The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced.

The iterative closest contour point (ICCP) matching algorithm has become more and more widely used in the underwater geomagnetic aided inertial navigation system (INS). In practical application, the traditional ICCP algorithm is sensitive to the initial positioning error of the INS and can only do rigid transformation for the INS track of the vehicle. Particularly when there exists scale error, the accuracy and stability of the traditional ICCP algorithm will be affected. To solve this problem, an improved algorithm based on affine transformation is proposed. Firstly, the fundamental of the ICCP is analyzed in detail, and an error analysis of the geomagnetic aided inertial navigation system is carried out, and then the rigid transformation is replaced with affine transformation to improve the performance of the ICCP. In contrast to the conventional approach, the proposed algorithm can solve the rotation, translation, and scaling parameters of the indicated track and the matching track, so it can significantly reduce the interference of the scale error. Experimental results confirm the effectiveness of the proposed algorithm.

Geomagnetic aided inertial navigation is a way to use the geophysical field for navigation. It can locate the carrier position by the correlation between geomagnetic data and running track. It is an effective mean to realize autonomous navigation. Matching area suitability is one of the important factors affecting geomagnetic aided inertial navigation. Through the suitability analysis of matching areas, the areas with obvious geomagnetic features and rich information are selected as matching areas, which can effectively improve the real‐time and accuracy of geomagnetic aided navigation. However, matching area suitability analysis for geomagnetic aided navigation is a complex process and needs to consider diverse factors, based on which a decision may be made. The area suitability analysis inherently can be considered as a multicriterion decision analysis (MCDA) problem. This paper presented a novel comprehensive model combining principal component analysis (PCA) and analytical hierarchy process (AHP) to evaluate the suitability of the navigation matching area. Firstly, according to the features of the areas, key feature parameters and the corresponding weights are determined by PCA and AHP, respectively. Then comprehensive evaluation values of the navigation matching areas are calculated through the comprehensive model. Finally, experiments were implemented in Bohai Bay; the correlation‐matching algorithm is applied to verify the validity of the model in the areas. The experiment results well indicate the consistency between the comprehensive evaluation value and the matching area suitability. It is reasonable to regard the comprehensive evaluation value as a basis for area suitability analysis.

ITM Web of Conferences, open-access proceedings in information technology, computer science and mathematics