Research on Path Planning in 3D Complex Environments Based on Improved Ant Colony Algorithm

Abstract

Aiming at the problems of complex space, long planning time, and insufficient path security of 3D path planning, an improved ant colony algorithm (TGACO) is proposed, which can be used to solve symmetric and asymmetric path planning problems. Firstly, the 3D array is used to access the environment information, which can record the flight environment and avoid the inefficiency of planning. Secondly, a multi-objective function of distance and angle is established to improve the efficiency and safety of the path. Then, a target-guided heuristic function is proposed, and an anti-deadlock mechanism is introduced to improve the efficiency of the ant colony algorithm. Next, the node pheromone update rules are improved to further improve the efficiency of the algorithm. Finally, experiments prove the effectiveness of the improved algorithm, TGACO, and its efficiency in complex environments has obvious advantages. In the 20 × 20 × 20 environment, compared with the ant colony algorithm (ACO), the improved algorithm (TGACO) in this paper improves the path length, total turning angle, and running time by 17.8%, 78.4%, and 95.3%, respectively.

Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions

“…However, in real-world scenarios, most obstacles do not rise vertically from the ground; their elevation from the ground level typically increases gradually until they reach a point where they are considered "obstacles." Existing research on the A* algorithm in three-dimensional spaces is mostly applicable to drones, which have a high degree of mobility freedom [9][10][11][12] . Drones can ascend, descend, and move in any direction in space.…”

A Multigoal Path-Planning Approach for Explosive Ordnance Disposal Robots Based on Bidirectional Dynamic Weighted-A* and Learn Memory-Swap Sequence PSO Algorithm

“…x e y q = y c +y e 2 (15) When node P and node Q are above or at the bottom of node C, the relationships between the extended node and the waypoints are as shown in Formula (15), where (x e , y e ), (x p , y p ), (x q , y q ), (x c , y c ) are the coordinates of node E, node P, node Q, and node C in the Cartesian coordinate system, respectively. By using the proposed obstacle judgment formula method, we more accurately judge whether the new expansion point is an obstacle or not than is possible with many other methods, such as selecting the sample points in the path to judge whether the sample point is in the obstacle or not.…”

“…The main goal of path planning is to construct a collision-free path that allows the robot to move from the start position to the goal position in a given environment. Over the past few decades, a considerable amount of path planning algorithms have been proposed, such as artificial potential fields (APF) [7], genetic algorithm (GA) [8,9], harmony search algorithm (HSA) [10], A* algorithm [10][11][12], particle swarm optimization (PSO) [13,14], ant colony optimization (ACA) [15], rapidly exploring random tree. (RRT) [16,17], neural networks [18], etc.…”

Three-Dimensional Marine Ranching Cage Inspection Path Planning Integrating the Differential Evolution and Particle Swarm Optimization Algorithms

“…In recent years, many intelligent algorithms have been widely applied to underwater path planning for AUVs. These methods include the ant colony optimization (ACO) algorithm [9][10] [11] [12], tuna algorithm [13], whale algorithm [14][15] [16], grey wolf optimization algorithm [17] [18], artificial jellyfish search algorithm [19], water wave optimization algorithm [20], genetic algorithm (GA) [21] [22], and other methods [23].…”