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... central controller makes this decision based on parameters set by a user. Figure 1 schematically illustrates of our framework. ...
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... central controller makes this decision based on parameters set by a user. Figure 1 gives an illustration of our framework. ...
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... new configuration is good enough to compensate for the loss and the resilient coordination is done (Algorithm 2, lines 4-5). Otherwise, the central controller solves the MILP (Problem 4) to select a new robot set from the robot pool (Algorithm 2, line 7), inserts the new robot set into L-neighborhood of the failed robot (Algorithm 2, line 8), redoes the local repositioning (Algorithm 2, line 9) and obtains the configuration for the new team (Algorithm 2, line 10). ...

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Citations

... There has been significant work focusing on making individual robots resilient [33,34]. Recently, there is a trend on investigating resilience in multi-robot teams, often grounded in tasks such as formation control [14,18,35,36,37,38,39,40,41,42,43,44], wireless communication [13,45], state estimation [21,46,47,48], data collection [4,15,49,50,51], attack-defense games [52,53,54], and adaptive reconfiguration [19,20,55,56]. A summary of these applications is found in Table 1. ...
... In addition to designing coordination algorithms that withstand attacks or failures [4,50,51,69], we also need resilient reconfiguration approach that enables robot teams to adaptively recover after attacks or faults [19,20,55,56]. Ramachandran et al. studied the problem of maintaining resource availability in a network of multiple robots [19] in such conditions. ...
... Then this resilient reconfiguration framework was utilized for maintaining sensing quality for robots to track targets [55]. Later, a resilient multi-robot coverage framework was designed in [56] where well-functioning robots adaptively reposition themselves to maintain a good team coverage performance once a robot in the team fails. Further, to completely cover or explore an environment by a team of robots, Song et al. presented a distributed eventdriven replanning algorithm to adaptively assign tasks to compensate for the team loss induced by robot failures [70]. ...
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Deploying a team of robots that can carefully coordinate their actions can make the entire system robust to individual failures. In this report, we review recent algorithmic development in making multi-robot systems robust to environmental uncertainties, failures, and adversarial attacks. We find the following three trends in the recent research in the area of multi-robot coordination: (1) resilient coordination to either withstand failures and/or attack or recover from failures/attacks; (2) risk-aware coordination to manage the trade-off risk and reward, where the risk stems due to environmental uncertainty; (3) Graph Neural Networks based coordination to learn decentralized multi-robot coordination policies. These algorithms have been applied to tasks such as formation control, task assignment and scheduling, search and planning, and informative data collection. In order for multi-robot systems to become practical, we need coordination algorithms that can scale to large teams of robots dealing with dynamically changing, failure-prone, contested, and uncertain environments. There has been significant recent research on multi-robot coordination that has contributed resilient and risk-aware algorithms to deal with these issues and reduce the gap between theory and practice. Learning-based approaches have been seen to be promising, especially since they can learn who, when, and how to communicate for effective coordination. However, these algorithms have also been shown to be vulnerable to adversarial attacks, and as such developing learning-based coordination strategies that are resilient to such attacks and robust to uncertainties is an important open area of research.
... There has been significant work focusing on making individual robots resilient [33,34]. Recently, there is a trend on investigating resilience in multi-robot teams, often grounded in tasks such as formation control [14,18,35,36,37,38,39,40,41,42,43,44], wireless communication [13,45], state estimation [21,46,47,48], data collection [4,15,49,50,51], attack-defense games [52,53,54], and adaptive reconfiguration [19,20,55,56]. A summary of these applications is found in Table 1. ...
... In addition to designing coordination algorithms that withstand attacks or failures [4,50,51,69], we also need resilient reconfiguration approach that enables robot teams to adaptively recover after attacks or faults [19,20,55,56]. Ramachandran et al. studied the problem of maintaining resource availability in a network of multiple robots [19] in such conditions. ...
... Then this resilient reconfiguration framework was utilized for maintaining sensing quality for robots to track targets [55]. Later, a resilient multi-robot coverage framework was designed in [56] where well-functioning robots adaptively reposition themselves to maintain a good team coverage performance once a robot in the team fails. Further, to completely cover or explore an environment by a team of robots, Song et al. presented a distributed eventdriven replanning algorithm to adaptively assign tasks to compensate for the team loss induced by robot failures [70]. ...
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Chapter
We consider the setting where a team of robots is tasked with tracking multiple targets with the following property: approaching the targets enables more accurate target position estimation, but also increases the risk of sensor failures. Therefore, it is essential to address the trade-off between tracking quality maximization and risk minimization. In the previous work [1], a centralized controller is developed to plan motions for all the robots – however, this is not a scalable approach. Here, we present a decentralized and risk-aware multi-target tracking framework, in which each robot plans its motion trading off tracking accuracy maximization and aversion to risk, while only relying on its own information and information exchanged with its neighbors. We use the control barrier function to guarantee network connectivity throughout the tracking process. Extensive numerical experiments demonstrate that our system can achieve similar tracking accuracy and risk-awareness to its centralized counterpart.
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