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Journal articles on the topic 'Decentralized Navigation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Zhang, Boyang, and Henri P. Gavin. "Decentralized Control of Multiagent Navigation Systems." IEEE/CAA Journal of Automatica Sinica 9, no. 5 (May 2022): 922–25. http://dx.doi.org/10.1109/jas.2022.105569.

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2

Zhang, Boyang, and Henri P. Gavin. "Decentralized Control of Multiagent Navigation Systems." IEEE/CAA Journal of Automatica Sinica 9, no. 5 (May 2022): 922–25. http://dx.doi.org/10.1109/jas.2022.105569.

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3

Liu, Guohua, Juan Guan, Haiying Liu, and Chenlin Wang. "Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/5819409.

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Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.

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4

Kostaki, Maria, Argiro Vatakis, and Stavroula Samartzi. "Assisted spatial navigation: new directions." Homo Virtualis 2, no. 1 (March 27, 2019): 21. http://dx.doi.org/10.12681/homvir.20190.

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Blockchain technology brings new possibilities in assisted spatial navigation. Decentralized map building enables collaboration between users around the world, while providing researchers with a common reference map for extending the capabilities of navigational systems towards more intuitive and accurate landmark navigation assistance. Research on landmark navigation has been mainly focused on the visual characteristics of landmarks. Human behavior, however, has systematically been shown to be enhanced in the presence of multisensory unified events. We propose, therefore, the enhancement of spatial assisted navigation by utilizing landmarks that are multisensory and semantically congruent. Further, our research will provide insights in terms of the auditory parameters that could be combined with a given visual landmark, so as to facilitate landmark retrieval algorithms and user satisfaction during assisted spatial navigation.

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Qin, Tong, Malcolm Macdonald, and Dong Qiao. "Fully Decentralized Cooperative Navigation for Spacecraft Constellations." IEEE Transactions on Aerospace and Electronic Systems 57, no. 4 (August 2021): 2383–94. http://dx.doi.org/10.1109/taes.2021.3060734.

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6

Mavrogiannis, Christoforos, and Ross A. Knepper. "Hamiltonian coordination primitives for decentralized multiagent navigation." International Journal of Robotics Research 40, no. 10-11 (August 13, 2021): 1234–54. http://dx.doi.org/10.1177/02783649211037731.

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We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.

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Hoinville, Thierry, and Rüdiger Wehner. "Optimal multiguidance integration in insect navigation." Proceedings of the National Academy of Sciences 115, no. 11 (February 26, 2018): 2824–29. http://dx.doi.org/10.1073/pnas.1721668115.

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In the last decades, desert ants have become model organisms for the study of insect navigation. In finding their way, they use two major navigational routines: path integration using a celestial compass and landmark guidance based on sets of panoramic views of the terrestrial environment. It has been claimed that this information would enable the insect to acquire and use a centralized cognitive map of its foraging terrain. Here, we present a decentralized architecture, in which the concurrently operating path integration and landmark guidance routines contribute optimally to the directions to be steered, with “optimal” meaning maximizing the certainty (reliability) of the combined information. At any one time during its journey, the animal computes a path integration (global) vector and landmark guidance (local) vector, in which the length of each vector is proportional to the certainty of the individual estimates. Hence, these vectors represent the limited knowledge that the navigator has at any one place about the direction of the goal. The sum of the global and local vectors indicates the navigator’s optimal directional estimate. Wherever applied, this decentralized model architecture is sufficient to simulate the results of quite a number of diverse cue-conflict experiments, which have recently been performed in various behavioral contexts by different authors in both desert ants and honeybees. They include even those experiments that have deliberately been designed by former authors to strengthen the evidence for a metric cognitive map in bees.

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Seguin, Caio, Martijn P. van den Heuvel, and Andrew Zalesky. "Navigation of brain networks." Proceedings of the National Academy of Sciences 115, no. 24 (May 30, 2018): 6297–302. http://dx.doi.org/10.1073/pnas.1801351115.

