Cooperative Navigation Algorithm Research Articles

An Efficient Reinforcement Learning-Based Cooperative Navigation Algorithm for Multiple UAVs in Complex Environments

An Efficient Reinforcement Learning-Based Cooperative Navigation Algorithm for Multiple UAVs in Complex Environments

A Local-and-Global Attention Reinforcement Learning Algorithm for Multiagent Cooperative Navigation.

The cooperative navigation algorithm is the crucial technology for multirobot systems to accomplish autonomous collaborative operations, and it is still a challenge for researchers. In this work, we propose a new multiagent reinforcement learning algorithm called multiagent local-and-global attention actor-critic (MLGA2C) for multiagent cooperative navigation. Inspired by the attention mechanism, we design the local-and-global attention module to dynamically extract and encode critical environmental features. Meanwhile, based on the centralized training and decentralized execution (CTDE) paradigm, we extend a new actor-critic method to handle feature encoding and make navigation decisions. We also evaluate the proposed algorithm in two cooperative navigation scenarios: static target navigation and dynamic pedestrian target tracking. The multiple experimental results show that our algorithm performs well in cooperative navigation tasks with increasing agents.

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%.

Air-ground multi-agent system cooperative navigation based on factor graph optimization SLAM

In this study, a cooperative navigation algorithm centered on factor graph optimization—simultaneous localization and mapping (FGO-SLAM) is presented for an air-ground multi-agent system. The algorithm prioritizes the control of error statuses during the position and attitude estimation procedure throughout the entire back-end optimization process. In the conventional extended kalman filtering (EKF) algorithm, periodic cumulative errors may arise, introducing uncertainty to the estimation process. The application of the FGO algorithm not only mitigates deviation but also stabilizes errors, thereby eliminating the accumulation of periodic errors. In comparison to the practical EKF-SLAM, FGO-SLAM serves as a semi-offline optimization system that leverages key frames to minimize computational load. During multi-agent simulations, when two or more agents have overlapping field views, landmark data is merged, enhancing the optimization effectiveness. Through simulation experiments, the proposed algorithm demonstrates a 40% reduction in position error and a 41% reduction in attitude error, affirming the efficacy of FGO-SLAM for cooperative navigation.

Multi-AUV Cooperative Navigation Algorithm Based on Factor Graph With Stretching Nodes’ Strategy

Multi-AUV Cooperative Navigation Algorithm Based on Factor Graph With Stretching Nodes’ Strategy

Distributed Scheduling for Cooperative Navigation Based on Uncertainty Evolution

Cooperative navigation is a promising solution for many emerging applications of the Internet of Robotic Things to provide accurate location information via relative measurement, information exchange, and information fusion. However, these operations cause large communication overhead and thus are impractical for applications in resource-constrained environment. To alleviate this problem, a distributed scheduling algorithm based on uncertainty evolution under the framework of belief propagation (BP) is proposed in this paper. Specifically, we first derive a computationally efficient upper bound to characterize the approximated reduction of position uncertainty through cooperation. Then, a novel distributed cooperative navigation algorithm with candidate selection scheme is proposed for robots to guarantee satisfactory performance while reducing communication rate. Numerical simulations show that the proposed algorithm can achieve higher navigation accuracy and more efficient communication with limited on-board resources.

Cooperative Navigation for Heterogeneous Air-Ground Vehicles Based on Interoperation Strategy

This paper focuses on the cooperative navigation of heterogeneous air-ground vehicle formations in a Global Navigation Satellite System (GNSS) challenged environment and proposes a cooperative navigation method based on motion estimation and a regionally optimal path planning strategy. In air-ground vehicle formations, unmanned ground vehicles (UGVs) are equipped with low-precision inertial navigation measurement units and wireless range sensors, which interact with unmanned aerial vehicles (UAVs) equipped with high-precision navigation equipment for cooperative measurement information and use the UAVs as aerial benchmarks for cooperative navigation. Firstly, the Interacting Multiple Model (IMM) algorithm is used to predict the next moment’s motion position of the UGVs. Then regional real-time path optimization algorithms are introduced to design the motion position of the high-precision UAVs so as to improve the formation’s configuration and reduce the geometric dilution of precision (GDOP) of the configuration. Simulation results show that the Dynamic Optimal Configuration Cooperative Navigation (DOC-CN) algorithm can reduce the GDOP of heterogeneous air-ground vehicle formations and effectively improve the overall navigation accuracy of the whole formation. The method is suitable for the cooperative navigation environment of heterogeneous air-ground vehicle formations under GNSS-challenged conditions.

