Humanoid Trajectory Optimization with B-Splines and Analytical Centroidal Momentum Derivatives

Joint resource and trajectory optimization in a UAV-enabled dual-function radar-communication network

As a supplement of mobile communication in Fifth generation mobile networks (5G), Unmanned Aerial Vehicles (UAVs) are playing an important role in user coverage. When base stations (BSs) are not able to serve all users in hot spots, deployable UAVs are required to mitigate the burden of BSs and maximize the access number of users. Besides, thanks to the controllable mobility, UAVs can provide better coverage for the mobile users with high velocity or fixed direction. Therefore, it is important to solve the problem of the trajectory planing of multiple UAVs. In this paper, we propose a UAV trajectory optimization strategy to maximize the access number of moving users. Firstly, according to the mobility of UAVs and the Signal to Interference plus Noise Ratio (SINR) requirements of users, we formulate the UAV's trajectory optimization problem, which is an atypical mixed integer linear programming problem (MILP). Then, a convex optimization problem is obtained by correlation relaxation of the previous atypical MILP. Finally, we propose a practicable trajectory optimization algorithm with limited times of iteration. The simulation results show that the proposed algorithm can maximize the access number of users while maintaining enough SINR.

This paper proposes a mobile unmanned aerial vehicle (UAV) jamming scheme under the probabilistic line-ofsight channel model (PLCM) to improve the secrecy of ground wiretap channels, in which a friendly UAV is deployed to cooperatively transmit jamming signals to confuse the suspicious eavesdropper. Our goal is to maximize the average (expected) secrecy rate by jointly optimizing the source transmit power, UAV jamming power and trajectory for a given flight time. Since the expected secrecy rate is highly complicated with respect to the UAV trajectory, we derive a more tractable lower bound for it. Nevertheless, the resulting optimization problem remains a non-convex problem, which is difficult to solve optimally. Therefore, we propose an efficient iterative algorithm to obtain a suboptimal solution to it by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Simulation results show that the joint power and trajectory optimization scheme under the PLCM significantly outperforms various benchmark schemes.

The substrate glass has the characteristics of large size, thin thickness, and easy breakage. The quality of the substrate glass handling robot directly affects the yield and production efficiency of flat panel display products. According to the characteristics of the substrate glass, the optimal design criteria of the robot end effector are proposed with a design of the optimal time and minimum impact multi-objective trajectory optimization scheme. By establishing a kinematic model, we analyze the forward kinematics and inverse kinematics of the robot and the singular configuration of the robot. Based on the multi-objective genetic algorithm, the Bspline curve is used to construct the robot trajectory, and its optimization model is established. MATLAB Robotics Tool carries out the motion simulation and trajectory planning example simulation of the MCL130 robot. The simulation shows that the motion time and joint impact of the robot have reached the optimum.

Join trajectory optimization and communication design for UAV-enabled OFDM networks

With the explosive increase of wireless traffic generated by Internet of Things (IoT), it is challenging for the ground communication infrastructures to achieve flexible and efficient data collection. In this paper, we propose an intelligent trajectory optimization and collision avoidance scheme for cooperative multi-UAV data collection in urban IoT environments, where a new Double Deep Q-Network (D3QN) is designed to maximize the total collected data by optimizing UAVs' flying speeds and dynamically adjusting the IoT-UAV associations. Simulations show that the proposed solution achieves the fastest convergence and the lowest collision rate with the average data collection rate of 91.15%, compared to baseline methods. Moreover, 4 UAVs can achieve the data collection rate of 95% compared to that of 50% in single UAV scenario.

Model-based control of a simulated moving bed chromatographic process for the separation of fructose and glucose

The goal of this paper is to design spatial (3D) robust reference trajectories for lower limb exoskeletons through optimization. Robustness is defined as the magnitude of the smallest force applied at the Center of Mass that cannot be rejected without violating joint torque or ground reaction force bounds. A larger robustness margin in the reference trajectory increases the capability of the controller to reject perturbations. A trajectory optimization problem was augmented by adding the maximization of this robustness metric to it. The augmented trajectory optimization is then employed to design dynamic trajectories for three different tasks. We demonstrated that our method increases the robustness metric value by comparing the results from the proposed method with a nominal trajectory optimization that does not take robustness into account. Moreover, the designed trajectories were implemented on a lower limb exoskeleton with a paraplegic user to show the feasibility of the proposed method. The user managed successfully to walk on level ground, perform side-stepping, and ascend stairs.

