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Journal articles on the topic 'Trajectory search'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Xie, Dong, Feifei Li, and Jeff M. Phillips. "Distributed trajectory similarity search." Proceedings of the VLDB Endowment 10, no. 11 (August 2017): 1478–89. http://dx.doi.org/10.14778/3137628.3137655.

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2

Tedjopurnomo, David Alexander, Xiucheng Li, Zhifeng Bao, Gao Cong, Farhana Choudhury, and A. K. Qin. "Similar Trajectory Search with Spatio-Temporal Deep Representation Learning." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (December 31, 2021): 1–26. http://dx.doi.org/10.1145/3466687.

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Similar trajectory search is a crucial task that facilitates many downstream spatial data analytic applications. Despite its importance, many of the current literature focus solely on the trajectory’s spatial similarity while neglecting the temporal information. Additionally, the few papers that use both the spatial and temporal features based their approach on a traditional point-to-point comparison. These methods model the importance of the spatial and temporal aspect of the data with only a single, pre-defined balancing factor for all trajectories, even though the relative spatial and temporal balance can change from trajectory to trajectory. In this article, we propose the first spatio-temporal, deep-representation-learning-based approach to similar trajectory search. Experiments show that utilizing both features offers significant improvements over existing point-to-point comparison and deep-representation-learning approach. We also show that our deep neural network approach is faster and performs more consistently compared to the point-to-point comparison approaches.

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3

Shao, Xiao Fang, and Hong Chen. "Directional Search for Spiral Trajectory Extraction." Applied Mechanics and Materials 713-715 (January 2015): 577–80. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.577.

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Spiral trajectory curves often occur in modern production lines. In this paper we propose a curve inference method for spiral trajectory extraction. Based on the tensor voting result of the original images, the method performs a post-processing stage and a directional neighborhood searching process which takes into account the turning angle of the pixels on a certain curve. At last the method is tested on several real images.

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4

Chen, Wei, Lei Zhao, Jia-Jie Xu, Guan-Feng Liu, Kai Zheng, and Xiaofang Zhou. "Trip Oriented Search on Activity Trajectory." Journal of Computer Science and Technology 30, no. 4 (July 2015): 745–61. http://dx.doi.org/10.1007/s11390-015-1558-6.

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5

Chen, Mingming, Ning Wang, Guofeng Lin, and Jedi S. Shang. "Network-Based Trajectory Search over Time Intervals." Big Data Research 25 (July 2021): 100221. http://dx.doi.org/10.1016/j.bdr.2021.100221.

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6

Qi, Shuyao, Dimitris Sacharidis, Panagiotis Bouros, and Nikos Mamoulis. "Snapshot and continuous points-based trajectory search." GeoInformatica 21, no. 4 (August 17, 2016): 669–701. http://dx.doi.org/10.1007/s10707-016-0267-9.

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7

Zhao, Peng, Weixiong Rao, Chengxi Zhang, Gong Su, and Qi Zhang. "SST: Synchronized Spatial-Temporal Trajectory Similarity Search." GeoInformatica 24, no. 4 (April 28, 2020): 777–800. http://dx.doi.org/10.1007/s10707-020-00405-y.

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8

Wang, Hongzhi, and Amina Belhassena. "Parallel trajectory search based on distributed index." Information Sciences 388-389 (May 2017): 62–83. http://dx.doi.org/10.1016/j.ins.2017.01.016.

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9

Psiaki, M. L., and K. H. Park. "Parallel solver for trajectory optimization search directions." Journal of Optimization Theory and Applications 73, no. 3 (June 1992): 519–46. http://dx.doi.org/10.1007/bf00940054.

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10

Alexandropoulos, Stamatios-Aggelos N., Panos M. Pardalos, and Michael N. Vrahatis. "Dynamic search trajectory methods for global optimization." Annals of Mathematics and Artificial Intelligence 88, no. 1-3 (August 27, 2019): 3–37. http://dx.doi.org/10.1007/s10472-019-09661-7.