Cooperative Navigation Algorithm of Extended Kalman Filter Based on Combined Observation for AUVs

The navigation and positioning of multi-autonomous underwater vehicles (AUVs) in the complex and variable marine environment is a significant and much-needed area of attention, especially considering the fact that cooperative navigation technology is the essential method for multiple AUVs to solve positioning problems. When the extended Kalman filter (EKF) is applied for underwater cooperative localization, the outliers in the sensor observations cause unknown errors in the measurement system due to deep-sea environmental factors, which are difficult to calibrate and cause a significant reduction in the co-location accuracy of AUVs, and can even cause problems with a divergence of estimation error. In this paper, we proposed a cooperative navigation method of the EKF algorithm based on the combined observation of multiple AUVs. Firstly, the corresponding cooperative navigation model is established, and the corresponding measurement model is designed. Then, the EKF model based on combined observation is designed and constructed, and the unknown error is eliminated by introducing a previously measured value. Finally, simulation tests and lake experiments are designed to verify the effectiveness of the algorithm. The results indicate that the EKF algorithm based on combined observation can approximately eliminate errors and improve the accuracy of cooperative localization when the unknown measurement error cannot be calibrated by common EKF methods. The effect of state estimation is improved, and the accuracy of co-location can be effectively improved to avoid serious declines in—and divergence of—estimation accuracy.

Multi-AUV Cooperative Navigation Algorithm Based on Temporal Difference Method

To reduce the cooperative positioning error and improve the navigation accuracy, a single master–slave AUV cooperative navigation method is proposed in this paper, which mainly focuses on planning the optimal path of the master AUV by the time difference (TD) method, under the premise that the path of the slave AUV has been planned. First, the model of multi-AUV cooperative navigation is established, and the observable problem of the system is analyzed. Second, for the single master–slave AUV cooperative navigation system, a Markov decision process (MDP)-based multi-AUV cooperative navigation model is established, and the master AUV path planning method is designed based on the TD method. Finally, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) nonlinear filtering algorithms are applied to simulate and verify the algorithm that is proposed in this paper. The results show that the theoretical positioning error of the slave AUV can be controlled to about 3.2m by planning the path of the master AUV using the TD method. This method can not only reduce the observation error and positioning error of the slave AUV during the whole cooperative navigation process, but also keep the relative measurement distance between the master AUV and the slave AUV within an appropriate range.

A multi-AUV cooperative navigation method

Cooperative navigation is one of the key methods for multiple autonomous underwater vehicles (AUVs) to obtain accurate positions when performing tasks underwater. In the realistic state-space model of the multi-AUV cooperative navigation system, where the system noise does not satisfy the additivity, it is necessary to augment the dimension of the state variables before nonlinear filtering. Aiming at the problem that the error of traditional algorithms increases linearly with the dimension of state-space, a cooperative navigation method based on Augmented Embedded Cubature Kalman filter (AECKF) algorithm is proposed. The experiment results show that the AECKF cooperative navigation algorithm has better positioning accuracy and stability than the traditional algorithm.

An EKF based overlapping coalition formation game for cooperative wireless network navigation

The problem of efficient link selections for cooperative navigation is investigated in dynamic wireless networks, using an extended Kalman filter based overlapping coalition formation (OCF) game approach. Due to the densely deployment of nodes without proper link selections, the conventional cooperative navigation approaches usually yield high time-frequency resources as the existing resource management methods only work properly in static localisation systems. A new link selection mechanism which is reliable with limited resources in dynamic positioning scenarios is proposed. The varying topological structure and states of every node will be taken into consideration while each node abandons a considerable number of unnecessary links. Firstly, the state of every mobile node will be estimated in real-time. Then a selection criterion is applied to determine whether nodes are well navigated. If the criterion is satisfied, an OCF game will be employed to construct the effective link selection. Otherwise, a re-link mechanism will be used to obtain precise state information of each node. Numerical results show that the cooperative navigation algorithm can significantly improve the positioning accuracy and the proposed OCF game approach can build a cooperative network with limited number of links at the cost of appropriate performance degradation.

