trajectory optimization

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A multisine approach for trajectory optimization based on information gain

A multisine approach for trajectory optimization based on information gain

This paper presents a multisine approach for trajectory optimization based on information gain, with distance and orientation sensing to known beacons. It addresses the problem of active sensing, i.e. the selection of a robot motion or sequence of motions, which make the robot arrive in its desired goal configuration (position and orientation) with maximum accuracy, given the available sensor information. The optimal trajectory is parameterized as a linear combination of sinusoidal functions. An appropriate optimality criterion is selected which takes into account various requirements (e.g. maximum accuracy and minimum time). Several constraints can be formulated, e.g. with respect to collision avoidance. The optimal trajectory is then determined by numerical optimization techniques. The approach is applicable to both nonholonomic and holonomic robots. Its effectiveness is illustrated here for a nonholonomic wheeled mobile robot (WMR) in an environment with and without obstacles.
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Gauss Pseudospectral Method Based Trajectory Optimization for Hypersonic Glide Vehicles

Gauss Pseudospectral Method Based Trajectory Optimization for Hypersonic Glide Vehicles

The trajectory optimization for aircraft has always been a concern and a complex problem [1-3] . The most significant feature of glide trajectory optimization problem distinguished from other trajectory optimization problems is that hypersonic glide vehicles fly at high speed in the near-space for a long time, and the aerodynamic and thermal environment is very bad. Therefore, how to meet the aerodynamic and thermal constraints are key problems for hypersonic glide trajectory optimization. In addition, the trajectory optimization of hypersonic glider needs to consider various constraints to meet specific flight tasks. The trajectory optimization design under such many constraints is a complex problem. In order to solve this problem, based on the flight characteristics of hypersonic gliders, appropriate strategies and optimization methods should be adopted [4-5] .
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An Improved PSO Algorithm with Object- Oriented Performance Database for Flight Trajectory Optimization

An Improved PSO Algorithm with Object- Oriented Performance Database for Flight Trajectory Optimization

using the modified particle swarm optimization algorithm. According to object-oriented principle, the class and their inheritance are designed. Through designs the operating function library for data table processing, the complicated performance data can be disposed and calculated conveniently. Based on the proposed data structure, the data files are reorganized and classified into a unified format which can be easily stored and maintained. And by replacing the data file, the performance database can be expanded into different types for various airplanes. The particle swarm optimization algorithm is modified by introducing adaptive inertia factor. Meanwhile, by combination with the penalty function method, the trajectory optimization with constraint conditions is converted into unconstrained optimization problem. Based on the modified PSO algorithm, the optimal vertical trajectory of Boeing 737-800 is calculated. Comparison between optimization and experiment results reveal that the optimal solution acquired by modified PSO algorithm is approximately equal to actual flight data and the modified algorithm convergence faster than other algorithms.
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RBF network based motion trajectory optimization for robot used in agricultural activities

RBF network based motion trajectory optimization for robot used in agricultural activities

At present, the efficiency of the method to track and predict motion trajectory of fruit and vegetable picking robot was low and the realization process was complex. Therefore, a research on motion trajectory optimization of fruit and vegetable picking robot based on RBF network was proposed. After analyzing the reason for data class imbalance of fruit and vegetable picking robot, this paper introduced the processing technology MWMO in RBF network. Then, the MWMO technology was embedded in the tracking and prediction research of motion trajectory optimization of fruit and vegetable picking robot. Moreover, the semi-supervised learning algorithm was used as the framework and integrated the processing technology of data class imbalance of motion trajectory to improve the efficiency of tracking and prediction of fruit and vegetable picking robot. According to the integration result, combined with the idea about the calculation of spatial function and the tracking and prediction of motion trajectory in RBF network, we designed the matching principle of trajectory similarity of time and space and realized the matching between the predicted position and the actual position, so that the tracking and prediction of fruit and vegetable picking robot could be completed. Experimental results show that the average calculation time of proposed method is 2.0S, which is only half of average time of traditional tracking and prediction method. It fully proves that the proposed optimization method can accurately track and predict the motion trajectory of fruit and vegetable picking robot. The prediction efficiency is higher and the time consumptionis shorter.
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Re-entry trajectory optimization for a SSTO vehicle in the presence of atmosheric uncertainties

