A heterogeneous multi-robot team, where robots have varied sensing, actuation, communication and computational capabilities, is a promising direction to consider when building a resilient team. Such a team can work together by sharing resources between individual robots to perform complex tasks, thereby being resilient to failures of individual robots. For example, when a particular sensor on a robot fails, it may be able to rely on measurements made by a teammate nearby. A recent paper by members of the DCIST alliance proposes a method to maintain high resource availability in a networked heterogeneous multi-robot system subject to resource failures. In the proposed framework, the robots are engaged in a joint task using a pool of sensors, actuators, and processors. When a resource on a particular robot becomes unavailable (e.g., a sensor ceases to function due to a failure), the system automatically reconfigures so that the robot continues to have access to this resource by communicating with other robots. Specifically, this work considers the problem of the selecting edges to be modified in the system’s communication graph after a resource failure has occurred. The work defines a metric that characterizes the quality of the resource distribution in the network represented by the communication graph. Upon a resource becoming unavailable due to failure, the method reconfigures the network so that the resource distribution is brought as close to the ideal resource distribution as possible without a substantial change in the communication cost. The approach uses mixed integer semi-definite programming to achieve this goal. A simulated annealing method is employed to compute a spatial formation that satisfies the inter-robot distances imposed by the topology, along with other constraints. The overall strategy can compute a communication topology, spatial formation, and formation change motion planning in a few seconds. The effectiveness of the technique was validated in simulation and was demonstrated using a robot experiment involving a team of seven quadrotors. Current efforts are focused on incorporating explicit models of resources, namely sensing models, actuation models, and internal computation models, into the strategy.