Search Trajectory Research Articles

A 2-D Trajectory Design Algorithm for Multiple Asteroid Flyby Missions

Asteroid mining is one of the most promising private space ventures of the near future. Near-Earth Asteroids (NEAs), i.e. those with perihelion at less than 1.3 AU from the Sun, are among the best candidates for such venture. In preparation of mining expeditions, it is likely that prospector missions will be carried out well in advance so to assess the accessibility, potential for revenues and possible critical issues of target asteroids. This work is concerned with the problem of the feasibility of a single spacecraft prospector mission capable of visiting as many NEAs as possible in one shot, focusing on Apollo-class asteroids only. The search of possible trajectories is done assuming a chemically propelled spacecraft with realistic specific impulse and propellant mass ratio, so to allow for a credible mission design with a reasonable, cost-effective total duration. In order to restrict the number of possible trajectories, only those that lie in the plane of the ecliptic are examined; such trajectories can be reached from the Earth without expensive plane change maneuvers. The search for a maximum number of encounters is thus restricted to those occurring where the asteroid orbit crosses the ecliptic. A deterministic building blocks approach is adopted, dividing the optimization problem in two parts: a local optimization for possible target determination; and a global optimization for the choice of the overall trajectory. It is found that the combined approach leads to the identification of viable trajectories, able to perform a number of encounters that depends on the launch epoch; as an example, in one test case two different sets of 21 NEA’s each were identified that could be reached with a single launch, with a slightly different propellant expenditure. It is concluded that the method is well suited to perform feasibility studies of NEA missions with good accuracy and moderate computational cost.

Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm

The existing methodologies used for multilevel thresholding are not efficient in terms of both accuracy and computation time. Two-dimensional histogram-based techniques are better in terms of accuracy while they are computation intensive. The slime mould algorithm used for optimization mainly depends on the best leader and two randomly pooled slime moulds from the population, which leads to poor exploitation with more iteration to converge. These problems are solved in this paper by introducing a novel normalized square difference (NSD) based multilevel thresholding technique using a leader slime mould algorithm (LSMA). The contributions are many fold – i) a NSD based multilevel thresholding method is proposed using the gray level and normalized square difference (GLNSD) 2-D histogram with reduced computation; ii) LSMA is suggested; iii) 23 classical and 6 modern composition test functions from the IEEE CEC 2014 test suite are considered for evaluation of LSMA; iv) experiments on multispectral images are presented. The benefits are – i) reduces computations, ii) improves accuracy. The qualitative metrics used for analysis include – search history, trajectory, and average fitness history. Scalability analysis and statistical analysis (using Friedman's mean rank test) are presented. The proposal is compared with state-of-the-art techniques and found better.

Application of Lagrangian coherent structures to Coulomb formation on elliptic orbit

Coulomb thrusting presents attractive prospects in many astronautical missions from the standpoint of close-proximity formation flying. In this study, a dynamical model of Coulomb formation and its Hamiltonian is derived on the basis of the Tschauner–Hempel equation. As a nonautonomous system, it is more complicated than the dynamical model based on the Clohessy–Wiltshire equation. The Lagrangian coherent structure (LCS), a useful tool for describing the dynamical behavior of nonautonomous systems, is used to study the Coulomb formation dynamics, design, and reconfiguration. Simulation results show that, in the autonomous case of Coulomb formation, the LCS coincides with the invariant manifolds in the proper Poincare section. When it is extended to the nonautonomous case with a small-eccentricity reference orbit, the global morphology of the Coulomb formation dynamics remains almost unchanged. Based on the property that the LCS can act as the transport barrier in dynamical systems, it is used to construct homoclinic and heteroclinic orbits and search for invariant relative orbits and transfer trajectories for formation design and reconfiguration.

Context-aware similarity of GPS trajectories

ABSTRACT Measuring similarity of GPS trajectories has attracted a lot of attention in recent years. As a result, multiple trajectory similarity measures have been developed and are used in a wide set of applications which aim to extract meaningful information from large collections. In this paper, we focus on some of the most popular measures and study how they all can be adapted to use contextual information. We experiment using the buildings in an urban setting as the context and demonstrate how it impacts the similarity values. Experiments show that routes rank differently in terms of similarity in the presence of context which can have serious implications in applications such as trajectory search and clustering similar trajectories.