Re-entry trajectory optimization for a SSTO vehicle in the presence of atmosheric uncertainties

the optimization loop to determine their e ff ect on the vehicle performance. A statistical analysis is conducted on the re- sults in order to produce an expectancy curve which relates the probability of the vehicle having a certain performance to the expected variation due to uncertainty in the system design parameters. The optimization process proposed here is based on a hybrid stochastic-deterministic algorithm which combines a global explorative search and a local search. This approach is revealed to overcome successfully the limitations of gradient-based solvers, as they are affected by the presence of discontinuities in any system models and this makes it di ffi cult to find an optimal solution if a good initial guess is not properly chosen. The paper starts by describing the optimization tool that was developed to solve the re-entry optimal control problem for a representative reusable SSTO and the uncertainty methodology. The succeeding section describes the various model subsystems used to evaluate the spaceplane performance. The specific trajectory optimization problem is then addressed, followed by a discussion of the results.
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An incremental approach to the solution of global trajectory optimization problems

An incremental approach to the solution of global trajectory optimization problems

Solving the problem could be a challenge for every global optimization tool. However, this class of global trajectory optimization problems can be decomposed into sub-problems of smaller complexity and solved incrementally adding one planet at the time. At each incremental step, a portion of the search space can be pruned out. Previous attempts to use an incremental pruning have employed a simplified trajectory representation and a grid sampling of each sub-problem [9]. This approach fails if the accuracy and complexity of the trajectory model are increased, for two reasons: if a course grid and an aggressive pruning are used, many optimal solutions are lost; on the other hand, if a fine grid is used, the computational time becomes unacceptable even for a limited number of planets.
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An inflationary differential evolution algorithm for space trajectory optimization

An inflationary differential evolution algorithm for space trajectory optimization

In this paper we define a discrete dynamical system that governs the evolution of a population of agents. From the dynamical system, a variant of Differential Evolution is derived. It is then demonstrated that, under some assumptions on the differential mutation strategy and on the local structure of the objective function, the proposed dynamical system has fixed points towards which it converges with probability one for an infinite number of generations. This property is used to derive an algorithm that performs better than standard Differential Evolution on some space trajectory optimization problems. The novel algorithm is then extended with a guided restart procedure that further increases the performance, reducing the probability of stagnation in deceptive local minima.
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Solving multiobjective constrained trajectory optimization problem by an extended evolutionary algorithm

Solving multiobjective constrained trajectory optimization problem by an extended evolutionary algorithm

The problem address in this paper is a multi-objective optimal trajectory design for the spacecraft in the reentry phrase. For general engineering multi-objective problems, the evolutionary multi-objective optimization (EMO) methodol- ogy has been illustrated as a promising tool to analyze the relationships between objectives and calculate the pareto-front [21]. New techniques based on EMO have been widely studied during the past decades [22]–[26]. For example, Roy et al. [22] developed an optimal path control strategy for solving general multi-objective optimization problems. Ji et al. [23] designed a modified NSGA-II algorithm to address a multi- objective continuous berth allocation problem. In [24], the author proposed a decomposition-based EMO technique, along with a novel diversity factor, for handling many-objective problems. Moreover, an enhanced many-objective PSO method was proposed in [25], wherein a two-stage strategy was designed so as to better balance the convergence and diversity of the approximated pareto solutions. Furthermore, in [26] a constraint consensus-based mechanism, together with a new mutation operator, was studied for solving multi-objective benchmark problems. However, most of these EMO tech- niques cannot be directly applied to solve the multi-objective spacecraft trajectory design problem. This is because most of these works only targeted at unconstrained problems or problems with algebraic equality and inequality constraints. If an EMO is employed to calculate the multi-objective optimal spacecraft trajectory, a proper treatment of the continuous- time differential constraints is also required. To do this, in this paper, a novel NSGA-III-based optimal control solver is designed and applied to solve a multi-objective spacecraft trajectory optimization problem. So far to the best of the author’s knowledge, there is no adequate work that has been reported to investigate the multi-objective reentry trajectory design problem, and the NSGA-III-based algorithm has not been applied to this kind of problem before. Hence, the present study is an attempt to address these concerns.
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Trajectory Optimization for Chance-Constrained Nonlinear Stochastic Systems

Trajectory Optimization for Chance-Constrained Nonlinear Stochastic Systems

The paper presented an approximate deterministic surro- gate for the stochastic nonlinear optimal control problem with chance constraints for stochastic trajectory optimization. The approach used generalized polynomial chaos (gPC) to derive a deterministic ordinary differential equation for the stochastic differential equation and a deterministic cost function for the expectation cost function. The main theorem showed that a feasible solution of the deterministic optimal control problem is a feasible solution of the stochastic nonlinear optimal control problem. The proposed gPC-SCP is applied to an example problem to obtain a suboptimal feasible trajectory that is guaranteed to avoid collision with the specified probability. The effectiveness of the method is validated by comparing the trajectories obtained from the method with open-loop runs for multiple realizations of stochastic uncertainty using the open-loop control policy obtained.
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Computationally adaptive multi objective trajectory optimization for UAS with variable planning deadlines