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11

Ribeiro de Almeida, Damião, Cláudio de Souza Baptista, and Fabio Gomes de Andrade. "Similarity Search on Semantic Trajectories Using Text Processing." ISPRS International Journal of Geo-Information 11, no. 7 (July 21, 2022): 412. http://dx.doi.org/10.3390/ijgi11070412.

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The use of location-based sensors has increased exponentially. Tracking moving objects has become increasingly common, consolidating a new field of research that focuses on trajectory data management. Such trajectories may be semantically enriched using sensors and social media. This enables a detailed analysis of trajectory behavior patterns. One of the problems in this field is the search for a semantic trajectory database that is flexible and adaptable; flexibility in the sense of retrieving trajectories that are closest to the user’s query and not just based on exact matching. Adaptability refers to adjusting to different types of semantic trajectories. This article proposes a new approach for representing and querying semantic trajectories based on text-processing techniques. Furthermore, we describe a framework, called SETHE (SEmantic Trajectory HuntEr), that performs similarity queries on semantically enriched trajectory databases. SETHE can be adapted according to the aspect types posed in user queries. We also presented an evaluation of the proposed framework using a real dataset, and compare our results with those of state-of-the-art approaches.

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12

Ochoa, Gabriela, Katherine M. Malan, and Christian Blum. "Search trajectories illuminated." ACM SIGEVOlution 14, no. 2 (July 2021): 1–5. http://dx.doi.org/10.1145/3477379.3477381.

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This article summarizes our recent journal paper entitled "Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics", where we propose a graph-based, data-driven modeling tool (STNs) to visualize and analyze the dynamics of any type of metaheuristic (evolutionary, swarm-based or single-point).

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13

Deng, Liwei, Hao Sun, Rui Sun, Yan Zhao, and Han Su. "Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach." ACM Transactions on Intelligent Systems and Technology 13, no. 3 (June 30, 2022): 1–22. http://dx.doi.org/10.1145/3456723.

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Although many applications take subtrajectories as basic units for analysis, there is little research on the similar subtrajectory search problem aiming to return a portion of a trajectory (i.e., subtrajectory), which is the most similar to a query trajectory. We find that in some special cases, when a grid-based metric is used, this problem can be formulated as a reading comprehension problem, which has been studied extensively in the field of natural language processing (NLP). By this formulation, we can obtain faster models with better performance than existing methods. However, due to the difference between natural language and trajectory (e.g., spatial relationship), it is impossible to directly apply NLP models to this problem. Therefore, we propose a Similar Subtrajectory Search with a Graph Neural Networks framework. This framework contains four modules including a spatial-aware grid embedding module, a trajectory embedding module, a query-context trajectory fusion module, and a span prediction module. Specifically, in the spatial-aware grid embedding module, the spatial-based grid adjacency is constructed and delivered to the graph neural network to learn spatial-aware grid embedding. The trajectory embedding module aims to model the sequential information of trajectories. The purpose of the query-context trajectory fusion module is to fuse the information of the query trajectory to each grid of the context trajectories. Finally, the span prediction module aims to predict the start and the end of a subtrajectory for the context trajectory, which is the most similar to the query trajectory. We conduct comprehensive experiments on two real world datasets, where the proposed framework outperforms the state-of-the-art baselines consistently and significantly.

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14

Trumbauer, Eric, and Benjamin Villac. "Heuristic Search-Based Framework for Onboard Trajectory Redesign." Journal of Guidance, Control, and Dynamics 37, no. 1 (January 2014): 164–75. http://dx.doi.org/10.2514/1.61236.

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15

SASAKI, Toru, Tomohito TAKUBO, and Atsusi UENO. "Trajectory Planning for Search Targets in Known Environments." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 1P1—H04. http://dx.doi.org/10.1299/jsmermd.2018.1p1-h04.