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Understanding the mechanisms of neural communication in large-scale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse, and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45–60% reductions in navigation performance. We found that the human connectome cannot be progressively randomized or clusterized to result in topologies with substantially improved navigation performance (>5%), suggesting a topological balance between regularity and randomness that is conducive to efficient navigation. Navigation was also found to (i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks, and (ii) explain significant variation in functional connectivity. Unlike commonly studied communication strategies in connectomics, navigation does not mandate assumptions about global knowledge of network topology. We conclude that the topology and geometry of brain networks are conducive to efficient decentralized communication.

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9

Jiménez, Andrés C., Vicente García-Díaz, and Sandro Bolaños. "Decentralized navigation model for multiagent cooperative robotic systems." Journal of Ambient Intelligence and Smart Environments 12, no. 6 (November 26, 2020): 547–48. http://dx.doi.org/10.3233/ais-200583.

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On November 20, 2018 at 11 am, Andrés Camilo Jiménez Alvarez defended his Ph.D. thesis entitled Decentralized navigation model for multiagent cooperative robotic systems at the Distrital University Francisco José de Caldas. Andrés Camilo Jiménez Alvarez presented his dissertation in a public open event held in the “Wise Caldas Auditory”, and was able to expose and defend all his research, it was approved by the committee. The thesis was supervised by his advisors, Vicente García-Díaz and Sandro Javier Bolaños, together with the thesis committee, Rubén Gonzáles Crespo, Oscar Fernando Avilés and Roberto Ferro Escobar. All the cited people were present at the event.

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10

GAO, Wenyun, Xi CHEN, Dexiu HU, and Haisheng XU. "Cooperative/Parallel Kalman Filtering for Decentralized Network Navigation." IEICE Transactions on Communications E99.B, no. 9 (2016): 2087–98. http://dx.doi.org/10.1587/transcom.2016ebp3006.

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11

Kerr, Thomas. "Decentralized Filtering and Redundancy Management for Multisensor Navigation." IEEE Transactions on Aerospace and Electronic Systems AES-23, no. 1 (January 1987): 83–119. http://dx.doi.org/10.1109/taes.1987.313339.

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12

Lin, Fu-Tian, Chu-Sing Yang, Tien-Wen Sung, and Bi-jar Lin. "Decentralized Mobile Sensor Navigation for Hole Healing Policy in Wireless Hybrid Sensor Networks." International Journal of Future Generation Communication and Networking 6, no. 6 (December 31, 2013): 143–50. http://dx.doi.org/10.14257/ijfgcn.2013.6.6.15.

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13

Mercker, Travis, Maruthi Akella, and Jorge Alvarez. "Robot Navigation in a Decentralized Landmark-Free Sensor Network." Journal of Intelligent & Robotic Systems 60, no. 3-4 (May 6, 2010): 553–76. http://dx.doi.org/10.1007/s10846-010-9431-x.

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14

Krishnan, Shravan, Govind Aadithya Rajagopalan, Sivanathan Kandhasamy, and Madhavan Shanmugavel. "Continuous-Time Trajectory Optimization for Decentralized Multi-Robot Navigation." IFAC-PapersOnLine 53, no. 1 (2020): 494–99. http://dx.doi.org/10.1016/j.ifacol.2020.06.083.

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15

Zhang, Bolei, Lifa Wu, and Ilsun You. "Decentralized Policy Coordination in Mobile Sensing with Consensual Communication." Sensors 22, no. 24 (December 7, 2022): 9584. http://dx.doi.org/10.3390/s22249584.

