In order to improve the use of roads and solve the huge pressure brought by the influx of a large number of private cars on the existing traffic and transportation network due to the development of the economy and the improvement of people's living standards1 , thus leading to more and more large and medium-sized city roads facing traffic congestion and increasingly serious status quo, to achieve the purpose of reducing traffic congestion, this paper uses an intelligent algorithm - this paper uses an intelligent algorithm - the seagull optimisation algorithm - to optimise the driving route of in-car navigation, so as to achieve the shortest possible vehicle driving time and driving path, reduce traffic pressure and improve the current situation of traffic congestion.
In the current climate of traffic congestion and frequent traffic accidents due to increasing pressure on transport, the birth and development of driverless cars is particularly important. However, as the technologies of unmanned vehicles are not yet perfect, it becomes more practical to carry goods at low speed - unmanned delivery - than to carry people at high speed. In order to achieve the goal of making full use of the existing road resources and improving the efficiency of distribution in logistics, this paper uses an intelligent algorithm - the Pelican algorithm - to optimise the cost of the unmanned vehicle and solve for its minimum cost.
For node localization in wireless sensor networks, an optimization algorithm based on semidefinite programming is proposed. For the distance matrix with noise, the low rank matrix is restored by using the automatic outlier identification algorithm based on semidefinite programming. Then, based on the restored distance matrix, two new multi-source node location algorithms based on semidefinite programming, SDP-RLS and SDP-SRLS, are proposed, which effectively transform the non-convex nonlinear location problem into a convex optimal linear location problem. Simulation results show that the proposed algorithm based on SDP has stronger robustness and higher positioning accuracy.
KEYWORDS: Evolutionary algorithms, Detection and tracking algorithms, Computer simulations, Control systems, Telecommunications, Space robots, Process control, Mobile robots, Algorithms
In the field of robotics, mobile robots have always been a hot topic of research. Aiming at the problem of mobile robot formation, multi-robot collaborative control technology is adopted, and the leader-follower algorithm is realized through the design of the leader and follower roles, and the multiple robot formation control is realized, and the dynamic window algorithm is integrated to achieve the formation to avoid obstacles. First, the leader-follower algorithm is introduced and theoretically derived. Secondly, the dynamic window algorithm added in the formation collaborative control process is introduced and theoretically derived. Finally, through the simulation of cooperative control of formations, the dynamic window algorithm and the artificial potential field algorithm were used for simulation and comparison of the formation travel process and the path trajectory traveled by the final formation, respectively.
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