A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment

Xiroi II, an Evolved ASV Platform for Marine Multirobot Operations

In this paper, we present the design, development and a practical use of an Autonomous Surface Vehicle (ASV) as a modular and flexible platform for a large variety of marine tasks including the coordination strategies with other marine robots. This work tackles the integration of an open-source Robot-Operating-System (ROS)-based control architecture that provides the ASV with a wide variety of navigation behaviors. These new ASV capabilities can be used to acquire useful data from the environment to survey, map, and characterize marine habitats. In addition, the ASV is used as a radio frequency relay point between an Autonomous Underwater Vehicle (AUV) and the ground station as well as to enhance the Acoustic Communication Link (ACL) with the AUV. In order to improve the quality of the ACL, a new Marine Multirobot System (MMRS) coordination strategy has been developed that aims to keep both vehicles close to each other. The entire system has been successfully designed, implemented, and tested in real marine environment robotic tasks. The experimental tests show satisfactory results both in ROS-based navigation architecture and the MMRS coordination strategy resulting in a significant improvement of the quality of the ACL.

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network

Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader-follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

A Multi-Objective Reinforcement Learning Based Controller for Autonomous Navigation in Challenging Environments

In this paper, we introduce a self-trained controller for autonomous navigation in static and dynamic (with moving walls and nets) challenging environments (including trees, nets, windows, and pipe) using deep reinforcement learning, simultaneously trained using multiple rewards. We train our RL algorithm in a multi-objective way. Our algorithm learns to generate continuous action for controlling the UAV. Our algorithm aims to generate waypoints for the UAV in such a way as to reach a goal area (shown by an RGB image) while avoiding static and dynamic obstacles. In this text, we use the RGB-D image as the input for the algorithm, and it learns to control the UAV in 3-DoF (x, y, and z). We train our robot in environments simulated by Gazebo sim. For communication between our algorithm and the simulated environments, we use the robot operating system. Finally, we visualize the trajectories generated by our trained algorithms using several methods and illustrate our results that clearly show our algorithm’s capability in learning to maximize the defined multi-objective reward.

Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering

This paper addresses the development of a modular software architecture for the design/construction/operation of a remotely operated vehicle (ROV), based on systems engineering. First, systems engineering and the Vee model are presented with the objective of defining the interactions of the stakeholders with the software architecture development team and establishing the baselines that must be met in each development phase. In the development stage, the definition of the architecture and its connection with the hardware is presented, taking into account the use of the actor model, which represents the high-level software architecture used to solve concurrency problems. Subsequently, the structure of the classes is defined both at high and low levels in the instruments using the object-oriented programming paradigm. Finally, unit tests are developed for each component in the software architecture, quality assessment tests are implemented for system functions fulfillment, and a field sea trial for testing different modules of the vehicle is described. This approach is well suited for the development of complex systems such as marine vehicles and those systems which require scalability and modularity to add functionalities.

Formation Control of a Multi-Autonomous Underwater Vehicle Event-Triggered Mechanism Based on the Hungarian Algorithm

Among the key technologies of Autonomous Underwater Vehicle (AUV) leader–follower formations control, formation reconfiguration technology is one of the main technologies to ensure that multiple AUVs successfully complete their tasks in a complex operating environment. The biggest drawback of the leader–follower formations technology is the failure of the leader and the excessive communication pressure of the leader. Aiming at the problem of leader failure in multi- AUV leader–follower formations, the Hungarian algorithm is used to reconstruct the failed formation with a minimum cost, and the improvement of the Hungarian algorithm can solve the problem of a non-standard assignment. In order to solve the problem of an increased leader communication task after formation reconfiguration, the application of an event-triggered mechanism (ETM) can reduce unnecessary and useless communication, while the efficiency of the ETM can be improved through increasing the event-triggered conditions of the sampling error threshold. The simulation results of multi-AUV formation control show that the Hungarian algorithm proposed in this paper can deal with the leader failure in the multi-AUV leader–follower formation, and the ETM designed in this paper can reduce about 90% of the communication traffic of the formation which also proves the highly efficient performance of the improved ETM in the paper.

A Real-Time Collision Avoidance Framework of MASS Based on B-Spline and Optimal Decoupling Control

Real-time collision-avoidance navigation of autonomous ships is required by many application scenarios, such as carriage of goods by sea, search, and rescue. The collision avoidance algorithm is the core of autonomous navigation for Maritime autonomous surface ships (MASS). In order to realize real-time and free-collision under the condition of multi-ship encounter in an uncertain environment, a real-time collision avoidance framework is proposed using B-spline and optimal decoupling control. This framework takes advantage to handle the uncertain environment with limited sensing MASS which plans dynamically feasible, highly reliable, and safe feasible collision avoidance. First, owing to the collision risk assessment, a B-spline-based collision avoidance trajectory search (BCATS) algorithm is proposed to generate free-collision trajectories effectively. Second, a waypoint-based collision avoidance trajectory optimization is proposed with the path-speed decoupling control. Two benefits, a reduction of control cost and an improvement in the smoothness of the collision avoidance trajectory, are delivered. Finally, we conducted an experiment using the Electronic Chart System (ECS). The results reveal the robustness and real-time collision avoidance trajectory planned by the proposed collision avoidance system.

A Decision-Making Method for Autonomous Collision Avoidance for the Stand-On Vessel Based on Motion Process and COLREGs

A great number of collision accidents can be attributed to incongruous collision-avoidance actions between the give-way vessel and the stand-on vessel in a crossing or overtaking situation. If the give-way vessel does not take appropriate collision-avoidance action according to international regulations for preventing collisions at sea, the last barrier to pass safely is the appropriate and effective collision-avoidance action taken by the stand-on vessel. To find the proper autonomous collision-avoidance action of the stand-on vessel, a method is proposed that combines quantitative analysis rules of collision-avoidance with the deduction of nonlinear maneuvering motion process based on the mathematical model group, which conformity can reach 90%. This research presents a method to calculate the timing and most effective collision-avoidance actions for the stand-on vessel based on the four-stage theory of encountering vessels and the characteristics of vessel motion. The accuracy of the latest-action timing and the action amplitude for the stand-on vessel can be increased to the level of second and degree, respectively. A novel model of collision risk index is constructed by the latest time of the feasible collision-avoidance action on the precise of different course-altering amplitude. Methods to find the stand-on vessel’s proper collision-avoidance actions in the open sea are presented. The simulation indicates the proposed method for the stand-on vessel can make correct collision-avoidance decisions autonomously.

Multiple Task Assignment and Path Planning of a Multiple Unmanned Surface Vehicles System Based on Improved Self-Organizing Mapping and Improved Genetic Algorithm

This paper addresses multiple task assignment and path-planning problems for a multiple unmanned surface vehicle (USVs) system. Since it is difficult to solve multi-task allocation and path planning together, we divide them into two sub-problems, multiple task allocation and path planning, and study them separately. First, to resolve the multi-task assignment problem, an improved self-organizing mapping (ISOM) is proposed. The method can allocate all tasks in the mission area, and obtain the set of task nodes that each USV needs to access. Second, aiming at the path planning of the USV accessing the task nodes, an improved genetic algorithm (IGA) with the shortest path is proposed. To avoid USV collision during navigation, an artificial potential field function (APFF) is proposed. A multiple USV system with multi-task allocation and path planning is simulated. Simulation results verify the effectiveness of the proposed algorithms.

Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm

As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.