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In a typical mobile-sensing scenario, multiple autonomous vehicles cooperatively navigate to maximize the spatial–temporal coverage of the environment. However, as each vehicle can only make decentralized navigation decisions based on limited local observations, it is still a critical challenge to coordinate the vehicles for cooperation in an open, dynamic environment. In this paper, we propose a novel framework that incorporates consensual communication in multi-agent reinforcement learning for cooperative mobile sensing. At each step, the vehicles first learn to communicate with each other, and then, based on the received messages from others, navigate. Through communication, the decentralized vehicles can share information to break through the dilemma of local observation. Moreover, we utilize mutual information as a regularizer to promote consensus among the vehicles. The mutual information can enforce positive correlation between the navigation policy and the communication message, and therefore implicitly coordinate the decentralized policies. The convergence of this regularized algorithm can be proved theoretically under certain mild assumptions. In the experiments, we show that our algorithm is scalable and can converge very fast during training phase. It also outperforms other baselines significantly in the execution phase. The results validate that consensual communication plays very important role in coordinating the behaviors of decentralized vehicles.

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16

Elfakharany, Ahmed, and Zool Hilmi Ismail. "End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System." Applied Sciences 11, no. 7 (March 24, 2021): 2895. http://dx.doi.org/10.3390/app11072895.

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In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot’s steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index (TAI), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots.

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17

Hegazy, Doaa, Ibrahim El Henawy, and Shimaa Ouf. "TOWARDS BLOCKCHAIN-BASED INTELLIGENT LOGISTICS INDUSTRY FOR NAVIGATION SYSTEMS." Journal of Southwest Jiaotong University 56, no. 5 (October 30, 2021): 619–30. http://dx.doi.org/10.35741/issn.0258-2724.56.5.56.

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This paper aims to compare the Global Positioning System and and the Geo spatial Blockchain. Geoblockchain for decentralizes system and its potential applications in logistics research are presented. The goal of the Proof of Location protocol is to provide the framework and infrastructure to develop a decentralized, privacy preserving, highly accurate,censorship resistant alternative to the Global Positioning System (GPS). In this paper, a comparison is presented between geographic points measured from regions using GPS and projected onto the map to find the longitude and latitude coordinates of some Egyptian seaports and the points measured by the FOAM (GEO spatial Blockchain) application and projected onto the map to find the longitude and latitude coordinates The offer is for the same Egyptian sea port. The comparison of the difference between the Global Positioning System (GPS) and the Geo spatial Blockchain was done through statistical analysis, which represents an essential tool for analyzing and interpreting data through descriptive statistics and statistical testing described in the following subsections using the SPSS program. The result of the comparison that the longitude values of both GPS and Blockchain are much closed. Also the latitude with both GPS and Blockchain is much closed. This means that there is no significance difference between the Latitude and measures with GPS and Blockchain.

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18

Berman, S., Y. Edan, and Mo Jamshidi. "Navigation of decentralized autonomous automatic guided vehicles in material handling." IEEE Transactions on Robotics and Automation 19, no. 4 (August 2003): 743–49. http://dx.doi.org/10.1109/tra.2003.814513.

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19

Segovia, P., L. Rajaoarisoa, F. Nejjari, J. Blesa, V. Puig, and E. Duviella. "Decentralized Fault-Tolerant Control of Inland Navigation Networks: a Challenge." Journal of Physics: Conference Series 783 (January 2017): 012018. http://dx.doi.org/10.1088/1742-6596/783/1/012018.

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20

Yoshimoto, Masahiro, Takahiro Endo, Ryuma Maeda, and Fumitoshi Matsuno. "Decentralized navigation method for a robotic swarm with nonhomogeneous abilities." Autonomous Robots 42, no. 8 (June 11, 2018): 1583–99. http://dx.doi.org/10.1007/s10514-018-9774-x.

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21

Wang, Xiaogang, Wutao Qin, Yuliang Bai, and Naigang Cui. "A novel decentralized relative navigation algorithm for spacecraft formation flying." Aerospace Science and Technology 48 (January 2016): 28–36. http://dx.doi.org/10.1016/j.ast.2015.10.014.

