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Mobile robots, including ground robots, underwater robots, and unmanned aerial vehicles, play an increasingly important role in people’s work and lives. Path planning and obstacle avoidance are the core technologies for achieving autonomy in mobile robots, and they will determine the application prospects of mobile robots. This paper introduces path planning and obstacle avoidance methods for mobile robots to provide a reference for researchers in this field. In addition, it comprehensively summarizes the recent progress and breakthroughs of mobile robots in the field of path planning and discusses future directions worthy of research in this field. We focus on the path planning algorithm of a mobile robot. We divide the path planning methods of mobile robots into the following categories: graph-based search, heuristic intelligence, local obstacle avoidance, artificial intelligence, sampling-based, planner-based, constraint problem satisfaction-based, and other algorithms. In addition, we review a path planning algorithm for multi-robot systems and different robots. We describe the basic principles of each method and highlight the most relevant studies. We also provide an in-depth discussion and comparison of path planning algorithms. Finally, we propose potential research directions in this field that are worth studying in the future.

This paper presents the design and experiment of an autonomous underwater robot which can change the geometric configuration of its actuators, according to mission requirements or environmental constraints. The robot consists of two subsystems: forward part with three thrusters and backward part with four thrusters. The position and orientation of these thrusters can be dynamically changed during missions. Being different from most of other reconfigurable underwater robots which were designed as linked-modules, our robot has a unified design. It is suitable for specific mission in confined environments (e.g., karst exploration) in which the robot has to modify its shape to go through a narrow section or align the most part of its thrusters in the direction of a strong current, for examples. The design procedure, from hardware to software, of the robot is presented and experimental results are shown to demonstrate the versatility of the robot. Furthermore, the discussion and comparison between our robot and other underwater robots with adaptable actuation geometry are presented to highlight advantages of our design. Finally, the idea of using our robot for classic docking problem, which has some common features with karst exploration requirements in using dynamically reconfigurable robots, is discussed.

A dynamically reconfigurable underwater robot, which can vary its configuration during a mission, would be useful for confined environment exploration and docking because of its versatility. A mission can be performed by choosing among different configurations, and the energy cost may increase, owing to the reconfigurability of the robot. Energy saving is the critical issue in long-range missions with underwater robots. Moreover, control allocation must be considered for a redundant system and input constraints. We propose an approach for an energy-efficient configuration and control allocation for a dynamically reconfigurable underwater robot that is built for karst exploration. The proposed method is based on sequential quadratic programming, which minimizes an energy-like criterion with respect to robotic constraints, i.e., mechanical limitations, actuator saturations, and a dead zone. The optimization problem is solved in each sampling instant. Two popular tasks for underwater robots, i.e., path-following and station-keeping (observation) problems, are simulated, and the simulation results show the efficiency of the method. Moreover, an experiment is carried out to highlight the results.

Sliding mode control is a robust controller against modeling imprecisions and external disturbances, successfully employed to the dynamic positioning of autonomous underwater robot. In order to improve the performance of the whole system, the discontinuity in the control law must be smoothed out to avoid the undesirable chattering and unwanted ripples. One of the disadvantages of conventional sliding mode is great vulnerability in the presence of noise. However, noise and some initial condition causing undesirable chattering phenomenon and unwanted ripples in the control input. This paper describes the development of a depth control system for autonomous underwater robot. In this paper we used the sliding surface term and its derivation with adaptive gains in control law instead of the sign function with fixed gain. The proposed controller has been designed to solve great vulnerability of sliding mode control at the presence of noise. For contrast one of factors causing chattering, big controller gain, the gain of controller adapted according to state condition and uncertainties. Due to in the proposed controller there is no sign function, so our controller is not vulnerability to noise. Using this controller, ripples and unexpected sharp peak of the input control signal were canceled and control signal was smoother than conventional sliding mode controller with boundary layer. The stability and convergence properties of the closed-loop system are analytically proved using Lyapunov stability theorem. Simulation results are presented in order to demonstrate the control system performance.

With the increasing development of marine science and technology, autonomous underwater robots have become a hot spot in the field of underwater research and applications, and have been widely used in deep-sea scientific research, marine resources research, and deep-sea security. This paper focuses on analyzing the current research and development status and application prospects of autonomous underwater robots and predicts their future development trend. Aiming at the difficulties in engineering practice of path search in underwater unknown space, the traditional space path exploration method is analyzed, and a hybrid search algorithm is proposed to plan a reasonable and efficient dynamic path for target search in unknown underwater space.

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-agent GAIL (MAG) algorithm is proposed. The GAIL enables the AUV to directly learn from expert demonstrations, overcoming the difficulty of slow initial training of the network. Parallel training of multi-agents reduces the high correlation between samples to avoid local convergence. In addition, a reward function is designed to help training. Finally, the results show that in the unity simulation platform test, the proposed algorithm has a strong optimal decision-making ability in the tracking process.

Underwater target acquisition and identification performed by manipulators having broad application prospects and value in the field of marine development. Conventional manipulators are too heavy to be used for small target objects and unsuitable for shallow sea working. In this paper, a bio-inspired Father–Son Underwater Robot System (FURS) is designed for underwater target object image acquisition and identification. Our spherical underwater robot (SUR), as the father underwater robot of the FURS, has the ability of strong dynamic balance and good maneuverability, can realize approach the target area quickly, and then cruise and surround the target object. A coiling mechanism was installed on SUR for the recycling and release of the son underwater robot. A Salamandra-inspired son underwater robot is used as the manipulator of the FURS, which is connected to the spherical underwater robot by a tether. The son underwater robot has multiple degrees of freedom and realizes both swimming and walking movement modes. The son underwater robot can move to underwater target objects. The vision system is installed to enable the FURS to acquire the image information of the target object with the aid of the camera, and also to identify the target object. Finally, verification experiments are conducted in an indoor water tank and outdoor swimming pool conditions to verify the effectiveness of the proposed in this paper.

Amphibious Spherical Robots (ASRs) use an electric field to communicate and collaborate effectively in a turbid water of confined spaces where other mode communication modalities failed. This paper proposes an embedded architecture formation strategy for a group of turtle-inspired amphibious robots to maintain a long distance-parameterized path based on dynamic visual servoing. Inspired by this biological phenomenon, we design an artificial multi-robot cooperative mode and explore an electronic communication and collaborate devices, the control method is based in particular on underwater environment and also conduct a detailed analysis of control motion module. The objectives of control strategies are divided into four categories: The first strategy is that the leader robot controls the action of the overall robots to maintain collaborate together during motion along a desired geometric path and to follow a timing law that the communication efficiency and the arrival times to assigned sites. Furthermore, we design an adaptive visual servoing controller for trajectory tracking task, taking into account system dynamics with environment interactions. After that, the third strategy is a centralized optimization algorithm for the redistribution of target mission changes. Finally, this paper also proposes a new method of control strategies in order to guarantee that each robot in the team moves together according to the preset target toward its location in the group formation based on communication and stability modules.

Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.