ANALYTICAL EXPRESSION FOR UNCERTAINTY PROPAGATION OF AERIAL COOPERATIVE NAVIGATION

In this paper, the propagation of uncertainty in a cooperative navigation algorithm (CNA) for a group of flying robots (FRs) is investigated. Each FR is equipped with an inertial measurement unit (IMU) and range-bearing sensors to measure the relative distance and bearing angles between the agents. In this regard, an extended Kalman filter (EKF) is implemented to estimate the position and rotation angles of all the agents. For further studies, a relaxed analytical performance index through a closed-form solution is derived. Moreover, the effects of the sensors noise covariance and the number of FRs on the growth rate of the position error covariance is investigated. Analytically, it is shown that the covariance of position error in the vehicles equipped with the IMU is proportional to the cube of time. However, the growth rate of the navigation error is, considerably more rapid compared to a mobile robot group. Furthermore, the covariance of position error is independent of the path and noise resulting from the relative position measurements. Further, it merely depends on both the size of the group and noise characteristics of the accelerometers. Lastly, the analytical results are validated through comprehensive Guidance, Navigation, and Control (GNC) in-the-loop simulations.

TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

RCKF Cooperative Navigation Algorithm for Tightly Coupled Vehicle Ad Hoc Networks Based on Huber M Estimation

As a core technology of cooperative navigation, relative position sensing is crucial for vehicle intelligent driving. It plays a key role in the cooperative positioning algorithm of vehicle ad hoc networks (VANETs). However, because of system nonlinearity and colored noise, the acquisition of a posteriori information of cooperative positioning under the same hardware platform is usually limited to a certain accuracy. To address these problems, a vehicle cooperative positioning state estimation algorithm based on a robust cubature Kalman filter (RCKF) of Huber M estimation is herein proposed. The algorithm combines the cubature rule for nonlinear updating, converts the measurement equation into a linear regression problem, and uses M estimation to solve it. The colored noise is suppressed by reducing the weight of disturbed measurements by the Huber loss function. The experimental results of tightly coupled vehicle cooperative navigation show that compared with the extended Kalman filter (EKF) and cubature Kalman filter (CKF), the positioning accuracy of the RCKF proposed in this paper is improved by 26.04% and 27.10% respectively, which effectively improves the accuracy and robustness of relative position estimation. It provides a reference system quality control strategy for vehicle cooperative positioning solutions.

Robust Student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math> </inline-formula>-Based Cooperative Navigation for Autonomous Underwater Vehicles

A robust leader–slave cooperative navigation (CN) algorithm for autonomous underwater vehicles (AUVs) based on the Student’s $t$ extended Kalman filter (SEKF) is proposed. Compared with the conventional EKF based on a Gaussian distributed noise assumption, which has been widely used in the field of CN, the Student’s $t$ -based filtering algorithms show an improved robustness against outliers existing in the process and measurement noises. The utilization of the Student’s $t$ distribution can minimize the negative effect induced by the outliers existing in the underwater acoustic communication system and the cheap but unreliable dead reckoning sensors equipped on the slave AUVs. After a detailed derivation of the robust CN algorithm based on the SEKF, two approximation methods that are required in the Student’s $t$ -based filter are discussed and compared. Simulation results show the efficiency and superiority of the robust SEKF-based leader–slave CN algorithm as compared with the conventional EKF-based CN algorithm. The validity of the proposed CN algorithm is also evaluated on field trial data, and the performance of different degrees of freedom (DOF) values, which determine the tail behavior of the Student’s $t$ distribution is compared and analyzed, and then the link between the DOF value and the robust effect of the Student’s $t$ distribution is revealed, which will act as a guide when applying the Student’s $t$ -based filter in the field of CN.

Polar Cooperative Navigation Algorithm for Multi-Unmanned Underwater Vehicles Considering Communication Delays.

To solve the navigation accuracy problems of multi-Unmanned Underwater Vehicles (multi-UUVs) in the polar region, a polar cooperative navigation algorithm for multi-UUVs considering communication delays is proposed in this paper. UUVs are important pieces of equipment in ocean engineering for marine development. For UUVs to complete missions, precise navigation is necessary. It is difficult for UUVs to establish true headings because of the rapid convergence of Earth meridians and the severe polar environment. Based on the polar grid navigation algorithm, UUV navigation in the polar region can be accomplished with the Strapdown Inertial Navigation System (SINS) in the grid frame. To save costs, a leader-follower type of system is introduced in this paper. The leader UUV helps the follower UUVs to achieve high navigation accuracy. Follower UUVs correct their own states based on the information sent by the leader UUV and the relative position measured by ultra-short baseline (USBL) acoustic positioning. The underwater acoustic communication delay is quantized by the model. In this paper, considering underwater acoustic communication delay, the conventional adaptive Kalman filter (AKF) is modified to adapt to polar cooperative navigation. The results demonstrate that the polar cooperative navigation algorithm for multi-UUVs that considers communication delays can effectively navigate the sailing of multi-UUVs in the polar region.