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16

Wei, Ling-Yin, Wen-Chih Peng, and Wang-Chien Lee. "Exploring pattern-aware travel routes for trajectory search." ACM Transactions on Intelligent Systems and Technology 4, no. 3 (June 2013): 1–25. http://dx.doi.org/10.1145/2483669.2483681.

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17

Foraker, Joseph, Johannes O. Royset, and Isaac Kaminer. "Search-Trajectory Optimization: Part I, Formulation and Theory." Journal of Optimization Theory and Applications 169, no. 2 (June 16, 2015): 530–49. http://dx.doi.org/10.1007/s10957-015-0768-y.

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Foraker, Joseph, Johannes O. Royset, and Isaac Kaminer. "Search-Trajectory Optimization: Part II, Algorithms and Computations." Journal of Optimization Theory and Applications 169, no. 2 (June 16, 2015): 550–67. http://dx.doi.org/10.1007/s10957-015-0770-4.

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19

Ata, Atef A., and Thi Rein Myo. "Optimal Point-to-Point Trajectory Tracking of Redundant Manipulators using Generalized Pattern Search." International Journal of Advanced Robotic Systems 2, no. 3 (September 1, 2005): 24. http://dx.doi.org/10.5772/5781.

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Optimal point-to-point trajectory planning for planar redundant manipulator is considered in this study. The main objective is to minimize the sum of the position error of the end-effector at each intermediate point along the trajectory so that the end-effector can track the prescribed trajectory accurately. An algorithm combining Genetic Algorithm and Pattern Search as a Generalized Pattern Search GPS is introduced to design the optimal trajectory. To verify the proposed algorithm, simulations for a 3-D-O-F planar manipulator with different end-effector trajectories have been carried out. A comparison between the Genetic Algorithm and the Generalized Pattern Search shows that The GPS gives excellent tracking performance.

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20

Simon, Salomé, and Gabriele Röger. "Finding and Exploiting LTL Trajectory Constraints in Heuristic Search." Proceedings of the International Symposium on Combinatorial Search 6, no. 1 (September 1, 2021): 113–21. http://dx.doi.org/10.1609/socs.v6i1.18361.

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We suggest the use of linear temporal logic (LTL) for expressing declarative information about optimal solutions of search problems. We describe a general framework that associates LTLf formulas with search nodes in a heuristic search algorithm. Compared to previous approaches that integrate specific kinds of path information like landmarks into heuristic search, the approach is general, easy to prove correct and easy to integrate with other kinds of path information.

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21

Chen, Ren-Raw, Cameron D. Miller, and Puay Khoon Toh. "Modeling Firm Search and Innovation Trajectory Using Swarm Intelligence." Algorithms 16, no. 2 (January 22, 2023): 72. http://dx.doi.org/10.3390/a16020072.

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We developed a swarm intelligence-based model to study firm search across innovation topics. Firm search modeling has primarily been “firm-centric,” emphasizing the firm’s own prior performance. Fields interested in firm search behavior—strategic management, organization science, and economics—lack a suitable simulation model to incorporate a more robust set of influences, such as the influence of competitors. We developed a swarm intelligence-based simulation model to fill this gap. To demonstrate how to fit the model to real world data, we applied latent Dirichlet allocation to patent abstracts to derive a topic search space and then provide equations to calibrate the model’s parameters. We are the first to develop a swarm intelligence-based application to study firm search and innovation. The model and data methodology can be extended to address a number of questions related to firm search and competitive dynamics.

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22

Lu, Kunfeng, Ruiguang Hu, Zheng Yao, and Huixia Wang. "Onboard Distributed Trajectory Planning through Intelligent Search for Multi-UAV Cooperative Flight." Drones 7, no. 1 (December 26, 2022): 16. http://dx.doi.org/10.3390/drones7010016.