In this paper, a general guidance method based on a receding horizon control technique and online trajectory optimization is proposed for various interception problems. In this method, the interception problem is defined as an optimal control problem by full dynamics of the interceptor and the current position and velocity of the target in the three-dimensional space. By solving the interception optimal control problem for various objective and constraint functions, integrated guidance and control commands are generated online during interception. To find the closed-loop and state-dependent solution, the interception optimal control problem is defined in a receding horizon control framework. To quickly solve the interception problem, the direct trajectory optimization method, which is a combination of a differential flatness concept, b-spline curves, and nonlinear programming, is used. To demonstrate the implementation of the proposed method, a numerical example of interception of a maneuvering target by a missile is presented. The proposed optimal guidance method can be applied for integrated guidance and nonlinear control of the interceptors in various interception problems in aerospace engineering and robotics.

A EROSPACE engineering applications greatly stimulated the development of optimal control theory during the 1950s and 1960s, where the objective was to drive the system states in such a way that some defined cost was minimized. This turned out to have very useful applications in the design of regulators (where some steady state is to be maintained) and in tracking control strategies (where some predetermined state trajectory is to be followed). Among such applications was the problem of optimal flight trajectories for aircraft and space vehicles. Linear optimal control theory in particular has been very well documented and widely applied, where the plant that is controlled is assumed linear and the feedback controller is constrained to be linear with respect to its input. However, the availability of powerful low-cost microprocessors has spurred great advantages in the theory and applications of nonlinear control. The competitive era of rapid technological change, particularly in aerospace exploration, now demands stringent accuracy and cost requirements in nonlinear control systems. This has motivated the rapid development of nonlinear control theory for application to challenging, complex, dynamical real-world problems, particularly those that bear major practical significance in aerospace, marine, and defense industries. Infinite-time horizon nonlinear optimal control (ITHNOC) presents a viable option for synthesizing stabilizing controllers for nonlinear systems by making a state-input tradeoff, where the objective is to minimize the cost given by a performance index. The original theory of nonlinear optimal control dates from the 1960s. Various theoretical and practical aspects of the problem have been addressed in the literature over the decades since. In particular, the continuous-time nonlinear deterministic optimal control problem associated with autonomous (time-invariant) nonlinear regulator systems that are affine (linear) in the controls has been studied by many authors. The long-established theory of optimal control offers quite mature and well-documented techniques for solving this control-affine nonlinear optimization problem, based on dynamic programming or calculus of variations, but their application is generally a very tedious task. Bellman’s dynamic programming approach reduces to solving a nonlinear first-order partial differential equation (PDE), expressed by the Hamilton–Jacobi–Bellman (HJB) equation. The solution to the HJB equation gives the optimal performance/cost value (or storage) function and determines an optimal control in feedback form under some smoothness assumptions. Alternatively, in the classical calculus of variations, optimal control problems can be characterized locally in terms of the Hamiltonian dynamics arising from Pontryagin’s minimum principle. These are the characteristic equations of the HJB PDE, which result in a nonlinear, constrained two-point boundary value problem (TPBVP) that, in general, can only be solved by successive approximation of the optimal control input using iterative numerical techniques for each set of initial conditions. Numerically, even though the nonlinear TPBVP is somewhat easier to solve than the HJBPDE, control signals can only be determined offline and are thus best suited for feedforward control of plants for which the state trajectories are known a priori. Therefore, contrary to the dynamic programming approach, the resultant control law is not generally in feedback form. Open-loop control, however, is sensitive to random disturbances and requires that the initial state be on the optimal trajectory. In contrast, nonlinear optimal feedback has inherent robustness properties (inherent in the sense that it is obtained by ignoring uncertainty and disturbances). The potential difficulty with the HJB approach is that no efficient algorithm is available to solve the PDE when it is nonlinear and the problem dimension is high, making it impossible to derive exact expressions for optimal controls for most nontrivial problems of interest. The optimal can only be computed in special cases, such as linear dynamics and quadratic cost, or very low-dimensional systems. In particular, if the plant is linear time invariant (LTI) and the (infinite-time) performance index is quadratic, then the corresponding HJB equation for this infamous linear-quadratic regulator (LQR) problem reduces to an algebraic Riccati equation (ARE). Contrary to the well-developed and widely applied theory and computational tools for theRiccati equation (for example, see [1]), theHJB equation is difficult, if not impossible, to solve for most practical applications. The exact solution for the optimal control policies is very complex