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22

Gao, Chao, Guorong Zhao, Jianhua Lu, and Shuang Pan. "Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 11 (June 8, 2017): 2160–77. http://dx.doi.org/10.1177/0954410017712277.

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In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.

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23

Martínez-García, Edgar A., Rafael Torres-Córdoba, Victor M. Carrillo-Saucedo, and Elifalet López-González. "Neural control and coordination of decentralized transportation robots." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 5 (March 9, 2018): 519–40. http://dx.doi.org/10.1177/0959651818756777.

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This work presents the modeling, control architecture and simulation of a decentralized multi-robot system for transporting material in a warehouse. Each robot has a task scheduler comprising two different neural networks for task allocation and fault tolerance. The path planner consists of a first-order dynamical state equation to control the robot’s four-wheel asynchronous driving and steering, as well as a partial differential equation to coordinate speeds and arrival times. The task allocation and motion coordination combine the robot’s kinematic control law with a one-layer artificial neural network that classifies five-dimensional symbolic logical equations that define the state transitions between asynchronous events. These events include carry and fetch, material supply, robots stop, obstacle avoidance and battery state. Another multilayer artificial neural network reads the same state inputs for fault detection and recovery. The two neural systems feed forward a navigation planner, which uses a partial differential equation to coordinate the robot’s speed and its relaxation time with respect to the robot in front of it. The energy cost is measured by a Lagrangian function. The proposed planning control scheme was computationally validated through parallel computing simulations. The system is shown to be consistent, reliable and feasible, and it allows for fast navigational tasks.

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24

Dai, Mengyuan, Hua Mu, Meiping Wu, and Zhiwen Xian. "Decentralized State Estimation Algorithm of Centralized Equivalent Precision for Formation Flying Spacecrafts Based on Junction Tree." International Journal of Aerospace Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/714302.

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As centralized state estimation algorithms for formation flying spacecraft would suffer from high computational burdens when the scale of the formation increases, it is necessary to develop decentralized algorithms. To the state of the art, most decentralized algorithms for formation flying are derived from centralized EKF by simplification and decoupling, rendering suboptimal estimations. In this paper, typical decentralized state estimation algorithms are reviewed, and a new scheme for decentralized algorithms is proposed. In the new solution, the system is modeled as a dynamic Bayesian network (DBN). A probabilistic graphical method named junction tree (JT) is used to analyze the hidden distributed structure of the DBNs. Inference on JT is a decentralized form of centralized Bayesian estimation (BE), which is a modularized three-step procedure of receiving messages, collecting evidences, and generating messages. As KF is a special case of BE, the new solution based on JT is equivalent in precision to centralized KF in theory. A cooperative navigation example of a three-satellite formation is used to test the decentralized algorithms. Simulation results indicate that JT has the best precision among all current decentralized algorithms.

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Chen, Zhongyuan, Wanchun Chen, Xiaoming Liu, and Chuang Song. "Fault-Tolerant Optical Flow Sensor/SINS Integrated Navigation Scheme for MAV in a GPS-Denied Environment." Journal of Sensors 2018 (September 20, 2018): 1–17. http://dx.doi.org/10.1155/2018/9678505.

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An integrated navigation scheme based on multiple optical flow sensors and a strapdown inertial navigation system (SINS) are presented, instead of the global position system (GPS) aided. Multiple optical flow sensors are mounted on a micro air vehicle (MAV) at different positions with different viewing directions for detecting optical flow around the MAV. A fault-tolerant decentralized extended Kalman filter (EKF) is performed for estimating navigation errors by fusing the inertial and optical flow measurements, which can prevent the estimation divergence caused by the failure of the optical flow sensor. Then, the estimation of navigation error is inputted into the SINS settlement process for correcting the SINS measurements. The results verify that the navigation errors of SINS can be effectively reduced (even more than 9/10). Moreover, although the sensor is in a state of failure for 400 seconds, the fault-tolerant integrated navigation system can still work properly without divergence.