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Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, the Monte Carlo tree search (MCTS) is used as the task allocation algorithm to solve the cooperative obstacle avoidance problem. Taking the task allocation decisions as the constraint, knowledge-based particle swarm optimization (Know-PSO) is used as the optimization algorithm to solve the onboard distributed cooperative trajectory planning problem. Simulation results demonstrate that the proposed intelligent MCTS-PSO search framework is effective and flexible for multiple UAVs to conduct the cooperative trajectory planning and obstacle avoidance. Further, it has been applied in practical experiments and achieved promising results.

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23

Sushnigdha, Gangireddy. "Spacecraft Reentry Trajectory Optimization using Search Space Reduction Technique." IFAC-PapersOnLine 55, no. 1 (2022): 46–51. http://dx.doi.org/10.1016/j.ifacol.2022.04.008.

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24

MOHRI, Akira, Go HIRANO, and Motoji YAMAMOTO. "Trajectory Planning for Cooperative Multiple Manipulators with Path Search." Transactions of the Society of Instrument and Control Engineers 34, no. 6 (1998): 532–37. http://dx.doi.org/10.9746/sicetr1965.34.532.

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25

Pleter, Octavian Thor, Cristian Emil Constantinescu, and Barna Istvan Jakab. "Reconstructing the Malaysian 370 Flight Trajectory by Optimal Search." Journal of Navigation 69, no. 1 (July 30, 2015): 1–23. http://dx.doi.org/10.1017/s0373463315000570.

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In the aftermath of the disappearance of the Malaysian 370 (MH370) flight in March 2014, new positioning methods were employed to establish the search area. In the absence of all other positioning technologies (Transponder, Radio communications, Radar), these innovative methods are based on the handshake signals between an INMARSAT satellite and the satellite transceiver on board the aircraft. The log of these signals was made public in order for the scientific community to engage in solving the mystery of the MH370 trajectory. The log indicates the delay between the interrogation and response signals, as well as the relative velocity indications, based on the shift of the carrier frequency due to the Doppler-Fizeau effect. This paper puts forward an original, independent and accurate positioning method and allows the calculation of the MH370 trajectory considering the wind vector field that day, the accurate satellite orbit and an accurate model of the Earth (the WGS-84 ellipsoid). The results were compared to other results published, indicating a different final position of the aircraft from the locations of the published search area.

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26

Naresh Kumar, G., Mohammad Ikram, A. K. Sarkar, and S. E. Talole. "Hypersonic flight vehicle trajectory optimization using pattern search algorithm." Optimization and Engineering 19, no. 1 (September 21, 2017): 125–61. http://dx.doi.org/10.1007/s11081-017-9367-0.

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27

Karbowska-Chilinska, Joanna, Jolanta Koszelew, Krzysztof Ostrowski, Piotr Kuczynski, Eric Kulbiej, and Piotr Wolejsza. "Beam Search Algorithm for Ship Anti-Collision Trajectory Planning." Sensors 19, no. 24 (December 4, 2019): 5338. http://dx.doi.org/10.3390/s19245338.

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The biggest challenges in the maritime environment are accidents and excessive fuel consumption. In order to improve the safety of navigation at sea and to reduce fuel consumption, the strategy of anti-collision, shortest trajectory planning is proposed. The strategy described in this paper is based on the beam search method. The beam search algorithm (BSA) takes into account many safe trajectories for the present ship and chooses the best in terms of length and other criteria. The risk of collision of present ship with any target ships is detected when the closest point of approach (CPA) of the present ship is violated by the target ship’s planned trajectory. Only course alteration of the present ship is applied, and not speed alteration. The algorithm has been implemented in the decision support system NAVDEC and tested in a real navigation environment on the m/f Wolin, a Polish ferry. Almost all BSA trajectories calculated were shorter in comparison to the standard NAVDEC-calculated algorithm.

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28

Li, Weiqi. "Dynamics of Local Search Trajectory in Traveling Salesman Problem." Journal of Heuristics 11, no. 5-6 (December 2005): 507–24. http://dx.doi.org/10.1007/s10732-005-3604-y.

