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Understanding Digital Transformation
Published in Antonio Sartal, Diego Carou, J. Paulo Davim, Enabling Technologies for the Successful Deployment of Industry 4.0, 2020
Swarm robotics is the study of how to design groups of distributed robots that operate without relying on any external infrastructure or on any form of centralized control. A collective behaviour (Garnier et al., 2005) emerges from the interactions between the robots and interactions of robots with the environment in which they are operating. Swarm intelligence and biological studies of insects, ants, bees and others are the main source of swarm behaviour. In swarm robotics, automatic design has been mostly performed using the evolutionary robotics approach (Nolfi and Floreano, 2000). Evolutionary robotics has been used to develop several collective behaviours including collective transport (Groß and Dorigo, 2008) and development of communication networks (Huaert et al., 2008). A swarm robot team is fault tolerant, scalable and flexible. The robots in a swarm environment are able to perform different activities concurrently. More importantly, swarm robotics promotes the development of systems that are able to cope well with the failure of one or more of their constituent robots. That is to say that the failure of an individual robots does not imply the failure of the whole swarm (Fault tolerance) as the swarm does not rely on any centralized control entity, leaders or any individual robot playing a predefined role.
A Playbook-Based Interface for Human Control of Swarms
Published in Mustapha Mouloua, Peter A. Hancock, James Ferraro, Human Performance in Automated and Autonomous Systems, 2019
Phillip M. Walker, Christopher A. Miller, Joseph B. Mueller, Katia Sycara, Michael Lewis
The origins of swarm robotics can be found in early work investigating collectives of swarms in nature, such as that by Couzin and Krause (2003), which in turn draws inspiration from the established field of self-organization in biological systems (see Camazine et al., 2003 and Maini & Othmer, 2001 for examples). However, in the past decade, swarm robotics has become an engineering discipline in its own right (Barca & Sekercioglu, 2013; Parasuraman, Galster, Squire, Furukawa, & Miller, 2005), with numerous researchers focused on improving the hardware and algorithms that support swarms. Swarm robotics promises a wide range of benefits and applications, from improving coverage during monitoring and reconnaissance missions (Clark & Fierro, 2005) to improving tracking and search and rescue of multiple targets (Stormont, 2005). NASA also investigated the use of robot swarms in space for future missions as early as 2004 (Truszkowski, Hinchey, Rash, & Rouff, 2004).
Industry 5.0
Published in Roshani Raut, Salah-ddine Krit, Prasenjit Chatterjee, Machine Vision for Industry 4.0, 2022
Mahadi Hasan Miraz, Mohammad Tariq Hasan, Farhana Rahman Sumi, Shumi Sarkar, Mohammad Amzad Hossain
Swarm robotics is a coordination method and a system consisting of many physical robots (Banjanovic´-Mehmedovic´ & Mehmedovic´, 2020; Bousdekis, Apostolou, & Mentzas, 2020). IT is expected to emerge as the desired collective action from the interactions between robots’ interactions with the environment. The cooperative behavior of artificial, autonomous and self-organised systems is Swarm Intelligence (SI). The concept used for industry in artificial intelligence 5.0 (Chen, 2017; Clark et al., 2020).
Multiobjective coordinated search algorithm for swarm of UAVs based on 3D-simplified virtual forced model
Published in International Journal of Systems Science, 2020
Xinjie He, Shaowu Zhou, Hongqiang Zhang, Lianghong Wu, You Zhou, Yujuan He, Mao Wang
The methods for designing collaborative mechanisms of swarm robotics can be divided into two categories: behaviour-based design (Bahgeçi & Sahin, 2005) and automatic design (Francesca & Birattari, 2016). Essentially, the design methods of current strategies for multiobjective search tasks can be classified into behaviour-based categories, such as glowworm swarm optimisation (Chen et al., 2019) particle swarm optimisation (Xue et al., 2011), beetle antennae search (Khan et al., 2019; Q. Wu et al., 2019) and bat optimisation (Tang et al., 2020).
MONOLITh: a soft non-pneumatic foam robot with a functional mesh skin for use in delicate environments
Published in Advanced Robotics, 2022
Anthony E. Scibelli, Cassandra M. Donatelli, Ben K. Tidswell, Micah R. Payton, Eric D. Tytell, Barry A. Trimmer
We believe a device of this type would withstand drop impacts and crushing or puncturing damage. In addition to damage resilience, it is possible to carry a payload of sensors or lifesaving supplies to otherwise inaccessible areas. Due to the low cost and simplicity of these designs, they are well suited to distributing communication and sensor components, utilizing established techniques from swarm robotics.