ACO-A*: Ant Colony Optimization Plus A* for 3-D Traveling in Environments With Dense Obstacles

Abstract

Path planning is one of the most important problems in the development of autonomous underwater vehicles (AUVs). In some common AUV missions, e.g., wreckage search for rescue, an AUV is often required to traverse multiple targets in a complex environment with dense obstacles. In such case, the AUV path planning problem becomes even more challenging. In order to address the problem, this paper develops a two-layer algorithm, namely ACO-A*, by combining the ant colony optimization (ACO) with the A* search. Once a mission with a set of arbitrary targets is assigned, ACO is responsible to determine the traveling order of targets. But, prior to ACO, a cost graph indicating the necessary traveling costs among targets must be quickly established to facilitate traveling order evaluation. For this purpose, a coarse-grained modeling with a representativebased estimation (RBE) strategy is proposed. Following the order obtained by ACO, targets will be traversed one by one and the pairwise path planning to reach each target can be performed during vehicle driving. To deal with the dense obstacles, A* is adopted to plan paths based on a fine-grained modeling and an admissible heuristic function is designed for A* to guarantee its optimality. Experiments on both synthetic and realistic scenarios have been designed to validate the efficiency of the proposed ACO-A*, as well as the effectiveness of RBE and the necessity of A*.

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

“…However, the traditional ACO algorithm has some shortcomings, such as slow convergence speed and easy to fall into the local optimal solution. erefore, the ACO algorithm is often combined with other algorithms (such as A * [63] and PSO [64]) to solve the AUV path planning problem better. Che et al proposed an improved ACO algorithm based on PSO.…”

A Performance Review of Collision-Free Path Planning Algorithms

“…In Table 6, additional information from previous research related to PSO is shown. Ant colony optimization (ACO) is usually studied under varying network environments, such as grid network and Voronoi diagram [107][108][109][110][111][112][113][114][115][116][117][118][119]. Few papers, likewise, researched GA with ACO and coordinate system [120].…”

A Distributed Swarm Optimizer With Adaptive Communication for Large-Scale Optimization

“…L ARGE-SCALE optimization with high dimensionality and high computational cost has become more and more common in many research domains and engineering [1]- [4] in the era of big data [5]. Faced with such difficult problems, traditional population-based metaheuristic algorithms executed in serial would take hours or even days to find the optimum [6].…”