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29

Raap, Manon, Martin Zsifkovits, and Stefan Pickl. "Trajectory optimization under kinematical constraints for moving target search." Computers & Operations Research 88 (December 2017): 324–31. http://dx.doi.org/10.1016/j.cor.2016.12.016.

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30

Hu, Hanjie, Yu Wu, Jinfa Xu, and Qingyun Sun. "Cuckoo search-based method for trajectory planning of quadrotor in an urban environment." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 12 (February 2019): 4571–82. http://dx.doi.org/10.1177/0954410019827395.

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Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.

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31

Guo, Fumin, Hua Zhang, Yilu Xu, Genliang Xiong, and Cheng Zeng. "Isokinetic Rehabilitation Trajectory Planning of an Upper Extremity Exoskeleton Rehabilitation Robot Based on a Multistrategy Improved Whale Optimization Algorithm." Symmetry 15, no. 1 (January 13, 2023): 232. http://dx.doi.org/10.3390/sym15010232.

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Upper extremity exoskeleton rehabilitation robots have become a significant piece of rehabilitation equipment, and planning their motion trajectories is essential in patient rehabilitation. In this paper, a multistrategy improved whale optimization algorithm (MWOA) is proposed for trajectory planning of upper extremity exoskeleton rehabilitation robots with emphasis on isokinetic rehabilitation. First, a piecewise polynomial was used to construct a rough trajectory. To make the trajectory conform to human-like movement, a whale optimization algorithm (WOA) was employed to generate a bounded jerk trajectory with the minimum running time as the objective. The search performance of the WOA under complex constraints, including the search capability of trajectory planning symmetry, was improved by the following strategies: a dual-population search, including a new communication mechanism to prevent falling into the local optimum; a mutation centroid opposition-based learning, to improve the diversity of the population; and an adaptive inertia weight, to balance exploration and exploitation. Simulation analysis showed that the MWOA generated a trajectory with a shorter run-time and better symmetry and robustness than the WOA. Finally, a pilot rehabilitation session on a healthy volunteer using an upper extremity exoskeleton rehabilitation robot was completed safely and smoothly along the trajectory planned by the MWOA. The proposed algorithm thus provides a feasible scheme for isokinetic rehabilitation trajectory planning of upper extremity exoskeleton rehabilitation robots.

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32

Karahan, Oguzhan, Hasan Karci, and Ali Tangel. "Time-optimal trajectory generation in joint space for 6R industrial serial robots using Cuckoo search algorithm." Global Journal of Computer Sciences: Theory and Research 12, no. 1 (April 23, 2022): 42–54. http://dx.doi.org/10.18844/gjcs.v12i1.7447.

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The trajectory planning problem in industrial robotic applications has recently attracted the great attention of many researchers. In this paper, an optimal trajectory planning approach is proposed based on optimal time by utilizing the interpolation spline method. The method including a combination of cubic spline and 7th order polynomial is used for generating the trajectory in joint space for robot manipulators. Cuckoo Search (CS) optimization algorithm is chosen to optimize the joint trajectories based on objective, including minimizing total traveling time along the whole trajectory. The spline method has been applied to the PUMA robot for optimizing the joint trajectories with the CS algorithm based on the objective. With the trajectory planning method, the joint velocities, accelerations, and jerks along the whole trajectory optimized by CS meet the requirements of the kinematic constraints in the case of the objective. Simulation results validated that the used trajectory planning method based on the proposed algorithm is very effective in comparison with the same methods based on the algorithms proposed by earlier authors. Keywords: CS; industrial robots; interpolation; spline methods; trajectory planning.

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33

Cui, Hao, Thomas Keller, and Roni Khardon. "Stochastic Planning with Lifted Symbolic Trajectory Optimization." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 119–27. http://dx.doi.org/10.1609/icaps.v29i1.3467.

