Coverage path planning (CPP) is essential for robotic tasks, such as environmental monitoring and terrain surveying, which require covering all surface areas of interest. As the pioneering approach to CPP, inspired by the concept of predation risk in predator–prey relations, the predator–prey CPP (PPCPP) has the benefit of adaptively covering arbitrary bent 2-D manifolds and can handle unexpected changes in an environment, such as the sudden introduction of dynamic obstacles. However, it can only work in bounded environment and cannot handle tasks in unbounded one, e.g., search and rescue tasks where the search boundary is unknown. Sometimes, robots are required to handle both bounded and unbounded environments, i.e., dual environments, such as capturing criminals in a city. Once encountering a building, the robot enters it to cover the bounded environment, then continues to cover the unbounded one when leaving the building. Therefore, the capability of swarm robots for the coverage tasks both in bounded and unbounded environments is important. In nature, herbivores live in groups to find more food and reduce the risk of predation. Especially the juvenile ones prefer to forage near the herd to protect themselves. Inspired by the foraging behavior of animals in a herd, this article proposes an online adaptive CPP approach that enables swarm robots to handle both bounded and unbounded environments without knowing the environmental information in advance, called dual-environmental herd-foraging-based CPP (DH-CPP). It’s performance is evaluated in dual environments with stationary and dynamic obstacles of different shapes and quantity, and compared with three state-of-the-art approaches. Simulation results demonstrate that it is highly effective to handle dual environments.

中文翻译：

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双环境中基于群体觅食的自适应覆盖路径规划方法

覆盖路径规划（CPP）对于环境监测和地形测量等机器人任务至关重要，这些任务需要覆盖所有感兴趣的表面区域。作为 CPP 的开创性方法，受到捕食者-被捕食者关系中捕食风险概念的启发，捕食者-被捕食者 CPP (PPCPP) 具有自适应覆盖任意弯曲的二维流形的优点，并且可以处理环境中的意外变化，例如由于突然引入动态障碍。然而，它只能工作在有界环境中，无法处理无界环境中的任务，例如搜索边界未知的搜索和救援任务。有时，机器人需要同时处理有界和无界环境，即双重环境，例如在城市中抓捕罪犯。一旦遇到建筑物，机器人就会进入其中覆盖有界环境，然后在离开建筑物时继续覆盖无界环境。因此，群体机器人在有界和无界环境中执行覆盖任务的能力非常重要。在自然界中，食草动物群居是为了寻找更多食物并减少被捕食的风险。尤其是幼年的，更喜欢在牛群附近觅食以保护自己。受群体中动物觅食行为的启发，本文提出了一种在线自适应 CPP 方法，使群体机器人能够在不事先知道环境信息的情况下处理有界和无界环境，称为基于双环境群体觅食的 CPP（DH -CPP）。它的性能在具有不同形状和数量的静态和动态障碍物的双重环境中进行评估，并与三种最先进的方法进行比较。仿真结果表明，该方法对于处理双重环境非常有效。