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26

Gao, Chao, Guorong Zhao, Jianhua Lu, and Shuang Pan. "Decentralized navigational state estimation for networked navigation systems with finite channel capacity and randomly switching topologies." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 2 (December 20, 2016): 201–14. http://dx.doi.org/10.1177/0954410016683730.

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27

Huang, Hailong, Andrey V. Savkin, and Chao Huang. "Decentralized Autonomous Navigation of a UAV Network for Road Traffic Monitoring." IEEE Transactions on Aerospace and Electronic Systems 57, no. 4 (August 2021): 2558–64. http://dx.doi.org/10.1109/taes.2021.3053115.

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28

Cruse, Holk, and Rüdiger Wehner. "No Need for a Cognitive Map: Decentralized Memory for Insect Navigation." PLoS Computational Biology 7, no. 3 (March 17, 2011): e1002009. http://dx.doi.org/10.1371/journal.pcbi.1002009.

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29

Savkin, Andrey V., and Hamid Teimoori. "Decentralized Navigation of Groups of Wheeled Mobile Robots With Limited Communication." IEEE Transactions on Robotics 26, no. 6 (December 2010): 1099–104. http://dx.doi.org/10.1109/tro.2010.2081430.

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Maeda, Ryuma, Takahiro Endo, and Fumitoshi Matsuno. "Decentralized Navigation for Heterogeneous Swarm Robots With Limited Field of View." IEEE Robotics and Automation Letters 2, no. 2 (April 2017): 904–11. http://dx.doi.org/10.1109/lra.2017.2654549.

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31

Dimarogonas, Dimos V., and Kostas J. Kyriakopoulos. "Decentralized Navigation Functions for Multiple Robotic Agents with Limited Sensing Capabilities." Journal of Intelligent and Robotic Systems 48, no. 3 (January 23, 2007): 411–33. http://dx.doi.org/10.1007/s10846-006-9113-x.

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32

Fonti, A., A. Freddi, S. Longhi, and A. Monteriù. "Cooperative and decentralized navigation of autonomous underwater gliders using predictive control." IFAC Proceedings Volumes 44, no. 1 (January 2011): 12813–18. http://dx.doi.org/10.3182/20110828-6-it-1002.02980.

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33

Roussos, Giannis, and Kostas J. Kyriakopoulos. "Decentralized Navigation and Conflict Avoidance for Aircraft in 3-D Space." IEEE Transactions on Control Systems Technology 20, no. 6 (November 2012): 1622–29. http://dx.doi.org/10.1109/tcst.2011.2167974.

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Yang, Yue, Xiaoxiong Liu, Weiguo Zhang, Xuhang Liu, and Yicong Guo. "A Nonlinear Double Model for Multisensor-Integrated Navigation Using the Federated EKF Algorithm for Small UAVs." Sensors 20, no. 10 (May 24, 2020): 2974. http://dx.doi.org/10.3390/s20102974.

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Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy.

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35

Vizins, Kim, Dimos V. Dimarogonas, and Bo Wahlberg. "Modeling and Control of Dual Arm Robotic Manipulators using Decentralized Navigation Functions." IFAC Proceedings Volumes 45, no. 22 (2012): 241–46. http://dx.doi.org/10.3182/20120905-3-hr-2030.00082.

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Prodan, Ionela, Sorin Olaru, Cristina Stoica, and Silviu-Iulian Niculescu. "Predictive control for trajectory tracking and decentralized navigation of multi-agent formations." International Journal of Applied Mathematics and Computer Science 23, no. 1 (March 1, 2013): 91–102. http://dx.doi.org/10.2478/amcs-2013-0008.

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This paper addresses a predictive control strategy for a particular class of multi-agent formations with a time-varying topology. The goal is to guarantee tracking capabilities with respect to a reference trajectory which is pre-specified for an agent designed as the leader. Then, the remaining agents, designed as followers, track the position and orientation of the leader. In real-time, a predictive control strategy enhanced with the potential field methodology is used in order to derive a feedback control action based only on local information within the group of agents. The main concern is that the interconnections between the agents are time-varying, affecting the neighborhood around each agent. The proposed method exhibits effective performance validated through some illustrative examples.

