2023

Jump to: 2022

16 pages, 774 KiB

Open AccessArticle

Multi-Agent Task Allocation with Multiple Depots Using Graph Attention Pointer Network

by Wen Shi and Chengpu Yu

Electronics 2023, 12(16), 3378; https://doi.org/10.3390/electronics12163378 - 08 Aug 2023

Viewed by 770

The study of the multi-agent task allocation problem with multiple depots is crucial for investigating multi-agent collaboration. Although many traditional heuristic algorithms can be adopted to handle the concerned task allocation problem, they are not able to efficiently obtain optimal or suboptimal solutions. [...] Read more.

The study of the multi-agent task allocation problem with multiple depots is crucial for investigating multi-agent collaboration. Although many traditional heuristic algorithms can be adopted to handle the concerned task allocation problem, they are not able to efficiently obtain optimal or suboptimal solutions. To this end, a graph attention pointer network is built in this paper to deal with the multi-agent task allocation problem. Specifically, the multi-head attention mechanism is employed for the feature extraction of nodes, and a pointer network with parallel two-way selection and parallel output is introduced to further improve the performance of multi-agent cooperation and the efficiency of task allocation. Experimental results are provided to show that the presented graph attention pointer network outperforms the traditional heuristic algorithms. Full article

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2022

Jump to: 2023

15 pages, 5543 KiB

Open AccessArticle

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

by Guoxing Bai, Yu Meng, Li Liu, Qing Gu, Jianxiu Huang, Guodong Liang, Guodong Wang, Li Liu, Xinrui Chang and Xin Gan

Electronics 2022, 11(24), 4232; https://doi.org/10.3390/electronics11244232 - 19 Dec 2022

Cited by 3 | Viewed by 1899

Abstract

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. [...] Read more.

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions. Full article

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16 pages, 1319 KiB

Open AccessReview

High-Definition Map Representation Techniques for Automated Vehicles

by Babak Ebrahimi Soorchaei, Mahdi Razzaghpour, Rodolfo Valiente, Arash Raftari and Yaser Pourmohammadi Fallah

Electronics 2022, 11(20), 3374; https://doi.org/10.3390/electronics11203374 - 19 Oct 2022

Cited by 7 | Viewed by 3306

Abstract

Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. [...] Read more.

Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment’s information. Among these efforts is the high-definition (HD) map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD maps for automated vehicles. The current state of autonomous vehicle (AV) mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy. Full article

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14 pages, 1090 KiB

Open AccessArticle

Cooperative Localization Based on Augmented State Belief Propagation for Mobile Agent Networks

by Bolun Zhang, Guangen Gao and Yongxin Gao

Electronics 2022, 11(13), 1959; https://doi.org/10.3390/electronics11131959 - 22 Jun 2022

Cited by 2 | Viewed by 1114

Abstract

Belief propagation (BP) is widely used to solve the cooperative localization problem due to its excellent performance and natural distributed structure of implementation. For a mobile agent network, its factor graph inevitably encounters loops. In this case, the BP algorithm becomes iterative and [...] Read more.

Belief propagation (BP) is widely used to solve the cooperative localization problem due to its excellent performance and natural distributed structure of implementation. For a mobile agent network, its factor graph inevitably encounters loops. In this case, the BP algorithm becomes iterative and can only provide an approximate marginal probability density function of the estimate with finite iterations. We propose an augmented-state BP algorithm for mobile agent networks to alleviate the effect of loops. By performing state augmentation, the messages in the factor graph will actually be allowed to be backward propagated, which reduces the number of loops in the factor graph, increases the available information of agents, and thus, benefits the localization. Experimental results demonstrate the better performance of the proposed algorithm over the original BP method. Full article

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8 pages, 1101 KiB

Open AccessArticle

Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization

by Zhaobo Li, Yimin Deng and Shuanglei Sun

Electronics 2022, 11(9), 1377; https://doi.org/10.3390/electronics11091377 - 26 Apr 2022

Cited by 4 | Viewed by 1570

Abstract

A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and [...] Read more.

A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and parameter constraints were designed by integrating factors such as distance error, relative speed, acceleration and impact, and a mathematical model of the adaptive cruise predictive control optimization problem was constructed. Finally, PIO and PSO were used to solve the optimal control law for MPC and simulated by Matlab. The results show that the adaptive cruise system can reach a steady state quickly with the control laws of PIO or PSO. However, due to the global optimization and fast convergence characteristic, variable compass operator PIO has better time response characteristics. Full article

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16 pages, 1071 KiB

Open AccessArticle

A 3D Range-Only SLAM Algorithm Based on Improved Derivative UKF

by Chao Tang, Dajian Zhou, Lihua Dou and Chaoyang Jiang

Electronics 2022, 11(7), 1109; https://doi.org/10.3390/electronics11071109 - 31 Mar 2022

Cited by 3 | Viewed by 1857

Abstract

In this study, we constructed a 3D range-only (RO) localization algorithm based on improved unscented Kalman filtering (UKF). The algorithm can determine the location of unknown UWB nodes in a 3D environment through a moving node with low computational complexity, which can help [...] Read more.

