SUMMARYThis paper provides a mathematical approach to study the duration of collision-free motion of multiple unmanned autonomous vehicles (UXVs) operating in confined areas. A simple geometric model of the UXVs is first proposed, and the dynamics of the model is shown. The expected time of first collision is then formulated using the concept of mean free path from molecular dynamics. Monte-Carlo simulation is performed to verify the theory developed. The expected time of first collision is a function of the number of UXVs, the UXV speed and the sensor field of view (FOV) for a given operational area and vehicle size. Furthermore, the critical number of UXVs, above which collision can be deemed to occur instantly, is obtained.
The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.
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