Swarm foraging is a common test case application for multi-robot systems. In this paper RepAtt algorithm is used for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. This is a chemotaxis-inspired search behaviour where robots use the temporal gradients of these signals to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication and vision system. We then show through extensive simulation studies that RepAtt significantly improves swarm foraging time and robot efficiency under realistic communication and vision models. Note to Practitioners—This research developed a swarm foraging algorithm that takes into consideration the vision and communication sensing noise levels faced by robots in real world applications. The algorithm, known as RepAtt, was developed with the aim of emphasizing algorithmic simplicity and limiting the hardware requirements for the robots in the swarm. In this paper, we have focused on the problem of deploying swarm robots to forage litter in an environment such as a park. The communication model of the robots was based on the physics of sound, while their vision system was modelled using experiments with deep neural networks based object detectors. The results show that the RepAtt algorithm is robust to different distributions of targets (or litter) in the search space, exhibits good swarm efficiency with changes in swarm population and is robust to noise in its communication and vision systems. Apart from the RepAtt algorithm, other contributions made by this research include modelling of robot vision system to aid extensive study of the impact of communication and vision noise on swarm coordination. This will be relevant for extensive testing and validation before deployment to swarm robots hardware. The sound communication used in this research limits the kinds of environment the robots can be deployed in. Echoes within an enclosed environment and bandwidth limitation for communication frequency and public disturbance due to sound emitted by the robots can all contribute to this limitation. Thus, this research can be improved by investing in the development of a communication technology with similar physics. Other areas of improvement include adopting better obstacle avoidance algorithms and implementing suitable manipulators for handling litter objects. The algorithm can be extended to make it applicable for solving other problems such as search and rescue operations where foraging targets could be disaster survivors; demining and hazardous waste cleanup, where targets are the mines or waste material; and planetary exploration, where targets could be interesting features of the planets are the targets searched for by the robots.

中文翻译：

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通信和视觉不确定性下的群体觅食

群体觅食是多机器人系统的常见测试用例应用程序。在本文中，RepAtt 算法用于通过选择性地广播排斥和吸引信号来提高机器人群的协调性。这是一种受趋化性启发的搜索行为，机器人使用这些信号的时间梯度导航到更有利的区域。硬件实验用于模拟和验证逼真的、嘈杂的声音通信和视觉系统。然后，我们通过广泛的模拟研究表明，RepAtt 在现实通信和视觉模型下显着提高了群体觅食时间和机器人效率。从业者须知—该研究开发了一种群体觅食算法，该算法考虑了机器人在现实世界应用中面临的视觉和通信感知噪声水平。该算法被称为 RepAtt，旨在强调算法的简单性并限制集群中机器人的硬件要求。在本文中，我们专注于部署群体机器人在公园等环境中觅食垃圾的问题。机器人的通信模型基于声音的物理学，而它们的视觉系统则使用基于深度神经网络的对象检测器的实验进行建模。结果表明，RepAtt 算法对搜索空间中目标（或垃圾）的不同分布具有鲁棒性，随着群体数量的变化表现出良好的群体效率，并且在其通信和视觉系统中对噪声具有鲁棒性。除了 RepAtt 算法，这项研究的其他贡献包括机器人视觉系统的建模，以帮助广泛研究通信和视觉噪声对群体协调的影响。这将与部署到集群机器人硬件之前的广泛测试和验证相关。本研究中使用的声音通信限制了机器人可以部署的环境类型。封闭环境中的回声和通信频率的带宽限制以及机器人发出的声音引起的公共干扰都会造成这种限制。因此，可以通过投资开发具有类似物理特性的通信技术来改进这项研究。其他改进领域包括采用更好的避障算法和实施合适的机械手来处理垃圾。该算法可以扩展，使其适用于解决其他问题，例如搜索和救援行动，其中觅食目标可能是灾难幸存者；排雷和危险废物清理，目标是地雷或废料；和行星探索，其中目标可能是行星的有趣特征，是机器人搜索的目标。排雷和危险废物清理，目标是地雷或废料；和行星探索，其中目标可能是行星的有趣特征，是机器人搜索的目标。排雷和危险废物清理，目标是地雷或废料；和行星探索，其中目标可能是行星的有趣特征，是机器人搜索的目标。