A Green Ant-Based method for Path Planning of Unmanned Ground Vehicles
A Green Ant-Based method for Path Planning of Unmanned Ground Vehicles
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DOI：
10.1109/access.2017.2656999
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发表时间：
2017-01
期刊：
影响因子：
3.9
通讯作者：
M. Jabbarpour;H. Zarrabi;Jason J. Jung;Pankoo Kim
中科院分区：
文献类型：
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作者：
M. Jabbarpour;H. Zarrabi;Jason J. Jung;Pankoo Kim
无人地面车辆(UGV)的正常运行需要规划最优/最短路径。虽然现有的大多数方法都提供了适当的路径规划策略，但它们不能保证减少UGV的能量消耗，而UGV是通过车载电池在有限功率下提供的。因此，本文提出了一种新的基于蚂蚁的路径规划方法，在其规划策略中考虑了UGV的能量消耗。这种方法被称为绿色蚂蚁(G-Ant)，它将基于蚂蚁的算法与功率/能量消耗预测模型相结合，以达到其主要目标：提供一条低功耗、无冲突的最短路径。通过仿真工具对G-Ant算法进行了评估和验证。并与蚁群算法、遗传算法和粒子群算法进行了性能比较。通过模拟不同的场景，在不同的迭代次数、不同的障碍物数量和不同的种群规模下，从UGV旅行时间、旅行长度、计算时间等方面对G-Ant算法的性能进行了评估。实验结果表明，G-Ant算法在行程长度和迭代次数方面均优于已有的算法。
Planning of optimal/shortest path is required for proper operation of unmanned ground vehicles (UGVs). Although most of the existing approaches provide proper path planning strategy, they cannot guarantee reduction of consumed energy by UGVs, which is provided via onboard battery with constraint power. Hence, in this paper, a new ant-based path planning approach that considers UGV energy consumption in its planning strategy is proposed. This method is called Green Ant (G-Ant) and integrates an ant-based algorithm with a power/energy consumption prediction model to reach its main goal, which is providing a collision-free shortest path with low power consumption. G-Ant is evaluated and validated via simulation tools. Its performance is compared with ant colony optimization, genetic algorithm, and particle swarm optimization approaches. Various scenarios were simulated to evaluate G-Ant performance in terms of UGV travel time, travel length, computational time by taking into account different numbers of iterations, different numbers of obstacle, and different population sizes. The obtained results show that the G-Ant outperforms the existing methods in terms of travel length and number of iteration.