2023
DOI: 10.1109/tase.2022.3207289
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Dynamic Path Planning and Motion Control of Microrobotic Swarms for Mobile Target Tracking

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Cited by 19 publications
(8 citation statements)
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“…To develop automated control systems, there is considerable enthusiasm for integrating machine learning techniques into the control of biodegradable microrobots, allowing them to execute sophisticated tasks without explicit programming [ 126 , 127 , 128 , 129 , 130 , 131 ]. It is promising to develop more degradation mechanisms that have a wider scope of applications, and developing biodegradable microswarms is a good choice to address this problem [ 132 , 133 ]. The potential benefits of biodegradable microrobots in healthcare make these challenges worth addressing.…”
Section: Discussionmentioning
confidence: 99%
“…To develop automated control systems, there is considerable enthusiasm for integrating machine learning techniques into the control of biodegradable microrobots, allowing them to execute sophisticated tasks without explicit programming [ 126 , 127 , 128 , 129 , 130 , 131 ]. It is promising to develop more degradation mechanisms that have a wider scope of applications, and developing biodegradable microswarms is a good choice to address this problem [ 132 , 133 ]. The potential benefits of biodegradable microrobots in healthcare make these challenges worth addressing.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, through the implementation of machine learning and control loops into the guidance strategies, automated swarm control was realized to precisely adjust the swarm pattern and motion according to environmental changes and task requirements (Figure 9C). [67][68][69][70][71][72]365,366 In the development of automated swarm control, the underlying physical mechanisms of swarms must be taken into account. For instance, when the field parameters are adjusted to change the swarm pattern and moving direction, the modulation and synchronization of the micro/nanoagents require time to reconfigure themselves.…”
Section: Discussionmentioning
confidence: 99%
“…For instance, simulations on swarm generation and motion ,,,,, and swarm decision-making in completing cargo transportation in complex environments , have been performed, which validated the proposed models and elucidated the underlying swarm mechanisms. Moreover, through the implementation of machine learning and control loops into the guidance strategies, automated swarm control was realized to precisely adjust the swarm pattern and motion according to environmental changes and task requirements (Figure C). ,, In the development of automated swarm control, the underlying physical mechanisms of swarms must be taken into account. For instance, when the field parameters are adjusted to change the swarm pattern and moving direction, the modulation and synchronization of the micro/nanoagents require time to reconfigure themselves.…”
Section: Discussionmentioning
confidence: 99%
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“…Third, a real-time control strategy is necessary to achieve synchronous operation of collective patterns in a coordinated manner. An adaptive microswarm with cooperative functions encoded will meet multitasking requirements with superior collective intelligence [ 103 , 104 , 105 , 106 , 107 , 108 ]. As an interdisciplinary research field, colloidal microswarm systems require intense research efforts and close collaboration between different fields, including materials, physics, control, robotics, and biomedical engineering.…”
Section: Discussionmentioning
confidence: 99%