The cover image is based on the Research Article A global path planning algorithm based on the feature map by Peng Liu et al., https://doi.org/10.1049/csy2.12040.

This study researches the ?∞ control issue for a networked Markovian jump system with data packet loss occurring not only in the channel from sensor to controller but also in the channel from controller to actuator via an observer. The mathematical model for the closed-loop networked Markovian jump system with data packet loss is established. The necessary and sufficient conditions for the closed-loop system to be stochastically stable are derived. The design approach of the controller and the minimal performance index of the external disturbance suppression are also given in the case that the transition possibilities of the system modes and the data packet loss are both partially unavailable. Finally, two numerical examples are used to illustrate the effectiveness of the proposed method.

The feature map is a characteristic of high computational efficiency, but it is seldom used in path planning due to its lack of expression of environmental details. To solve this problem, a global path planning algorithm based on the feature map is proposed based on the directionality of line segment features. First, the robot searches the path along the direction of the target position but turns to search in the direction parallel to the obstacle, which it approaches until the line between the robot and the target position does not intersect with obstacles. Then it turns to the target position, keep searching the path. Meanwhile, the problems of the direction selection of turning point, corner point and obstacle circumvention in the searching process are analysed and corresponding solutions are put forth. Finally, a path optimisation algorithm with variable parameters is proposed, making the optimised path shorter and smoother. Simulation experiments demonstrate that the proposed algorithm is superior to A* algorithm in terms of computation time and path length, especially of the computation efficiency.

The existing attribute reductions are carried out using equivalence relations under a complete information system, and there is less research on attribute reductions of incomplete information systems with new theoretical models such as multi-granularity decision rough sets. To address the above shortcomings, this paper first makes up a pessimistic-optimistic multi-granularity decision rough set model based on tolerance relations in incomplete information systems. The concepts of attribute importance and approximate distribution quality are introduced into the model to form an attribute reduction algorithm under incomplete information systems. Secondly, due to the NP-hard problem of attribute reduction, in order to further ensure the accuracy of the reduction result, this paper proposes a pessimistic-optimistic multi-granularity reduction algorithm under quantum particle swarm optimization. Experimental results on multiple-attribute data proved that the algorithm proposed in this paper can effectively attribute reduction in the decision table with missing data. At the same time, the algorithm of this paper has the role of iterative optimization search, ensuring the accuracy of the reduction results and increasing the applicability of multi-granularity decision rough sets.

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change, target deformation, scale change, motion blur, and other factors. More specifically, a target tracking algorithm, called re-detection multi-feature fusion, is proposed based on the fusion of scale-adaptive kernel correlation filtering and re-detection. The target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients, colour name, and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked target. In order to deal with the problem that the target is occluded and disappears in the tracking process, a random fern classifier is trained to perform re-detection when the target is occluded. After comparing the OTB-50 target tracking dataset, the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.

This study investigates the integral line of sight (ILOS) path-following control problem of surface vehicles whose dynamics feature external disturbances, model uncertainties, and actuator dead zones. First, introducing the ILOS guidance law, the path-following control problem is converted to stabilising a 2nd order nonlinear system. Subsequently, the fixed-time observer is designed to estimate and compensate for the uncertainties and unknown external disturbances while overcoming the actuator dead zones. Finally, the trajectory tracking control strategy is designed based on the fixed-time observer. Numerical examples are given to illustrate the effectiveness of the proposed approaches.

Mixed targets are composed of point targets, extended targets, and group targets. The point target can produce one measurement at most, the extended target and the group target can produce multiple measurements, but the sub-goals of the group target have a certain dependency relationship. At this time, the estimated fusion of the group target is converted to the estimated fusion of sub-targets with formation motion structure, and the distance among the sub-targets is very close, which brings difficulties to the estimated fusion of mixed targets. This paper combines the adjacency matrix in graph theory to dynamically model the discernible group target and introduces the concept of deformation. Also, it uses the finite mixture model method to dynamically model the extended target. Then the Gibbs-GLMB algorithm is used to estimate the state and number of the mixed targets. A dynamic detection federated filter fusion algorithm is proposed to fuse the mixed targets state estimates. The effectiveness of the algorithm is verified in the final simulation.