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McCabe, James S., and Kyle J. DeMars. "Vision‐based, terrain‐aided navigation with decentralized fusion and finite set statistics." Navigation 66, no. 3 (August 16, 2019): 537–57. http://dx.doi.org/10.1002/navi.320.

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38

Mendes, Pedro, Pedro Batista, Paulo Oliveira, and Carlos Silvestre. "Cooperative decentralized navigation algorithms based on bearing measurements for arbitrary measurement topologies." Ocean Engineering 270 (February 2023): 113564. http://dx.doi.org/10.1016/j.oceaneng.2022.113564.

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Endo, Takahiro, Ryuma Maeda, and Fumitoshi Matsuno. "Stability Analysis of Swarm Heterogeneous Robots with Limited Field of View." Informatics and Automation 19, no. 5 (October 13, 2020): 942–66. http://dx.doi.org/10.15622/ia.2020.19.5.2.

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This paper presents a stability analysis of swarm robots, a group of multiple robots. In particular, we focus on robot swarms with heterogeneous abilities, in which each robot has a different sensing range and physical limitations, including maximum velocity and acceleration. In addition, each robot has a unique sensing region with a limited angle field of view. We previously proposed a decentralized navigation method for such heterogeneous swarm robots consisting of one leader and multiple followers. With the decentralized navigation method, a single leader can navigate for followers while maintaining connectivity and satisfying the physical limitations unique to each robot; i.e., each follower has a target robot and follows it without violating its physical limitations. In this paper, we focus on a stability analysis of such swarm robots. When the leader moves at a constant velocity, we mathematically prove that the shape and orientations of all robots eventually converge to the equilibrium state. For this, we must first prove that the equilibrium state exists. Then, we show the convergence of the state to its equilibrium. Finally, we carry out experiments and numerical simulations to confirm the stability analysis, i.e., the convergence of the swarm robots to the equilibrium states.

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Ahmed, Ishriak, Gary G. Yen, and Imraan A. Faruque. "Decentralized Multi-agent Navigation via Knee-based Multi-objective Optimization of Potential Functions." IFAC-PapersOnLine 55, no. 15 (2022): 51–56. http://dx.doi.org/10.1016/j.ifacol.2022.07.607.

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Webster, Sarah E., Jeffrey M. Walls, Louis L. Whitcomb, and Ryan M. Eustice. "Decentralized Extended Information Filter for Single-Beacon Cooperative Acoustic Navigation: Theory and Experiments." IEEE Transactions on Robotics 29, no. 4 (August 2013): 957–74. http://dx.doi.org/10.1109/tro.2013.2252857.

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Manrique, Pedro, Mason Klein, Yao Sheng Li, Chen Xu, Pak Ming Hui, and Neil Johnson. "Decentralized Competition Produces Nonlinear Dynamics Akin to Klinotaxis." Complexity 2018 (July 22, 2018): 1–8. http://dx.doi.org/10.1155/2018/9803239.

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One of the biggest challenges in unravelling the complexity of living systems, is to fully understand the neural logic that translates sensory input into the highly nonlinear motor outputs that are observed when simple organisms crawl. Recent work has shown that organisms such as larvae that exhibit klinotaxis (i.e., orientation through lateral movements of portions of the body) can perform normal exploratory practices even in the absence of a brain. Abdominal and thoracic networks control the alternation between crawls and turns. This motivates the search for decentralized models of movement that can produce nonlinear outputs that resemble the experiments. Here, we present such a complex system model, in the form of a population of decentralized decision-making components (agents) whose aggregate activity resembles that observed in klinotaxis organisms. Despite the simplicity of each component, the complexity created by their collective feedback of information and actions akin to proportional navigation, drives the model organism towards a specific target. Our model organism’s nonlinear behaviors are consistent with empirically observed reorientation rate measures for Drosophila larvae as well as nematode C. elegans.