Motion-encoded particle swarm optimization for moving target search using UAVs

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.

A novel local search method for LSGO with golden ratio and dynamic search step

Depending on the developing technology, large-scale problems have emerged in many areas such as business, science, and engineering. Therefore, large-scale optimization problems and solution techniques have become an important research field. One of the most effective methods used in this research field is memetic algorithm which is the combination of evolutionary algorithms and local search methods. The local search method is an important part that greatly affects the memetic algorithm’s performance. In this paper, a novel local search method which can be used in memetic algorithms is proposed. This local search method is named as golden ratio guided local search with dynamic step size (GRGLS). To evaluate the performance of proposed local search method, two different performance evaluations were performed. In the first evaluation, memetic success history-based adaptive differential evolution with linear population size reduction and semi-parameter adaptation (MLSHADE-SPA) was chosen as the main framework and comparison is made between three local search methods which are GRGLS, multiple trajectory search local search (MTS-LS1) and modified multiple trajectory search. In the second evaluation, the improved MLSHADE-SPA (IMLSHADE-SPA) framework which is a combination of MLSHADE-SPA framework and proposed local search method (GRGLS) was compared with some recently proposed nine algorithms. Both of the experiments were performed using CEC’2013 benchmark set designed for large-scale global optimization. In general terms, the proposed method achieves good results in all functions, but it performs superior on overlapping and non-separable functions.

Vessel Trajectory Prediction Using Historical Automatic Identification System Data

For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.

Efficient and effective similar subtrajectory search with deep reinforcement learning

Similar trajectory search is a fundamental problem and has been well studied over the past two decades. However, the similar subtrajectory search (SimSub) problem, aiming to return a portion of a trajectory (i.e., a subtrajectory), which is the most similar to a query trajectory, has been mostly disregarded despite that it could capture trajectory similarity in a finer-grained way and many applications take subtrajectories as basic units for analysis. In this paper, we study the SimSub problem and develop a suite of algorithms including both exact and approximate ones. Among those approximate algorithms, two that are based on deep reinforcement learning stand out and outperform those non-learning based algorithms in terms of effectiveness and efficiency. We conduct experiments on real-world trajectory datasets, which verify the effectiveness and efficiency of the proposed algorithms.

Generalized Optimal Control for Autonomous Mine Countermeasures Missions

This article presents a computational framework for planning mine countermeasures (MCM) search missions by autonomous vehicles. It employs generalized optimal control (GenOC), a model-based trajectory optimization approach, to maximize the expected search performance of vehicle-sensor pairs in different minehunting scenarios. We describe each element of the proposed framework and adapt it to solve real-world MCM motion planning problems. A key contribution of this article develops sensor models that are more tunable than conventional ones based on lateral range curves. The proposed models incorporate engineering parameters and 3-D geometry to compute mine detection probability as a function of sonar design and search vehicle trajectories. Specific examples for various forward-looking and sidescan sonar systems deployed by unmanned vehicles are included. Objective computations utilize these sonar detection models during optimization to minimize the risk that candidate search trajectories fail to detect mines in an area of interest. Simulation results highlight the flexibility of our proposed GenOC framework and confirm that optimal trajectories outperform conventional search patterns under time or resource constraints. We conclude by identifying some of the practical considerations of this approach, and suggest ways that numerical analysis of GenOC solutions can be used for MCM mission planning and decision aid development.

Development of Recruitment Information and Human Resources in the Chinese Labor Market New Format

The main prerequisites for the transition of the Chinese labor market to a new development paradigm are highlighted. A combination of economic, social and demographic factors that determine fundamentally new aspects of the transformation of the labor market in China is revealed. The economic conditions of functioning of the national labor market are defined. The necessity of using the concept of a "New Format" for the Chinese labor market is justified. The features of the formation of the labor market of the People's Republic of China in the XXI century are revealed. Among the conditions for transforming the functioning of the labor market, attention is focused on the elements of the infrastructure development. It is recognized that within the framework of innovative development, the content model of employment provision is changing most intensively, technologies are improving, and the trajectory of staff search and recruitment is expanding. The article considers the main personnel information resources that characterize the development of recruitment in the conditions of the dynamic development of the labor market. The concept of "personnel information resources" as an element of recruitment services in the digital economy is substantiated. A classification of personnel information resources that offer recruitment services in the Chinese labor market is given. Priority services (personnel information resources) provided in the labor market for the purpose of citizens' employment have been established. The conditions of mismatch between supply and demand in the modern labor market of China are substantiated.

