Research On Evolutionary Navigation Of Mobile Robot Based On Immunity And Multi-instance Learning

Mobile robot technique has shown broader application prospect in many fields, such as space exploration, national defense, industry, agriculture, medicine, and social service etc. And the technique has become a research focus in the academic field of robotics globally. Basing on the techniques of immune evolution and multi-instance learning and focusing on changing environments, large scope environments and unknown environments, this dissertation revolves the localization and path planning problems, which are crucial and need to be solved in the motion of the mobile robot. The main research efforts of this dissertation are about localization and path planning of the mobile robot in large scope environments, changing environments and unknown environments, which include: localization based on multiple images, concurrent mapping and location (CML), path planning, evolutionary computation and immune computation, multi-instance learning, etc.The primary work and the contributions in this dissertation are as follows: By means of analysis of current sampling methods and typical phenomena in the optimization process of multimodal functions, it is discovered that there is a demand of the sampling paradigm to search mainly at the both near sides of the parent and an anti-normal distribution is presented as its implementation. Employing theoretic consequences and experiments, it is proved that the sampling method using the anti-normal distribution has greater ability of escaping from local optima than other probability distributions such as normal distribution, Cauchy distribution, etc. Methods of calculating adaptively probabilities of selection, crossover and mutation operators with the combination of both diversity and fitness are proposed by means of a simple method of machine learning. By means of experiments and analyses, relations of selection pressure and performances of EC are summed up. Employing mathematical reasoning, mathematical relations of mutation probabilities and diversity in binary genetic algorithms are proved which provide theorems and computation methods for adjusting adaptively mutation probabilities with diversity. Under the framework of evolutionary computation (EC), immune clonal evolutionary algorithms are constructed, which integrate the vaccination with heuristic local search and clonal selection with parallel global search. And the global convergences about the algorithms are proved. On the foundation of the algorithms, four algorithms are designed individually in which immune opreators and evolutionary opereators with domain knowledge-based heuristic rules are embedded for tasks of path planning under large environments, changing environments and unknown environments, and for ones of CML under unknown environments. Since these algorithms are combined with domain knowledge-based opreators and the basal algorithm can explore globally, the ability of dealing with corresponding problems by these algorithms has been obviously improved.Combining the inherent characteristics of the multi-instance learning (MIL), supervised-unsupervised MIL neural networks are proposed, in which negative instances are taken as teacher's signs and positive instances in positive bag are clustered with self-organization. The training of the networks will be finished quickly. The predictive accuracy of the networks is high and the networks have the ability of learning multi-concepts. An approach to recognize candidate from multiple targets in an image based on the multi-instance learning (MIL) and a method of mobile robot localization are designed to utilize these natures, further the navigation experiments are finished by the mobile robot. Aiming at problems of mobile robot in large unknown environments, the information of expressing scenes by multiple images is adopted. Further, the method of mobile robot localization is presented and implemented using the ability of automatic discovery of multi-instance learning to identify different scenes. We have realized to extract the hidden features automatically from the multiple images, further realized the mobile robot localization in large and unknown environments by the mobile robot.In a word, by means of immune evolutionary computation with immunity and multi-instance learning, problems of mobile robot localization and path planning in large scope environments, changing environments and unknown environments have been investigated, the thoeries and methods of evolutionary computation, immune evolutionary computation and multi-instance learning have been investigated deeply, the sampling method using anti-normal distribution is presented, strategies of calculating adaptively probabilities of selection, crossover and mutation operators with the combination of both diversity and fitness and the supervised-unsupervised MIL neural networks with self-organization are proposed. The theorems put forward in the dissertation are proved and analyzed on mathematics. The efficiency, reliability and practicability of the methods in this dissertation have been validated for the mobile robot navigation through theoretic analysis, simulations and physical experiments. The research work of this dissertation has offered some new thinkings of settlement for mobile robot navigation in complicated environments. The localization and planning methods proposed have directive significance in theory and reality for mobile robot to carry out the navigation task in complicated environments.