It was designed to work equally well with higher degree of freedom agents in different applications. It provides a practical implementation of feasible and fast planning, namely where a closed loop is required. Initial and final configurations are allowed to be exactly the same.

The TWIN-RRT* algorithm computes an efficient path for a single agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*.

It uses efficiency as cost function, which can be adjusted to the requirements of any given application. TWIN-RRT also shows compliance with kinematic constraints.

The practical application where this work has been used consists of an autonomous mobile robot that picks up golf balls in a driving range. The multiple targets are the golf balls and the optimum path is a requirement to reduce the time and energy to refill as quickly as possible the balls dispensing machine.

The new random sampling algorithm – TWIN-RRT* – is able to generate feasible efficient paths towards multiple targets retrieving closed-loop paths starting and finishing at the same configuration.

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.

In this chapter we consider the “Regularization of Derivative Expectation Operator” (Rodeo) of Lafferty and Wasserman (2008) and propose a modified Rodeo algorithm for semiparametric single index models (SIMs) in big data environment with many regressors. The method assumes sparsity that many of the regressors are irrelevant. It uses a greedy algorithm, in that, to estimate the semiparametric SIM of Ichimura (1993), all coefficients of the regressors are initially set to start from near zero, then we test iteratively if the derivative of the regression function estimator with respect to each coefficient is significantly different from zero. The basic idea of the modified Rodeo algorithm for SIM (to be called SIM-Rodeo) is to view the local bandwidth selection as a variable selection scheme which amplifies the coefficients for relevant variables while keeping the coefficients of irrelevant variables relatively small or at the initial starting values near zero. For sparse semiparametric SIM, the SIM-Rodeo algorithm is shown to attain consistency in variable selection. In addition, the algorithm is fast to finish the greedy steps. We compare SIM-Rodeo with SIM-Lasso method in Zeng et al. (2012). Our simulation results demonstrate that the proposed SIM-Rodeo method is consistent for variable selection and show that it has smaller integrated mean squared errors (IMSE) than SIM-Lasso.

The purpose of this paper is to investigate time-optimal control problems for multiple unmanned aerial vehicle (UAV) systems to achieve predefined flying shape.

Two time-optimal protocols are proposed for the situations with or without human control input, respectively. Then, Pontryagin’s minimum principle approach is applied to deal with the time-optimal control problems for UAV systems, where the cost function, the initial and terminal conditions are given in advance. Moreover, necessary conditions are derived to ensure that the given performance index is optimal.

The effectiveness of the obtained time-optimal control protocols is verified by two contrastive numerical simulation examples. Consequently, the proposed protocols can successfully achieve the prescribed flying shape.

This paper proposes a solution to solve the time-optimal control problems for multiple UAV systems to achieve predefined flying shape.

As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots.

The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness.

According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented.

EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study.

The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios.

This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.

This paper considers the problem of estimating a partially linear varying coefficient fixed effects panel data model. Using the series method, we establish the root N normality for the estimator of the parametric component; and we show that the unknown function can be consistently estimated at the standard nonparametric rate. Furthermore, we extend the model to allow endogeneity in the parametric component and establish the asymptotic properties of the semiparametric instrumental variable estimators.

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.

In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm.

Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks.

This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.