Research on path planning of cleaning robot based on an improved ant colony algorithm

Abstract

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.

Simulation of Film and Television Transmission Path Based on Ant Colony Optimization Algorithm

“…When the traditional ant colony algorithm constructs the solution, the pseudorandom factor is set as a constant, but the size of the pseudorandom factor often has a certain in uence on the diversity and convergence speed of the algorithm in the early stage of the algorithm. When the pseudorandom factor is small, the ants will choose roulette with a high probability to construct the next solution, which increases the possibility of ants exploring unknown paths, thereby expanding the search space in the early stage and increasing the diversity of understanding, but it reduces the algorithmic complexity [25]. Conversely, when the pseudorandom factor is large, the ants will select the path according to the pseudorandom ratio with a large probability.…”

Optimal Design of Circuit Breaker Based on Flower Pollination Algorithm

“…However, during the Levy flight, the value of the step influence factor is fixed, and there is no adaptiveness in the iterative process, which leads to a small random step size, which reduces the search speed of the algorithm; and when the random step size is too large, it is very It is easy to miss the opportunity to search for the global optimal value. Therefore, in the improvement of the global pollination process of the flower pollination algorithm, an adaptive step size is introduced [12], which affects the step length in Levy's flight, which can improve the convergence speed and achieve a higher solution accuracy. The step size scaling function is shown in formula (4):…”