In this paper, an optical multiple image encryption method based on ellipsometry reconstruction is proposed. In our proposal, the cryptosystem is designed as multiple paths, and each path is used to encrypt one image. In each path, one original image is decomposed into two phase only masks (POMs) using the interference method. Then, every two masks decomposed from the same original image are encoded onto the spatial light modulator (SLM) to generate an elliptically polarized light (EPL) with specific amplitude and polarization. After that, each light passes through a linear polarizer. CCD is used to record the sum of the intensity of these path outputs, which is ciphertext. Jones matrix is used to describe these procedures. Moreover, our proposed encryption system has certain anti-attack ability. Computer simulations are conducted to verify the correctness and validity of our proposed method.

Aiming to address problems such as low sampling success rate and long computation time in the motion planning of a dual-arm cooperative system with multiple constraints, this paper proposes an Informed-Bi-Quick RRT* algorithm based on offline sampling. First, in the process of pre-sampling, the new algorithm relaxes the approximation of constrained manifolds by introducing the idea of incremental construction, and it incorporates the stochastic gradient descent method to replace global random sampling with local random sampling, which enriches the data set and shortens the offline sampling time of the data set. Second, the new algorithm improves the original Quick-RRT* algorithm by combining the two-tree idea and the multi-target bias expansion strategy, and it improves the adaptability of the algorithm to different obstacle environments. In addition, the loosely constrained motion and tightly constrained motion in the two-arm cooperative system are analyzed, and the adaptive planning of the two-arm trajectory in different motions is described in detail. In this paper, the two-arm cooperative model constructed with UR5 and UR10 robot arms is studied, and the ability of the proposed algorithm to deal with multiple constraints is verified by simulating assembly and handling tasks. The experimental results show that compared with other methods, the proposed algorithm has obvious advantages in path quality and planning efficiency.