In this paper, we consider a ground terminal (GT) to an unmanned aerial vehicle (UAV) wireless communication system where data from GTs are collected by an unmanned aerial vehicle. We propose to use the ground terminal-UAV (G-U) region for the energy consumption model. In particular, to fulfill the data collection task with a minimum energy both of the GTs and UAV, an algorithm that combines optimal trajectory design and resource allocation scheme is proposed which is supposed to solve the optimization problem approximately. We initialize the UAV’s trajectory firstly. Then, the optimal UAV trajectory and GT’s resource allocation are obtained by using the successive convex optimization and Lagrange duality. Moreover, we come up with an efficient algorithm aimed to find an approximate solution by jointly optimizing trajectory and resource allocation. Numerical results show that the proposed solution is efficient. Compared with the benchmark scheme which did not adopt optimizing trajectory, the solution we propose engenders significant performance in energy efficiency.

PurposeThis paper aims to present an optimal trajectory planning for industrial MOTOMAN MA1440A gas metal arc welding system. A new and efficient evolutionary algorithm, enhanced multi-objective teaching learning-based optimization (EMOTLBO) method, i.e. TLBO with non-dominated sorting approach has been proposed to obtain the optimal joint trajectory for the defined weld seam path.Design/methodology/approachThe joint trajectory of the welding robot need to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the weld path and for achieving higher positional accuracy. This can be achieved by limiting the kinematic and dynamic variations of the robot joints like joint jerks, squared acceleration and torque induced in the joints while travel of the robot along the weld path. Also, the robot travel should be done within minimum possible time for maintaining productivity. This leads to a multi-objective optimization problem which needs to be solved for maintaining proper orientation of the robot end effector. EMOTLBO has been proposed to obtain the Pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the Pareto front with best trade-off between objectives.FindingsThe proposed method has been implanted in MATLAB R2017a for simulation results. The joint positions have been used to program the robot for performing welding operation along the weld seam. From the simulation and experimental results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of MOTOMAN MA 1440 A arc welding robot system as a very smooth and uniform weld bead has been obtained with maximum weld quality.Originality/valueIn this paper, a novel approach for optimal trajectory planning welding arc robot has been performed. Though trajectory planning of industrial robots has been done before, it has not been done yet for welding robot. The objectives are formulated taking in consideration of requirement of welding process like minimization of joint jerks and torques induced during welding operation due to travel of robot with the effect of arc spatter, minimization of squared acceleration for maintaining constant joint velocity and finally minimization of total travel time for maintaining productivity.

The applications of two typical swarm intelligence algorithms in the optimization of the reentry trajectory for the hypersonic gliding vehicles are discussed in our paper. A trajectory optimization strategy based on the swarm intelligence algorithms is presented. Firstly, a penalty function is constructed to deal with the inequality constraint functions. Secondly, the attack angle commands of the vehicles are considered as the parameters to be optimized, and on the basis of the mathematical model of the hypersonic vehicles, the optimal law of the attack angle is considered as the input for the trajectory optimization. Finally, two kinds of swarm intelligence algorithms are applied to handle this difficult optimization problem. Numerical simulations have demonstrated the effectiveness of the swarm intelligence algorithms for the complex and large-scale trajectory optimization problem. Our work lays a solid basis for the schematic trajectory design of the hypersonic gliding vehicles.KeywordsTrajectory optimizationswarm intelligencereentryhypersonic gliding vehicles

The problem of the optimum rapid braking of an axisymmetric solid rotating around its centre of mass

Quadruped animation is a ever-growing field having enormous complex parameters of motion due to vide range of gait patterns. In this paper we discuss the problem of generating quadruped motion automatically by first predicting and generating footprints of quadruped motion based on number of steps and stride length and stride height. The planning and prediction algorithms is able to generate autonomous motion, which dynamically generates new footprints and associated new gait motions. The purposed model is using procedural programming technique to calculate the foot placement values for quadruped based on center of mass and trajectory optimization.