Improved Bidirectional RRT* Algorithm for Robot Path Planning.

In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.

Optimal control of stochastic system with Fractional Brownian Motion.

In this paper, we introduce a class of stochastic harvesting population system with Fractional Brownian Motion (FBM), which is still unclear when the stochastic noise has the character of memorability. Stochastic optimal control problems with FBM can not be studied using classical methods, because FBM is neither a Markov pocess nor a semi-martingale. When the external environment impact on the system of FBM, the necessary and sufficient conditions for the optimization are offered through the stochastic maximum principle, Hamilton function and ItÔ formula in our work. To illustrate our study, we provide an example to demonstrate the obtained theoretical results, which is the expansion of certainty population system.

An efficient and provably secure key agreement scheme for satellite communication systems.

Satellite communication has played an important part in many different industries because of its advantages of wide coverage, strong disaster tolerance and high flexibility. The security of satellite communication systems has always been the concern of many scholars. Without authentication, user should not obtain his/her required services. Beyond that, the anonymity also needs to be protected during communications. In this study, we design an efficient and provably secure key agreement scheme for satellite communication systems. In each session, we replace user's true identity by a temporary identity, which will be updated for each session, to guarantee the anonymity. Because the only use of lightweight algorithms, our proposed scheme has high performance. Furthermore, the security of the proposed scheme is proved in the real-or-random model and the performance analysis shows that the proposed scheme is more efficient than some other schemes for satellite communication systems.

A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle.

Teaching-learning-based optimization (TLBO) algorithm is a novel heuristic method which simulates the teaching-learning phenomenon of a classroom. However, in the later period of evolution of the TLBO algorithm, the lower exploitation ability and the smaller scope of solutions led to the poor results. To address this issue, this paper proposes a novel version of TLBO that is augmented with error correction strategy and Cauchy distribution (ECTLBO) in which Cauchy distribution is utilized to expand the searching space and error correction to avoid detours to achieve more accurate solutions. The experimental results verify that the ECTLBO algorithm has overall better performance than various versions of TLBO and is very competitive with respect to other nine original intelligence optimization algorithms. Finally, the ECTLBO algorithm is also applied to path planning of unmanned aerial vehicle (UAV), and the promising results show the applicability of the ECTLBO algorithm for problem-solving.