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Wu, Baolin, and Xibin Cao. "Decentralized control for spacecraft formation in elliptic orbits." Aircraft Engineering and Aerospace Technology 90, no. 1 (January 2, 2018): 166–74. http://dx.doi.org/10.1108/aeat-12-2015-0250.

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Purpose This paper aims to address the problem of formation control for spacecraft formation in elliptic orbits by using local relative measurements. Design/methodology/approach A decentralized formation control law is proposed to solve the aforementioned problem. The control law for each spacecraft uses only its relative state with respect to the neighboring spacecraft it can sense. These relative states can be acquired by local relative measurements. The formation control problem is converted to n stabilization problems of a single spacecraft by using algebraic graph theories. The resulting relative motion model is described by a linear time-varying system with uncertain parameters. An optimal guaranteed cost control scheme is subsequently used to obtain the desired control performance. Findings Numerical simulations show the effectiveness of the proposed formation control law. Practical implications The proposed control law can be considered as an alternative to global positioning system-based relative navigation and control system for formation flying missions. Originality/value The proposed decentralized formation control architecture needs only local relative measurements. Fuel consumption is considered by using an optimal guaranteed cost control scheme.

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Fan, Tingxiang, Pinxin Long, Wenxi Liu, and Jia Pan. "Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios." International Journal of Robotics Research 39, no. 7 (May 31, 2020): 856–92. http://dx.doi.org/10.1177/0278364920916531.

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Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other robots’ states and intentions. Prior distributed multi-robot collision-avoidance systems often require frequent inter-robot communication or agent-level features to plan a local collision-free action, which is not robust and computationally prohibitive. In addition, the performance of these methods is not comparable with their centralized counterparts in practice. In this article, we present a decentralized sensor-level collision-avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent’s steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy-gradient-based reinforcement-learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy’s robustness and effectiveness. We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller’s robustness against the simulation-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution for safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. More importantly, the policy has been successfully deployed on different types of physical robot platforms without tedious parameter tuning. Videos are available at https://sites.google.com/view/hybridmrca .

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Manrique, Pedro D., Mason Klein, Yao Sheng Li, Chen Xu, Pak Ming Hui, and Neil F. Johnson. "Getting closer to the goal by being less capable." Science Advances 5, no. 2 (February 2019): eaau5902. http://dx.doi.org/10.1126/sciadv.aau5902.

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Understanding how systems with many semi-autonomous parts reach a desired target is a key question in biology (e.g., Drosophila larvae seeking food), engineering (e.g., driverless navigation), medicine (e.g., reliable movement for brain-damaged individuals), and socioeconomics (e.g., bottom-up goal-driven human organizations). Centralized systems perform better with better components. Here, we show, by contrast, that a decentralized entity is more efficient at reaching a target when its components are less capable. Our findings reproduce experimental results for a living organism, predict that autonomous vehicles may perform better with simpler components, offer a fresh explanation for why biological evolution jumped from decentralized to centralized design, suggest how efficient movement might be achieved despite damaged centralized function, and provide a formula predicting the optimum capability of a system’s components so that it comes as close as possible to its target or goal.

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Bhattacharya, Sourabh, Abhishek Gupta, and Tamer Basar. "Decentralized Opportunistic Navigation Strategies for Multi-agent Systems in the Presence of an Adversary." IFAC Proceedings Volumes 44, no. 1 (January 2011): 11809–14. http://dx.doi.org/10.3182/20110828-6-it-1002.02186.

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Wang, Yuanzhe, Danwei Wang, and Senqiang Zhu. "A New Navigation Function Based Decentralized Control of Multi-Vehicle Systems in Unknown Environments." Journal of Intelligent & Robotic Systems 87, no. 2 (November 30, 2016): 363–77. http://dx.doi.org/10.1007/s10846-016-0450-0.