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This paper investigates online stochastic planning for problems with large factored state and action spaces. One promising approach in recent work estimates the quality of applicable actions in the current state through aggregate simulation from the states they reach. This leads to significant speedup, compared to search over concrete states and actions, and suffices to guide decision making in cases where the performance of a random policy is informative of the quality of a state. The paper makes two significant improvements to this approach. The first, taking inspiration from lifted belief propagation, exploits the structure of the problem to derive a more compact computation graph for aggregate simulation. The second improvement replaces the random policy embedded in the computation graph with symbolic variables that are optimized simultaneously with the search for high quality actions. This expands the scope of the approach to problems that require deep search and where information is lost quickly with random steps. An empirical evaluation shows that these ideas significantly improve performance, leading to state of the art performance on hard planning problems.

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34

Farid, Ghulam, Silvio Cocuzza, Talha Younas, Asghar Abbas Razzaqi, Waqas Ahmad Wattoo, Ferdinando Cannella, and Hongwei Mo. "Modified A-Star (A*) Approach to Plan the Motion of a Quadrotor UAV in Three-Dimensional Obstacle-Cluttered Environment." Applied Sciences 12, no. 12 (June 7, 2022): 5791. http://dx.doi.org/10.3390/app12125791.

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Motion-planning algorithms play a vital role in attaining various levels of autonomy for any ground or flying agent. Three-dimensional (3D) motion-planning is interesting, but rather complex, especially for flying agents such as autonomous unmanned aerial vehicles (UAVs), due to the increased dimensionality of space and consideration of dynamical constraints for a feasible trajectory. Usually, in 3D path search problems, it is hard to avoid extra expanded nodes due to increased dimensionality with various available search options. Therefore, this paper discusses and implements a modified heuristic-based A* formalism that uses a truncation mechanism in order to eradicate the mentioned problem. In this formalism, the complete motion planning is divided into shortest path search problem and smooth trajectory generation. The shortest path search problem is subdivided into an initial naïve guess of the path and the truncation of the extra nodes. To find a naïve shortest path, a conventional two-dimensional (2D) A* algorithm is augmented for 3D space with six-sided search. This approach led to the inclusion of extra expanded nodes and, therefore, it is not the shortest one. Hence, a truncation algorithm is developed to further process this path in order to truncate the extra expanded nodes and then to shorten the path length. This new approach significantly reduces the path length and renders only those nodes that are obstacle-free. The latter is ensured using a collision detection algorithm during the truncation process. Subsequently, the nodes of this shortened path are used to generate a dynamically feasible and optimal trajectory for the quadrotor UAV. Optimal trajectory generation requires that the plant dynamics must be differentially flat. Therefore, the corresponding proof is presented to ensure generation of the optimal trajectory. This optimal trajectory minimizes the control effort and ensures longer endurance. Moreover, quadrotor model and controllers are derived as preliminaries, which are subsequently used to track the desired trajectory generated by the trajectory planner. Finally, results of numerical simulations which ultimately validate the theoretical developments are presented.

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35

Strawser, Daniel, and Brian Williams. "Motion Planning Under Uncertainty with Complex Agents and Environments via Hybrid Search." Journal of Artificial Intelligence Research 75 (September 19, 2022): 1–81. http://dx.doi.org/10.1613/jair.1.13361.

