A Deterministic Annealing Neural Network Algorithm for the Minimum Concave Cost Transportation Problem

Robot path planning based on artificial potential field with deterministic annealing

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot's obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination. The initial path is optimised using an annealing algorithm to enhance the overall performance. The time, length and success rate of the planned path measures the quality of the solution. Simulation results and comparative experiments demonstrate that the proposed algorithm can solve path planning in different environments. The proposed algorithm is suitable for complex environments with convex or non-convex polygon obstacles.

Aircraft route recovery based on distributed integer programming method

In order to further promote the application and development of unmanned aviation in the manned field, and reduce the difficulty that airlines cannot avoid due to unexpected factors such as bad weather, aircraft failure, and so on, the problem of restoring aircraft routes has been studied. To reduce the economic losses caused by flight interruption, this paper divides the repair problem of aircraft operation plans into two sub problems, namely, the generation of flight routes and the reallocation of aircraft. Firstly, the existing fixed-point iteration method proposed by Dang is used to solve the feasible route generation model based on integer programming. To calculate quickly and efficiently, a segmentation method that divides the solution space into mutually independent segments is proposed as the premise of distributed computing. The feasible route is then allocated to the available aircraft to repair the flight plan. The experimental results of two examples of aircraft fault grounding and airport closure show that the method proposed in this paper is effective for aircraft route restoration.

Robot Static Path Planning Method Based on Deterministic Annealing

Heuristic calculation is an essential method to solve optimisation problems. However, its vast computing requirements limit its real-time and online applications, especially in embedded systems with limited computing resources, such as mobile robots. This paper presents a robot path planning algorithm called DA-APF based on deterministic annealing. It is derived from the artificial potential field and can effectively solve the local minimum problem of the model established by the potential field method. The calculation performance of DA-APF is considerably improved by introducing temperature parameters to enhance the potential field function and by using annealing and tempering methods. Moreover, an optimal or near-optimal robot path planning scheme is given. A comprehensive case study is performed using heuristic methods, such as genetic algorithm and simulated annealing. Simulation results show that DA-APF performs well in various static path planning environments.

A scoping review on monitoring mental health using smart wearable devices

With the continuous development of the times, social competition is becoming increasingly fierce, people are facing enormous pressure and mental health problems have become common. Long-term and persistent mental health problems can lead to severe mental disorders and even death in individuals. The real-time and accurate prediction of individual mental health has become an effective method to prevent the occurrence of mental health disorders. In recent years, smart wearable devices have been widely used for monitoring mental health and have played an important role. This paper provides a comprehensive review of the application fields, application mechanisms, common signals, common techniques and results of smart wearable devices for the detection of mental health problems, aiming to achieve more efficient and accurate prediction for individual mental health, and to achieve early identification, early prevention and early intervention to provide a reference for improving the level of individual mental health.

Sliding-mode variable structure control for complex automatic systems: a survey

Automatic systems (ASs) can automatically control the work of controlled objects without unattended participation. They have been extensively used in industry, agriculture, automobiles, robots and other fields in recent years. However, the performance of the controller cannot meet the work requirements under complex environmental conditions. Therefore, improving the control performance is one of the difficult problems that automated systems should solve. Sliding-mode variable structure control has the advantages of fast response, insensitivity to uncertainty and interference and easy implementation; thus, it has been extensively used in the field of complex control systems. This article analyses and explains the research status of motors, microgrids, switched systems, aviation guidance, robots, mechanical systems, automobiles and unmanned aerial vehicles (UAVs) and prospects for the application of sliding-mode variable structure control in complex ASs.

Gradient Descent-Based Adaptive Learning Control for Autonomous Underwater Vehicles With Unknown Uncertainties

This article investigates the adaptive learning control problem for a class of nonlinear autonomous underwater vehicles (AUVs) with unknown uncertainties. The unknown nonlinear functions in the AUVs are approximated by radial basis function neural networks (RBFNNs), in which the weight updating laws are designed via gradient descent algorithm. The proposed gradient descent-based control scheme guarantees the semiglobal uniform ultimate boundedness (SUUB) of the system and the fast convergence of the weight updating laws. In order to reduce the computational burden during the backstepping control design process, the command-filter-based design technique is incorporated into the adaptive learning control strategy. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.