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Barrios-dV, Sergio, Michel Lopez-Franco, Jorge D. Rios, Nancy Arana-Daniel, Carlos Lopez-Franco, and Alma Y. Alanis. "An Autonomous Path Controller in a System on Chip for Shrimp Robot." Electronics 9, no. 3 (March 5, 2020): 441. http://dx.doi.org/10.3390/electronics9030441.

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This paper presents a path planning and trajectory tracking system for a BlueBotics Shrimp III®, which is an articulate mobile robot for rough terrain navigation. The system includes a decentralized neural inverse optimal controller, an inverse kinematic model, and a path-planning algorithm. The motor control is obtained based on a discrete-time recurrent high order neural network trained with an extended Kalman filter, and an inverse optimal controller designed without solving the Hamilton Jacobi Bellman equation. To operate the whole system in a real-time application, a Xilinx Zynq® System on Chip (SoC) is used. This implementation allows for a good performance and fast calculations in real-time, in a way that the robot can explore and navigate autonomously in unstructured environments. Therefore, this paper presents the design and implementation of a real-time system for robot navigation that integrates, in a Xilinx Zynq® System on Chip, algorithms of neural control, image processing, path planning, and inverse kinematics and trajectory tracking.

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Wang, Xinyi, Lele Xi, Yizhou Chen, Shupeng Lai, Feng Lin, and Ben M. Chen. "Decentralized MPC-Based Trajectory Generation for Multiple Quadrotors in Cluttered Environments." Guidance, Navigation and Control 01, no. 02 (June 2021): 2150007. http://dx.doi.org/10.1142/s2737480721500072.

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Challenges in motion planning for multiple quadrotors in complex environments lie in overall flight efficiency and the avoidance of obstacles, deadlock, and collisions among themselves. In this paper, we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption. A model predictive control (MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning. First, the motion primitives of each quadrotor are formulated as the boundary state constrained primitives (BSCPs) which are constructed with jerk limited trajectory (JLT) generation method, a boundary value problem (BVP) solver, to obtain time-optimal trajectories. They are then approximated with a neural network (NN), pre-trained using this solver to reduce the computational burden. The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee flight safety without deadlock. Finally, the reference trajectories are generated using the same BVP solver. Our simulation and experimental results demonstrate the superior performance of the proposed method.

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Lemasson, B. H., J. J. Anderson, and R. A. Goodwin. "Motion-guided attention promotes adaptive communications during social navigation." Proceedings of the Royal Society B: Biological Sciences 280, no. 1754 (March 7, 2013): 20122003. http://dx.doi.org/10.1098/rspb.2012.2003.

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Animals are capable of enhanced decision making through cooperation, whereby accurate decisions can occur quickly through decentralized consensus. These interactions often depend upon reliable social cues, which can result in highly coordinated activities in uncertain environments. Yet information within a crowd may be lost in translation, generating confusion and enhancing individual risk. As quantitative data detailing animal social interactions accumulate, the mechanisms enabling individuals to rapidly and accurately process competing social cues remain unresolved. Here, we model how motion-guided attention influences the exchange of visual information during social navigation. We also compare the performance of this mechanism to the hypothesis that robust social coordination requires individuals to numerically limit their attention to a set of n -nearest neighbours. While we find that such numerically limited attention does not generate robust social navigation across ecological contexts, several notable qualities arise from selective attention to motion cues. First, individuals can instantly become a local information hub when startled into action, without requiring changes in neighbour attention level. Second, individuals can circumvent speed–accuracy trade-offs by tuning their motion thresholds. In turn, these properties enable groups to collectively dampen or amplify social information. Lastly, the minority required to sway a group's short-term directional decisions can change substantially with social context. Our findings suggest that motion-guided attention is a fundamental and efficient mechanism underlying collaborative decision making during social navigation.

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