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As autonomous systems and robots are applied to more real world situations, they must reason about uncertainty when planning actions. Mission success oftentimes cannot be guaranteed and the planner must reason about the probability of failure. Unfortunately, computing a trajectory that satisfies mission goals while constraining the probability of failure is difficult because of the need to reason about complex, multidimensional probability distributions. Recent methods have seen success using chance-constrained, model-based planning. However, the majority of these methods can only handle simple environment and agent models. We argue that there are two main drawbacks of current approaches to goal-directed motion planning under uncertainty. First, current methods suffer from an inability to deal with expressive environment models such as 3D non-convex obstacles. Second, most planners rely on considerable simplifications when computing trajectory risk including approximating the agent’s dynamics, geometry, and uncertainty. In this article, we apply hybrid search to the risk-bound, goal-directed planning problem. The hybrid search consists of a region planner and a trajectory planner. The region planner makes discrete choices by reasoning about geometric regions that the autonomous agent should visit in order to accomplish its mission. In formulating the region planner, we propose landmark regions that help produce obstacle-free paths. The region planner passes paths through the environment to a trajectory planner; the task of the trajectory planner is to optimize trajectories that respect the agent’s dynamics and the user’s desired risk of mission failure. We discuss three approaches to modeling trajectory risk: a CDF-based approach, a sampling-based collocation method, and an algorithm named Shooting Method Monte Carlo. These models allow computation of trajectory risk with more complex environments, agent dynamics, geometries, and models of uncertainty than past approaches. A variety of 2D and 3D test cases are presented including a linear case, a Dubins car model, and an underwater autonomous vehicle. The method is shown to outperform other methods in terms of speed and utility of the solution. Additionally, the models of trajectory risk are shown to better approximate risk in simulation.

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36

Cheng, Kaixin, Di Wu, Tao Hu, Jinjin Wei, and Zhifu Tian. "Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM." International Journal of Aerospace Engineering 2022 (December 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/8561245.

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Aiming to address the problem of unknown dynamic target trajectory prediction and search path optimization in unmanned aerial vehicle (UAV) swarm path planning, this paper proposes a target search algorithm based on a modified target probability map (TPM). First, using the TPM, the proposed algorithm generates a high-probability distribution region of a target with directionality to fit the target trajectory and realizes the trajectory prediction of an unknown dynamic target. Then, the distributed ant colony (ACO) algorithm and the artificial potential field (APF) algorithm are combined to generate and optimize the UAV swarm search result and return path with the goal of maximizing task execution efficiency. Finally, the Monte Carlo simulation method is used to analyze the effectiveness of the proposed algorithm, and the results are evaluated from five aspects, including the number of targets captured. The simulation results show that under the condition of an unknown dynamic target trajectory, the average target captured rate and average unknown region search rate of the MTPM method were higher than that of the traditional TPM method, and the performance was improved by 14.6% and 10.7%, respectively.

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37

Gu, Shangding, Chunhui Zhou, Yuanqiao Wen, Changshi Xiao, and Alois Knoll. "Motion Planning for an Unmanned Surface Vehicle with Wind and Current Effects." Journal of Marine Science and Engineering 10, no. 3 (March 14, 2022): 420. http://dx.doi.org/10.3390/jmse10030420.

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Aiming at the problem that unmanned surface vehicle (USV) motion planning is disturbed by effects of wind and current, a USV motion planning method based on regularization-trajectory cells is proposed. First, a USV motion mathematical model is established while considering the influence of wind and current, and the motion trajectory is analyzed. Second, a regularization-trajectory cell library under the influence of wind and current is constructed, and the influence of wind and current on the weight of the search cost is analyzed. Finally, derived from the regularization-trajectory cell and the search algorithm, a motion planning method for a USV that considers wind and current effects is provided. The experimental results indicate that the motion planning is closer to the actual trajectory of a USV in complex environments and that our method is highly practicable.

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Ata, A. A. "Collision-free trajectory planning for manipulators using generalized pattern search." International Journal of Simulation Modelling 5, no. 4 (December 15, 2006): 145–54. http://dx.doi.org/10.2507/ijsimm05(4)2.074.

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Sogo, Hiroyuki, and Yuji Takeda. "Saccade trajectory curvature in visual search using natural scene images." Japanese journal of psychology 78, no. 5 (2007): 512–18. http://dx.doi.org/10.4992/jjpsy.78.512.

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Narvaez-Teran, Valentina, Gabriela Ochoa, and Eduardo Rodriguez-Tello. "Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem." IEEE Access 9 (2021): 151266–77. http://dx.doi.org/10.1109/access.2021.3126015.

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Léchevin, Nicolas, Franklin Wong, and Camille Alain Rabbath. "Trajectory Shaping of Projectile Through Cross-Entropy-Minimization-Based Search." Journal of Guidance, Control, and Dynamics 32, no. 1 (January 2009): 300–304. http://dx.doi.org/10.2514/1.39231.

