We propose a framework for resilience in a networked heterogeneous multirobot team subject to resource failures. Each robot in the team is equipped with resources that it shares with its neighbors, which are identified based on the team’s communication graph. Additionally, each robot in the team executes a task, whose performance depends on the resources to which it has access. When a resource on a particular robot becomes unavailable ( e.g., a camera ceases to function), the team optimally reconfigures its communication network so that the robots affected by the failure can continue their tasks. We focus on a monitoring task, where robots individually estimate the state of an exogenous process. We encode the end-to-end effect of a robot’s resource loss on the monitoring performance of the team by defining a new stronger notion of observability—one-hop observability. By abstracting the impact that low-level individual resources have on the task performance through the notion of one-hop observability, our framework leads to the principled reconfiguration of information flow in the team to effectively replace the lost resource on one robot with information from another, as long as certain conditions are met. Network reconfiguration is converted to the problem of selecting edges to be modified in the system’s communication graph after a resource failure has occurred. A controller based on finite-time convergence control barrier functions drives each robot to a spatial location that enables the communication links of the modified graph. We validate the effectiveness of our framework by deploying it on a team of differential-drive robots estimating the position of a group of quadrotors.

In this paper we discuss how to use a barrier function that is subject to kinematic constraints and limited sensing in order to guarantee that fixed wing unmanned aerial vehicles (UAVs) will maintain safe distances from each other at all times despite being subject to sensing constraints. Prior work has shown that a barrier function can be used to guarantee safe system operation when the state can be sensed at all times. However, we show that this construction does not guarantee safety when the UAVs are subject to limited range sensing. To resolve this issue, we introduce a method for constructing a new barrier function that accommodates limited sensing range from a previously existing barrier function that may not necessarily accommodate limited range sensing. We show that, under appropriate conditions, the newly constructed barrier function ensures system safety even in the presence of limited range sensing. We demonstrate the contribution of this paper in a simulated scenario of 20 fixed wing aircraft where the vehicles are able to maintain safe distances from each other even though the vehicles are subject to limited range sensing.

A number of coordinated behaviors are proposed for achieving specific tasks for multirobot systems. However, since most applications require more than one such behavior, one needs to be able to compose together sequences of behaviors while respecting local information flow constraints. Specifically, when the interagent communication depends on interrobot distances, these constraints translate into particular configurations that must be reached in finite time in order for the system to be able to transition between the behaviors. To this end, we develop a distributed framework based on finite-time convergence control barrier functions that enables a team of robots to adjust its configuration in order to meet the communication requirements for the different tasks. In order to demonstrate the significance of the proposed framework, we implemented a full-scale scenario where a team of eight planar robots explore an urban environment in order to localize and rescue a subject.

In densely-packed robot swarms operating in confined regions, spatial interference-which manifests itself as a competition for physical space-forces robots to spend more time navigating around each other rather than performing the primary task. This paper develops a decentralized algorithm that enables individual robots to decide whether to stay in the region and contribute to the overall mission, or vacate the region so as to reduce the negative effects that interference has on the overall efficiency of the swarm. We develop this algorithm in the context of a distributed collection task, where a team of robots collect and deposit objects from one set of locations to another in a given region. Robots do not communicate and use only binary information regarding the presence of other robots around them to make the decision to stay or retreat. We illustrate the efficacy of the algorithm with experiments on a team of real robots.

As the size of robots decreases in multirobot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots' surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multirobot experiments illustrate the feasibility of the proposed approach.

In multi-robot systems, although the idea of behaviors allows for an efficient solution to low-level tasks, high-level missions can rarely be achieved by the execution of a single behavior. In contrast to this, a sequence of behaviors would provide the requisite expressiveness, but there are no a priori guarantees that the sequence is composable in the sense that the robots can actually execute it. In order to guarantee a provably correct composition of behaviors, Finite-Time Convergence Control Barrier Functions are introduced in this paper to guarantee the terminal configuration of one behavior is a valid initial configuration for the following one. Nominal control inputs prescribed by the behaviors are modified in a minimally invasive fashion, in order to establish the information-exchange network required by the following behavior. The effectiveness of the proposed composition strategy is validated on a team of mobile robots.