Computationally adaptive multi objective trajectory optimization for UAS with variable planning deadlines

During operations, civilian UAS may have multiple objectives to meet. The use of multi-objective optimization allows for the generation of a solution which better reflects the overall mission requirements. Additionally, if operations are undertaken at lower altitudes, the environment may present several challenges not encountered during high altitude flight. Terrain and urban structures become hazards to the safety of the UAS. The proximity of obstacles to the UAS places real-time constraints on the re/planning computation time available. This paper presents a new framework for the Computationally Adaptive Multi-Objective Flight Management of UAS in civilian environments. An outline of UAS trajectory generation approaches and related work is given in section 2. Section 3 presents an overview of the trajectory optimization process, and section 4 outlines the real-time re/planning requirements of UAS operating in cluttered requirements. Simulation results presented in section 5 demonstrate how the addition of the CATD can allow for the generation of feasible trajectories within given real-time deadlines. Finally, conclusions are presented in section 6.
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UAV trajectory optimization for data offloading at the edge of multiple cells

UAV trajectory optimization for data offloading at the edge of multiple cells

In this paper, the UAV trajectory optimization for data offloading in the edge area of multiple cells has been re- searched. In the proposed scheme, three adjacent cells were considered, and the trajectory was optimized to maximize the sum rate of edge users by avoiding the interference between BSs and UAV, with the rate requirements of all the mobile users satisfied. To solve this non-convex problem, it was first transformed into two convex subproblems, and then, an effective algorithm was proposed to calculate the solutions

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System Architecture Optimization Using Hidden Genes Genetic Algorithms with Applications in Space Trajectory Optimization

System Architecture Optimization Using Hidden Genes Genetic Algorithms with Applications in Space Trajectory Optimization

Space trajectory optimization is the process of searching for the optimal trajectory from one celestial body or orbit to another, such that the mission requirements are satisfied and a given objective is optimized. The objective can be minimizing the mission cost or fuel consumption, minimizing the mission duration, maximizing the number of visited asteroids, or a combination of these objectives. The spacecraft can have continuous or impulsive thrusters, for which various trajectory design tech- niques have been developed. In this dissertation, the impulsive thrust spacecraft is considered. The earliest research on space trajectory optimization goes back to the work of Walter Hohmann on trajectory design of a spacecraft with impulsive thrusters between two coplanar orbits [30]. Cornelisse [31] showed that in the patched conics method, the cost of an interplanetary trajectory mission can be reduced by applying a DSM. Several works have studied the effect of DSMs in different space missions [32, 33, 34, 35]. Planetary flybys utilize the gravity of a planet to change the mo- mentum vector of a spacecraft. Such trajectories that use DSMs and flybys are called Multi Gravity-Assist Deep Space Maneuver (MGADSM) trajectories.
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A Direct Multiple Shooting Method for Missile Trajectory Optimization with The Terminal Bunt Manoeuvre

A Direct Multiple Shooting Method for Missile Trajectory Optimization with The Terminal Bunt Manoeuvre

The optimal control problem is solved by numerical method using a direct multiple shooting. Direct multiple shooting to trajectory optimization is generally based on the discretisation of control and/or state variables. The basic idea of the direct multiple shooting methods is to transform the original optimal control problem into nonlinear programming problem by coupling the control parameterisation with a multiple shooting discretisation of the state variables [8-10]. The control can be approximated by piecewise functions and the state variables are approximated at the shooting nodes t i (see
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Trajectory Optimization of Pneumatic Marking Needle Based on Genetic Algorithm

Trajectory Optimization of Pneumatic Marking Needle Based on Genetic Algorithm

Above all, the trajectory optimization of the marking machine can be described as following: The points in the set of S are { s 1 ( x 1 , y 1 ), s 2 ( x 2 , y 2 ),..., s n ( x n , y n ) } . The marking needle prints the above points from the initial position and finally returns to the initial position, requiring the design of the print trajectory route, so that the total print target function (time target, distance target) can achieve the minimum value, namely the track optimization.