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Rippel, Eran, Aharon Bar-Gill, and Nahum Shimkin. "Fast Graph-Search Algorithms for General-Aviation Flight Trajectory Generation." Journal of Guidance, Control, and Dynamics 28, no. 4 (July 2005): 801–11. http://dx.doi.org/10.2514/1.7370.

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Chen, Youdong, Liang Yan, Hongxing Wei, and Tianmiao Wang. "Optimal trajectory planning for industrial robots using harmony search algorithm." Industrial Robot: An International Journal 40, no. 5 (August 16, 2013): 502–12. http://dx.doi.org/10.1108/ir-12-2012-444.

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Alejandro, MURRIETA-MENDOZA, GAGNÉ Jocelyn, and BOTEZ Ruxandra Mihaela. "New Search Space Reduction Algorithm for Vertical Reference Trajectory Optimization." INCAS BULLETIN 8, no. 2 (June 10, 2016): 77–95. http://dx.doi.org/10.13111/2066-8201.2016.8.2.7.

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Meng, Jingjing, Junsong Yuan, Jiong Yang, Gang Wang, and Yap-Peng Tan. "Object Instance Search in Videos via Spatio-Temporal Trajectory Discovery." IEEE Transactions on Multimedia 18, no. 1 (January 2016): 116–27. http://dx.doi.org/10.1109/tmm.2015.2500734.

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Lindawati, H. C. Lau, and D. Lo. "Clustering of search trajectory and its application to parameter tuning." Journal of the Operational Research Society 64, no. 12 (December 2013): 1742–52. http://dx.doi.org/10.1057/jors.2012.167.

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Vasile, M. "A memetic multi-agent collaborative search for space trajectory optimisation." International Journal of Bio-Inspired Computation 1, no. 3 (2009): 186. http://dx.doi.org/10.1504/ijbic.2009.023814.

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Ruby, Margaux, and Ruxandra Mihaela Botez. "Trajectory Optimization for vertical navigation using the Harmony Search algorithm." IFAC-PapersOnLine 49, no. 17 (2016): 11–16. http://dx.doi.org/10.1016/j.ifacol.2016.09.003.

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Li, Mingkai. "Robot Trajectory Planning Based on the Energy Management Strategy." Mathematical Problems in Engineering 2022 (September 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/9597075.

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Abstract:

With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.

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Ferreira, Rafael Pereira, and Américo Scotti. "The Concept of a Novel Path Planning Strategy for Wire + Arc Additive Manufacturing of Bulky Parts: Pixel." Metals 11, no. 3 (March 17, 2021): 498. http://dx.doi.org/10.3390/met11030498.

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Abstract:

An innovative trajectory strategy was proposed and accessed for wire arc additive manufacturing (WAAM), applicable to different and more complex geometries, rather than being a single solution. This strategy, named Pixel, can be defined as a complex multitask procedure to carry out optimized path planning, whose operation is made through computational algorithms (heuristics), with accessible computational resources and tolerable computational time. The model layers are fractioned in squared grids, and a set of dots is systematically generated and distributed inside the sliced outlines, resembling pixels on a screen, over which the trajectory is planned. The Pixel strategy was based on creating trajectories from the technique travelling salesman problem (TSP). Unlike existing algorithms, the Pixel strategy uses an adapted greedy randomized adaptive search procedure (GRASP) metaheuristic, aided by four concurrent trajectory planning heuristics, developed by the authors. Interactions provide successive trajectories from randomized initial solutions (global search) and subsequent iterative improvements (local search). After all recurrent loops, a trajectory is defined and written in machine code. Computational evaluation was implemented to demonstrate the effect of each of the heuristics on the final trajectory. An experimental evaluation was eventually carried out using two different not easily printable shapes to demonstrate the practical feasibility of the proposed strategy.

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