A cost-aware path planning algorithm for mobile robots
A cost-aware path planning algorithm for mobile robots
复制标题
移动机器人的成本感知路径规划算法
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Songhwai Oh
中科院分区：
文献类型：
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作者：
Junghun Suh;Songhwai Oh
在本文中，我们提出了一个成本敏感的路径规划算法的移动的机器人。当机器人从一个位置移动到另一个位置时，机器人会受到其当前位置的成本的惩罚。机器人的总成本由机器人在成本图上的轨迹确定。所提出的成本感知路径规划算法的目标是找到具有最小成本的轨迹。场的成本图可以表示环境参数，例如温度、湿度、化学浓度、无线信号强度和隐蔽性。例如，如果成本图表示不同位置处的丢包率，则两个位置之间的最小成本路径是具有最佳可能通信的路径，这在机器人在具有弱无线信号的环境下操作时是期望的。提出的代价感知路径规划算法扩展了快速探索随机树（RRT）算法，通过应用交叉熵（CE）方法扩展运动段。我们表明，该算法找到了一条接近最优成本路径的路径，并通过大量的仿真，给出了一个突出的性能相比，RRT和CE为基础的路径规划方法。
In this paper, we propose a cost-aware path planning algorithm for mobile robots. As a robot moves from one location to another, the robot is penalized by the cost at its current location. The overall cost of the robot is determined by the trajectory of the robot over the cost map. The goal of the proposed cost-aware path planning algorithm is to find the trajectory with the minimal cost. The cost map of a field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware path planning algorithm extends the rapidly-exploring random tree (RRT) algorithm by applying the cross entropy (CE) method for extending motion segments. We show that the proposed algorithm finds a path which is close to the near-optimal cost path and gives an outstanding performance compared to RRT and CE-based path planning methods through extensive simulation.