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Simulation of Robot Manipulator Trajectory Optimization Design

Simulation of Robot Manipulator Trajectory Optimization Design

This article is mainly for energy and time-consuming two aspects of optimization. In the traditional joint space trajectory planning, the energy consumption is usually solved directly by the velocity and acceleration of the joints, and then the kinetic model is used to solve the energy consumption. In this paper, a trajectory of the end is preliminarily set for the problem of terminal trajectory optimization. Then, the velocity and acceleration are programmed. The velocity, acceleration and kinetic model of the joint are calculated by the inverse kinematics of the robot. Finally, we use the optimization algorithm to find the optimal trajectory of various performance indexes on the basis of the preset end trajectory.
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Joint-space recipes for manipulator robots performing compliant motion tasks : trajectory-optimization, interpolation, and control

Joint-space recipes for manipulator robots performing compliant motion tasks : trajectory-optimization, interpolation, and control

The major goal of this thesis is to bring a new thought of robot manipulator’s com- pliant motion control concerning both planning and control aspects. Due to the natural easiness of describing motion constraints and the simplified treatment of robot’s kinematic/dynamic models, operational-space is taken for granted as the de- fault coordinate frame of robot’s hybrid motion/force control formulation, especially after Khatib’s historic work [37]. However, we believe joint-space is in fact a better choice in terms of global optimization, robust performance, and general applicability. Apart from these desirable benefits, the corresponding price of introducing joint-space processing is its computational complicity, as the robot manipulator’s kinematic or dynamic features are now considered in reference trajectory and control algorithm de- signs. To cope with such technical difficulties, we have proposed numerical trajectory- optimization, interpolation, and control algorithms, which have been presented in the previous chapters.
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Trajectory optimization for the Hevelius-lunar microsatellite mission

Trajectory optimization for the Hevelius-lunar microsatellite mission

In this paper trajectory optimisation for the Hevelius mission is presented. The Hevelius – Lunar Microsatellite Mission – is a multilander mission to the dark side of the Moon, supported by a relay microsatellite, orbiting on a Halo orbit around L2. Three landers, with miniaturized payloads, are transported by a carrier from a LEO to the surface of the Moon, where they perform a semi-hard landing with an airbag system. This paper will present the trajectory optimisation process, focusing, in particular, on the approach employed for Δv manoeuvre optimization. An introduction to the existing methods for trajectory optimization will be presented, subsequently it will be described how these methods have been exploited and originally combined in the Hevelius mission analysis and design.
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Trajectory Optimization for Satellite Reconfiguration Maneuvers with Position and Attitude Constraints

Trajectory Optimization for Satellite Reconfiguration Maneuvers with Position and Attitude Constraints

The problem of finding a feasible path between two points with an arbitrary number of constraints is a classical path planning problem that has been studied for many years. The difficulty of this problem makes it intractable to find a solution with guarantees for problems with more than a few (e.g., 3–6) dimensions. However, by relaxing the guaranteed completion, randomized path planning algorithms such as the Probabilistic Roadmaps (PRM) have been successful in solving larger problems [12]. Rapidly-exploring Random Trees (RRT), a more recent variant of these randomized planners, is used in this paper because it performed better than other algorithms in our experimental comparisons [1]. The path planning problem is posed without differential constraints. In general these additional constraints limit the expansion of the randomized search trees and increase the solution time. Instead, the direct trajectories between two points consist of rest-to-rest straight-line translations and eigen-axis rotations at constant rates. Since the spacecraft is at rest at each node of the search tree, a branch of the search tree can grow in any direction. The algorithm generates a trajectory consisting of a sequence of states, connected by these feasible “direct trajectories”. This trajectory is then passed to the smoother.
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A hybrid multiagent approach for global trajectory optimization

A hybrid multiagent approach for global trajectory optimization

Four stochastic methods were considered: a basic best start algorithm (called BS in the table) that samples uniformly the solution space and then starts a local search from the best sample, DEVEC [17] an implementation of differen- tial evolution, GATBX a Matlab implementation of genetic algorithms [4] and PSO an implementation of Particle Swarm Optimization [7]. Since for each stochastic method a number of parameters needs to be set (for example number of generations and size of the population for genetic algorithms), different set- tings were tested and the best result found over all the tested settings has been reported. This is a very important point; in fact, it would be erroneous to use, for example, the same population size for two population-based methods like GA and DE since they work on different principles. From the results in Table 1 it can be seen how the stochastic methods provide on average a better answer than deterministic methods for a low number of function evaluations though they fail at guaranteeing convergence.
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The impact of strategic trajectory optimization on illusory target biases during goal-directed aiming

The impact of strategic trajectory optimization on illusory target biases during goal-directed aiming

The multiple process model of limb control posits two types of online control during goal-directed reaching and aiming: early impulse regulation, and late limb-target control (Elliott, Hansen, Grierson, Lyons, Bennett, & Hayes, 2010). The early impulse regulation modulates limb velocity and direction, and depends on feedforward processes involving a comparison between the predicted and actual sensory consequences (Desmurget & Grafton, 2000; Wolpert, Miall, & Kawato, 1998). In contrast, limb-target control occurs toward the end of the movement trajectory as the limb approaches the target. It constitutes discrete corrective processes based on the spatial position of the moving limb with respect to the target location.
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