Abstract Rapidly-exploring Randomized Trees (RRT) is a kind of probabilistically complete exploration algorithm based on the tree structure. It has been widely used in the robotic navigation since it guarantees the complete discovery and the exploration of environment maps through robots. In the present study, the RRT algorithm is extended to propose an optimization-based map exploration strategy for multiple robots to actively explore and build environment maps. The present study adopts a market-based task allocation strategy, which to maximize the profit, for the coordination between robots. In the extension of the RRT, the cost function consists the unknown region and the passed unknown region. The unknown region is explored for a given frontier point, while the passed unknown region is the area, where the robot moves towards the target frontier point. When the robot moves from the start position to the target frontier point, the trajectory length is defined as a constraint for the optimization. The main contributions of the present study can be summarized in optimizing the frontier points, defining a new task allocation strategy and applying different evaluation rules, including the running time and the trajectory length. These rules are applied to explore the multi-robot map in simulated and practical environments. Then the Robot Operating System (ROS) is utilized to evaluate the application of the proposed exploration strategy on Turtlebots in a 270 m 2 room. Obtained results from the simulation and the experiment demonstrate that the proposed method outperforms the Umari’s approach from both the running time and the trajectory length aspects.

中文翻译：

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优化框架下的快速探索随机树多机器人地图探索

摘要 快速探索随机树（RRT）是一种基于树结构的概率完备探索算法。它在机器人导航中得到了广泛的应用，因为它保证了机器人对环境地图的完整发现和探索。在本研究中，对 RRT 算法进行了扩展，提出了一种基于优化的地图探索策略，用于多个机器人主动探索和构建环境地图。本研究采用基于市场的任务分配策略，以最大化利润，用于机器人之间的协调。在 RRT 的扩展中，代价函数由未知区域和通过的未知区域组成。对于给定的边界点探索未知区域，而通过的未知区域是区域，机器人向目标边界点移动的位置。当机器人从起始位置移动到目标边界点时，轨迹长度被定义为优化的约束。本研究的主要贡献可以概括为优化边界点、定义新的任务分配策略和应用不同的评估规则，包括运行时间和轨迹长度。这些规则用于在模拟和实际环境中探索多机器人地图。然后利用机器人操作系统 (ROS) 来评估所提出的探索策略在 270 m 2 房间内的 Turtlebots 上的应用。