In this study, we constructed a 3D range-only (RO) localization algorithm based on improved unscented Kalman filtering (UKF). The algorithm can determine the location of unknown UWB nodes in a 3D environment through a moving node with low computational complexity, which can help agents to accurately identify feature points in 3D SLAM based only on the range. Specifically, we established an original UKF framework based the 3D RO localization algorithm, and developed a derivative UKF framework to reduce the computational complexity of the algorithm. We used singular value decomposition to compensate for the robustness of the algorithm. Next, we performed a theoretical analysis to show that our method reduces the computational burden without reducing the stability or accuracy of the system. Finally, we conducted numerical simulations and physical experiments to show the effectiveness of the developed 3D RO localization algorithm. Full article

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15 pages, 1493 KiB

Open AccessReview

A Survey of Multi-Agent Cross Domain Cooperative Perception

by Zhongpan Zhu, Qiwei Du, Zhipeng Wang and Gang Li

Electronics 2022, 11(7), 1091; https://doi.org/10.3390/electronics11071091 - 30 Mar 2022

Cited by 2 | Viewed by 3232

Abstract

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the [...] Read more.

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded. Full article

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17 pages, 3225 KiB

Open AccessFeature PaperArticle

Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO

by Jiandong Zhang, Yuyang Chen, Qiming Yang, Yi Lu, Guoqing Shi, Shuo Wang and Jinwen Hu

Electronics 2022, 11(7), 1028; https://doi.org/10.3390/electronics11071028 - 25 Mar 2022

Cited by 10 | Viewed by 2199

Abstract

With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus [...] Read more.

With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task. Full article

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19 pages, 537 KiB

Open AccessArticle

Resilient Consensus for Multi-Agent Systems in the Presence of Sybil Attacks

by Xiaochen Dong, Yiming Wu, Ming Xu and Ning Zheng

Electronics 2022, 11(5), 800; https://doi.org/10.3390/electronics11050800 - 04 Mar 2022

Viewed by 1896

Abstract

This paper investigates the problem of resilient consensus control for discrete-time linear multi-agent systems under Sybil attacks. We consider a node to be a Sybil node if it can generate a large number of false identities in the graph as a way of [...] Read more.

This paper investigates the problem of resilient consensus control for discrete-time linear multi-agent systems under Sybil attacks. We consider a node to be a Sybil node if it can generate a large number of false identities in the graph as a way of gaining disproportionate influence on the consensus performance of the network. Such attacks can easily invalidate existing resilient consensus algorithms that assume an upper bound on the number of malicious nodes in the network. To this end, we first built a new attack model based on the characteristics of the Sybil nodes. In addition, a quantized-data-based transmission scheme was developed for identifying and resisting Sybil nodes in the network. Then, an attack-resilient consensus algorithm was developed, where each normal node sends the quantitative data information with a specific label, which is generated by truncated normal distribution sampling to their neighbors. We give sufficient graphical conditions for attack models considering limited energy to ensure the consensus of linear multi-agent systems. Finally, numerical simulation examples are provided to validate the effectiveness of the proposed methods. Full article

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12 pages, 559 KiB

Open AccessArticle

Localization Error Modeling for Autonomous Driving in GPS Denied Environment

by Feihu Zhang, Zhiliang Wang, Yaohui Zhong and Liyuan Chen

Electronics 2022, 11(4), 647; https://doi.org/10.3390/electronics11040647 - 18 Feb 2022

Cited by 3 | Viewed by 1795

Abstract

Precise localization plays a crucial role in autonomous driving applications. As Global Position System (GPS) signals are often susceptible to interference or even not fully available, odometry sensors can precisely calculate positions in urban environments. However, the cumulative error is thus originated with [...] Read more.

