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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.
This paper is organized as follows: In Section 2, we provide a general introduction of BIAs and give out a classification of these BIAs. Section 3 analyzes and summarizes some typical BIAs. The main applications of BIAs in mobile robot control are introduced in Section 4. Section 5 discusses the future research directions for the theory of BIAs and their applications in mobile robot control. Finally, conclusions are given out in Section 6.
Selfish Gene Algorithm is a new member of BIAs, which is based on the selfish gene theory presented by Dawkins . In the selfish gene theory, the evolution is suggested to be viewed as acting at gene level. The selection in organisms or populations is based on genes. In addition, the population is seen as a genes pool where the number of individuals and their specific identities are not of interest. So Selfish Gene Algorithm focuses on the fitness of genes rather than individuals. The main concepts in Selfish Gene Algorithm are summarized as follows [43, 44] and the pseudocode of Selfish Gene Algorithm is summarized in Algorithm 1.
Selfish Gene Algorithm has been successfully tested in several problems. For example, António  proposed a strategy based on the hybridization of Memetic Algorithm and Selfish Gene Algorithm, to overcome the difficulties in attaining a global solution for the optimization design problem of composite structures. Wang et al.  exploited the selfish gene theory in the approach to improve the performance of the bivariate estimation of distribution algorithms.
Yang and Meng  used a bioinspired neural network to realize the dynamic collision-free trajectory generation for mobile robot in a nonstationary environment. This bioinspired neural network is based on a shunting model, which is obtained from a computational membrane model for a patch of membrane in a biological neural system (proposed by Hodgkin and Huxley in 1952 ). The core idea of this bioinspired neural network based robot trajectory generation method is that the neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation:where is the neural activity (membrane potential) of the th neuron; , , and are nonnegative constants, representing the passive decay rate and the upper and lower bounds of the neural activity, respectively; and and are the excitatory and inhibitory inputs to the neuron. The excitatory input results from the target and the lateral connections from its neighboring neurons, while the inhibitory input results from obstacles only. Thus the differential equation for the th neuron is given bywhere is the number of neural connections of the th neuron to its neighboring neurons within a receptive field; is the lateral connection weight from the th neuron to the th neuron; function is defined as , and function is defined as . is the external input to the th neuron, which is defined aswhere is a positive constant and . The dynamic activity landscape of the topologically organized neural network is used to determine the next robot location:where , is the activity of all the neighboring neurons of the present neuron (the present location of the robot); is the location of the neuron with the maximum activity in these neurons (the next possible locations of the robot). One of the path planning results in a U-shaped environment based on this bioinspired neural network is shown in Figure 5, where the generated path is shown in Figure 5(a), while the neural activity landscape in 3D is shown in Figure 5(b).
Villacorta-Atienza et al.  proposed an internal representation neural network (IRNN), which can create compact internal representations (CIRs) of dynamic situations, describing the behavior of a mobile agent in an environment with moving obstacles. Emergence of a CIR in IRNN can be viewed as a result of virtual exploration of the environment. The general architecture of this IRNN is shown in Figure 6, which consists of two coupled subnetworks: Trajectory Modeling RNN (TM-RNN) and Causal Neural Network (CNN). The output of TM-RNN is time independent, and hence it just maps the immobile objects into CNN whose dynamics model the process of virtual explorationwhere is the neuron state variable, representing the concentration of virtual agents at the cell ; the time derivative is taken with respect to the mental (inner) time ; denotes the discrete Laplace operator describing the local (nearest neighbor) interneuronal coupling, whose strength is controlled by (a constant number); and accounts for the target; if is occupied by a target, ; otherwise . In their work, the effectiveness of IRNN is proved by some tests in different simulated environments, including the environment with a single moving obstacle and the realistic environments. 2b1af7f3a8