Field Complete Coverage Path Planning Based on Improved Genetic Algorithm for Transplanting Robot

Abstract

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design

“…Many agricultural machines have been designed for improving agricultural tasks replacing manual operations [2]. The fruit-harvesting robot (FHR) is an important automated machine for improving the productivity of agricultural produce.…”

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

“…The advantage of this crossover and mutation operation is that it maximizes lization of information obtained from the population to guide individuals towa optimal direction of evolution, making the genetic operators more efficient and avoid local convergence. This is the biggest difference from traditional genetic algo Frequency encoding uses partial mapping crossover operation [44] and the mult mutation operation [45] to increase the diversity of the population, ensuring that e route considers the appropriate departure frequency to reduce operating cost meeting customer needs.…”

An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

“…This selection process is facilitated by the utilization of an evaluation function. In the context of the traditional dynamic window approach (DWA) algorithm, the evaluation function is mathematically represented by Equation (9).…”

“…Currently, numerous AGV path-planning algorithms cater to diverse environments. Common static global path planning algorithms encompass the A* algorithm [3,4], Dijkstra's algorithm [5,6], ant colony algorithm [7,8], genetic algorithm [9], and particle swarm optimization algorithm [10,11]. Meanwhile, local path planning algorithms comprise the dynamic window approach (DWA) [12,13], the artificial potential field method [14,15], and the time elastic band (TEB) algorithm [16].…”