Searching for optimal process routes: A reinforcement learning approach

Developing optimisation tools is a key target in supporting computer-aided process design as the complexity of the designed space grows beyond conventional unit operations. A process design problem can be formulated as a search of an optimal processing route in the thermodynamic state space, going from feedstock to products. This paper describes a design architecture that enables reinforcement learning agent to use trial-and-error to narrow its search to the most promising routes, rather than exhaustively enumerating solutions. In each iteration, the agent employs previously collected data to guide the search for new trajectories. This is successfully demonstrated in a hydrogen production process using both conventional and intensified process design principles. The agent outperformed standard nonlinear optimisation methods in competitive computational time. Limitations and future work are discussed.

Corpus-Level End-to-End Exploration for Interactive Systems

A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.

A heuristic for the minimum cost chromatic partition problem

The graph coloring problem consists in coloring the vertices of a graphG=(V, E)with a minimum number of colors, such as that any two adjacent vertices receive different colors. The minimum cost chromatic partition problem (MCCPP) is an extension of the graph coloring problem in which there are costs associated with the colors and one seeks a vertex coloring minimizing the sum of the costs of the colors used in each vertex. The problem finds applications in VLSI design and in some scheduling problems modeled on interval graphs. We propose a trajectory search heuristic using local search, path-relinking, and perturbations for solving MCCPP and discuss computational results.

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms.

Preoperative assessment to find the safest trajectory in keyhole neurosurgery can reduce post operative complications. We introduced a novel preoperative risk assessment semiautomated methodology based on the sum of N maximum risk values using a generic genetic algorithm for the safest trajectory search. A set of candidates trajectories were found for two surgical procedures. The trajectories search is done using a risk map considering the proximity of voxels within risk structures in multiple points and a genetic algorithm to avoid an exhaustive search. The trajectories were validated by a group of neurosurgeons. The trajectories obtained with the proposal method were shorter in 5% and have greater distance from the voxels within the blood vessels in 4.7%. The use of genetic algorithm (GA) speeds up the search for the safest trajectory, decreasing in 99.9% the time required for an exhaustive search.

Research and Optimization of D-Start Lite Algorithm in Track Planning

With the widespread application of the aircraft, how to achieve the safety and efficiency of the aircraft during flight has attracted widespread attention. The track planned by the traditional D-Start Lite algorithm is very close to the obstacle. Due to the large size of the aircraft itself, it is very likely that the aircraft will collide with the obstacle when operating in a real environment. However, the existing D-Start Lite algorithm is only a single solution to the safety problem of trajectory planning or to improve the efficiency of the algorithm's trajectory search. Therefore, this article optimizes the existing D-Start Lite algorithm. First, this article adopts the strategy of selecting priority level by child nodes. The child nodes in the vertical direction are selected as the first-level nodes. The state of the first-level node determines whether the second-level node on the diagonal needs to be expanded, so that a safe distance is generated between the planned path and the obstacle, thus improving the safety of the path. However, a certain space is reserved between the track and the obstacle, which results in the increase of the number of nodes in the path and the low efficiency of search. This article improves and optimizes the valuation function and heuristic function in the algorithm. Therefore, the number of expansion nodes and the number of inflection points are reduced, and the search efficiency of the algorithm is improved. Therefore, the improved algorithm improves the efficiency of trajectory search while ensuring the safety of trajectory planning. And the effectiveness and feasibility of the algorithm are proved by the MATLAB platform.

