Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments

A novel neural network model with distributed evolutionary approach for big data classification

The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.

Formation Control of Multiple Underactuated Surface Vessels with a Disturbance Observer

To maintain the formation of underactuated surface vessels (USVs), this study designs a formation controller based on a virtual structure strategy. The problem of formation control is transformed into the problems of tracking the USV position and the virtual structure point position. Meanwhile, to eliminate the effects of model parameter uncertainties and external environment disturbances on USV tracking control, a compensation control algorithm based on disturbance estimation is proposed. The Lyapunov theorem is introduced to ensure that the trajectory tracking error of the proposed control algorithm eventually converges to any small region, which confirms global stability of the designed tracking error. The simulation results demonstrate that the proposed controller can eliminate the effect of external uncertain interference and maintain the formation of multiple USVs.

Self-Organizing Cooperative Pursuit Strategy for Multi-USV with Dynamic Obstacle Ships

A self-organizing cooperation strategy for multiple unmanned surface vessels (USVs) to pursue intelligent evaders in the case of a dynamic obstacle vessel is proposed. Firstly, a self-organizing cooperative hunting strategy is proposed to form an Apollonius circle. According to the escape strategies of evaders under different encirclement states, the pursuers are divided into pursuit group and ambush group. The pursuit group drives the evaders into the ambush area and completes the encirclement together with the ambush group. In order to better deal with the dynamic obstacle ships encountered in the pursuit process in the dynamic ocean environment, the artificial potential field-based collision avoidance method for inter-USV and dynamic collision avoidance strategy for encountering obstacle ships based on International Regulations for Preventing Collisions at Sea (COLREGs) are proposed. The simulation results show that the algorithm can make the pursuers complete the encirclement of the evaders and has good obstacle avoidance performance and flexibility in the environment with dynamic obstacle ships.

Application of bio-inspired optimization algorithms in food processing

Bio-inspired optimization techniques (BOT) are part of intelligent computing techniques. There are several BOTs available and many new BOTs are evolving in this era of industrial revolution 4.0. Genetic algorithm, particle swarm optimization, artificial bee colony, and grey wolf optimization are the techniques explored by researchers in the field of food processing technology. Although, there are other potential methods that may efficiently solve the optimum related problem in food industries. In this review, the mathematical background of the techniques, their application and the potential microbial-based optimization methods with higher precision has been surveyed for a complete and comprehensive understanding of BOTs along with their mechanism of functioning. These techniques can simulate the process efficiently and able to find the near-to-optimal value expeditiously.

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration-exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration-exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one's ability to understand and manage the intricacies of the exploration-exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

As one of the challenging tasks of multiple autonomous underwater vehicles systems, the realization of target hunting is the great significance. The multiple autonomous underwater vehicle target hunting is studied in this article. In some research, because the hunting members cannot reach the hunting point at the same time, the hunting time is long or the target escapes. To improve the efficiency of the target hunting, the leader–follower formation algorithm is introduced. Firstly, the task is assigned based on the distance between the autonomous underwater vehicle and the target. Then, the autonomous underwater vehicles with the same task are formed based on leader–follower mode, and the formation is kept to track the target. In the final capture phase, multiple autonomous underwater vehicle system use angle matching algorithm to round up target. The simulation results show that the proposed algorithm can effectively accomplish the target hunting task, save the hunting time, and avoid the target escape. Compared with the bioinspired neural network algorithm, the proposed algorithm shows better performance.

Cooperative Robots to Observe Moving Targets: Review

The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

Coordinated Dynamic Behaviors for Multirobot Systems With Collision Avoidance

In this paper, we propose a novel methodology for achieving complex dynamic behaviors in multirobot systems. In particular, we consider a multirobot system partitioned into two subgroups: 1) dependent and 2) independent robots. Independent robots are utilized as a control input, and their motion is controlled in such a way that the dependent robots solve a tracking problem, that is following arbitrarily defined setpoint trajectories, in a coordinated manner. The control strategy proposed in this paper explicitly addresses the collision avoidance problem, utilizing a null space-based behavioral approach: this leads to combining, in a non conflicting manner, the tracking control law with a collision avoidance strategy. The combination of these control actions allows the robots to execute their task in a safe way. Avoidance of collisions is formally proven in this paper, and the proposed methodology is validated by means of simulations and experiments on real robots.

A Bio-Inspired Approach to Task Assignment of Swarm Robots in 3-D Dynamic Environments

Intending to mimic the operating mechanism of biological neural systems, a self organizing map-based approach to task assignment of a swarm of robots in 3-D dynamic environments is proposed in this paper. This approach integrates the advantages and characteristics of biological neural systems. It is capable of dynamically planning the paths of a swarm of robots in 3-D environments under uncertain situations, such as when some robots are presented in or broken down or when more than one robot is needed for some special task locations. A Bezier path optimizing algorithm and a parameter adjusting algorithm are integrated in this paper. It is capable of reducing the complexity of the robot navigation control and limiting the number of convergence iterations. The simulation results with different environments demonstrate the effectiveness of the proposed approach.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Multi-AUV Target Search Based on Bioinspired Neurodynamics Model in 3-D Underwater Environments

Target search in 3-D underwater environments is a challenge in multiple autonomous underwater vehicles (multi-AUVs) exploration. This paper focuses on an effective strategy for multi-AUV target search in the 3-D underwater environments with obstacles. First, the Dempster-Shafer theory of evidence is applied to extract information of environment from the sonar data to build a grid map of the underwater environments. Second, a topologically organized bioinspired neurodynamics model based on the grid map is constructed to represent the dynamic environment. The target globally attracts the AUVs through the dynamic neural activity landscape of the model, while the obstacles locally push the AUVs away to avoid collision. Finally, the AUVs plan their search path to the targets autonomously by a steepest gradient descent rule. The proposed algorithm deals with various situations, such as static targets search, dynamic targets search, and one or several AUVs break down in the 3-D underwater environments with obstacles. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve search task of multiple targets with higher efficiency and adaptability compared with other algorithms.

Route Optimization for Autonomous Container Truck Based on Rolling Window

Route optimization for autonomous container truck is one of the key problems to realize the automatic container port. An environment model for container truck is built by grid method. Considering the complex and unknown construction environment of the container port, an improved ant colony optimization (IACO) algorithm based on rolling window is proposed to achieve path planning for container truck. In the simulations, it is obvious that the IACO will not only achieve a safe collision avoidance path, the length of which is shorter than the truck's traditional route no matter how complex the environment is, but also show good analytical and disposing ability of dead ends in the path planning process. Compared with conventional ant colony optimization (ACO), the running time of IACO is shorter. The results of the simulation experiments demonstrate that the IACO is a good method which is applicable to route optimization for autonomous container truck.

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.

The Path Planning of AUV Based on D-S Information Fusion Map Building and Bio-Inspired Neural Network in Unknown Dynamic Environment

In this paper a biologically inspired neural dynamics and map planning based approach are simultaneously proposed for AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance in an unknown dynamic environment. Firstly the readings of an ultrasonic sensor are fused into the map using the D-S (Dempster-Shafer) inference rule and a two-dimensional occupancy grid map is built. Secondly the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation. The AUV path is autonomously generated from the dynamic activity landscape of the neural network and previous AUV location. Finally, simulation results show high quality path optimization and obstacle avoidance behaviour for the AUV.