Precise localization plays a crucial role in autonomous driving applications. As Global Position System (GPS) signals are often susceptible to interference or even not fully available, odometry sensors can precisely calculate positions in urban environments. However, the cumulative error is thus originated with time increasing. This paper proposes an effective empirical formula to model such unbounded cumulative errors from noisy relative measurements. Furthermore, a recursive cumulative error expression has been established by calculating the first and second moments of the Ackermann model. Finally, based on the developed formula, numerical experiments have also been conducted to verify the validity of the proposed model. Full article

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14 pages, 1915 KiB

Open AccessArticle

A Multisensor Fusion-Based Cooperative Localization Scheme in Vehicle Networks

by Ting Yin, Decai Zou, Xiaochun Lu and Cheng Bi

Electronics 2022, 11(4), 603; https://doi.org/10.3390/electronics11040603 - 16 Feb 2022

Cited by 4 | Viewed by 1806

Abstract

Utilizing the measured distance and information exchanged between two different nodes to cooperatively locate in a mobile network has become a solution to replace global navigation satellite system (GNSS) positioning. However, the localization accuracy of the belief propagation-based cooperative localization scheme is substantially [...] Read more.

Utilizing the measured distance and information exchanged between two different nodes to cooperatively locate in a mobile network has become a solution to replace global navigation satellite system (GNSS) positioning. However, the localization accuracy of the belief propagation-based cooperative localization scheme is substantially influenced by the number of neighbors. In this paper, we propose a cooperative localization scheme combined with a trajectory tracking algorithm. With an insufficient number of neighbors, the trajectory tracking algorithm is utilized to participate in the positioning process of agents. Concretely, we carry out sensor information fusion and utilize quantum-behaved, particle-swarm-optimized, bidirectional long short-term memory (QPSO–BiLSTM) as a trajectory tracking strategy, to precisely predict the positions of agents. It is evident from simulations and results that the proposed cooperative localization scheme performs better than the belief propagation (BP)-based cooperative localization scheme in position error. Full article

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19 pages, 3322 KiB

Open AccessArticle

Autonomous Traffic System for Emergency Vehicles

by Mamoona Humayun, Maram Fahhad Almufareh and Noor Zaman Jhanjhi

Electronics 2022, 11(4), 510; https://doi.org/10.3390/electronics11040510 - 09 Feb 2022

Cited by 27 | Viewed by 3078

Abstract

An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to [...] Read more.

An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay. The proposed system passes the EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane. The shoulder lane is a lane that cyclists and motorcyclists will use in normal situations. However, when an EV enters the communication range, traffic from the adjacent lane will move to the shoulder lane. As the number of vehicles on the road increases rapidly, crossing the EV in the shortest possible time is crucial. The EVMS and algorithms are presented in this study to find the optimal vehicle sequence that gives EVs the highest priority. The proposed solution uses cutting-edge technologies (IoT Sensors, GPS, 5G, and Cloud computing) to collect and pass EVs’ information to the Roadside Units (RSU). The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles. Full article

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22 pages, 1070 KiB

Open AccessArticle

Autonomous Maneuver Decision Making of Dual-UAV Cooperative Air Combat Based on Deep Reinforcement Learning

by Jinwen Hu, Luhe Wang, Tianmi Hu, Chubing Guo and Yanxiong Wang

Electronics 2022, 11(3), 467; https://doi.org/10.3390/electronics11030467 - 05 Feb 2022

Cited by 32 | Viewed by 4863

Abstract

Autonomous maneuver decision making is the core of intelligent warfare, which has become the main research direction to enable unmanned aerial vehicles (UAVs) to independently generate control commands and complete air combat tasks according to environmental situation information. In this paper, an autonomous [...] Read more.

Autonomous maneuver decision making is the core of intelligent warfare, which has become the main research direction to enable unmanned aerial vehicles (UAVs) to independently generate control commands and complete air combat tasks according to environmental situation information. In this paper, an autonomous maneuver decision making method is proposed for air combat by two cooperative UAVs, which is showcased by using the typical olive formation strategy as a practical example. First, a UAV situation assessment model based on the relative situation is proposed, which uses the real-time target and UAV location information to assess the current situation or threat. Second, the continuous air combat state space is discretized into a 13 dimensional space for dimension reduction and quantitative description, and 15 typical action commands instead of a continuous control space are designed to reduce the difficulty of UAV training. Third, a reward function is designed based on the situation assessment which includes the real-time gain due to maneuver and the final combat winning/losing gain. Fourth, an improved training data sampling strategy is proposed, which samples the data in the experience pool based on priority to accelerate the training convergence. Fifth, a hybrid autonomous maneuver decision strategy for dual-UAV olive formation air combat is proposed which realizes the UAV capability of obstacle avoidance, formation and confrontation. Finally, the air combat task of dual-UAV olive formation is simulated and the results show that the proposed method can help the UAVs defeat the enemy effectively and outperforms the deep Q network (DQN) method without priority sampling in terms of the convergence speed. Full article

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