Unified Approach to Solving the Trajectory Problem by Usage of Neural Maps

In modern robotics, there are many approaches to planning the trajectory of the portal manipulator. Methods based on the use of graphs, methods of potential fields, with the use of cellular decomposition, intelligent and others are used. Many varieties are based on similar mathematical apparatus. An important aspect of the trajectory search method applicability is convenience of chosen workspace and obstacles representation method. The approach proposed for manipulator path modelling implies a multi-level process, which includes periodic calculation and updating of invalid configurations set, considering the held cargo dimensions, a neural map creation and activation by the wave front propagation method etc. During experimental simulations with different activation functions, numbers of artificial neurons and synaptic connection implementation types, few methods of neural map activation have been considered, including flat and/or three-dimensional raster methods and wave propagation with static obstacles.

Efficient Local Map Search Algorithms for the Placement of Flying Relays

This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for potential blockage of a direct propagation link, we propose an approach capable of leveraging local terrain information to offer performance guarantees. The proposed method allows to strike the best trade-off between minimizing propagation distances to ground terminals and discovering good propagation conditions. The algorithm only requires several propagation parameters, but it is capable to avoid deep propagation shadows and is proven to find the globally optimal UAV position. Only a local exploration over the target area is required, and the maximum length of search trajectory is linear to the geographical scale. Hence, it lends itself to online search. Significant throughput gains are found when compared to other positioning approaches based on statistical propagation models.

Математическое описание вращения точки вокруг эллиптической оси в некоторых частных случаях

Previously, we developed a constructive method for modeling surfaces of rotation with axes, which were second-order curves such as circle, ellipse, parabola and hyperbola [1]. We also described the principle of constructing a mathematical model [23] corresponding to this constructive technique [2], and expressed the method in mathematical form. In this paper, we applied the previously developed mathematical model that allows us to determine the trajectory of rotation of a point around an elliptical axis to some special cases of the location of this point and identified the features of each of them. We applied the previously accepted terminology and the system of designating points, straight and curved lines involved in the search for circular trajectories of rotation of points. We analyzed the cases of the location of the generating point on the coordinate axes. We determined in mathematical form the trajectory of the point located in these positions. This entry is represented as systems of parametrically given equations. The article also describes a step-by-step algorithm used to find the equation of a circle, which is the trajectory of rotation of a point around an elliptic axis. We applied this algorithm to various positions of the generating point relative to the elliptic axis foci. We applied the previously developed criteria for selecting near and far centers of rotation relative to one of the focuses of the ellipse. The results of these mathematical studies will be used in the future to create a computer program capable of generating digital 3D-models of surfaces formed by the rotation of arbitrary sets forming points around the curves of the axes of the second order.

Method for optimal conflict-free aircraft trajectory generation

Purpose A number of automated tools will be required for the purpose of enabling efficient services in air traffic control. The purpose of this paper is to devise an optimal flight trajectory search method that optimizes airspace system efficiency for 3D space in the presence of uncertainties. Design/methodology/approach This paper put forward an optimization model for generating applicable solutions of multi-aircraft conflict resolution problem, and the solution is based on the principle of optimality. Findings The conflict resolution problem between multiple aircraft can be described by spatial discretization, and the approach taken digitizes the 3D space into a grid of nodes. Practical implications The simulation examples are given to illustrate the validity of trajectory search model and stress on the impact of different system parameters. Originality/value Realistic constraints that are convenient to operate are incorporated in the system model, and the results show that it can provide reliable decision-making for conflict avoidance.

Rendezvous design in a cislunar near rectilinear Halo orbit

ABSTRACTIn the context of future human spaceflight exploration missions, Rendezvous and Docking (RVD) activities are critical for the assembly and maintenance of cislunar structures. The scope of this research is to investigate the specifics of orbits of interest for RVD in the cislunar realm and to propose novel strategies to safely perform these kinds of operations. This paper focuses on far rendezvous approaches and passively safe drift trajectories in the Ephemeris model. The goal is to exhibit phasing orbit requirements to ensure a safe far approach. Ephemeris representations of Near Rectilinear Halo Orbits (NRHOs) were derived using multiple-shooting and adaptive receding-horizon targeting algorithms. Simulations showed significant drift and overlapping properties for phasing and target orbits of interest, motivating the search for safe natural drift trajectories and using impact prediction strategies.