Concurrent Tree Traversals for Improved Mission Performance under Limited Communication Range

Abstract — In previous work we presented a multi-robot strategy for routing missions in large scenarios where network connectivity must be explicitly preserved. This strategy is founded on the traversal of path trees in such a way that connectivity to a static control center is always maintained, while ensuring that any target that is reachable by a chain consisting of all robots is eventually visited. In this work we improve the strategy performance by extending its sequential onetask-at-a-time execution approach with concurrent execution of tasks. We demonstrate that the general problem is NP-hard and offer several heuristic approaches to tackle it. We study the improvements that these heuristics can offer in regard to several important variables like network range and clustering of targets, and finally compare their performance over the optimal solutions for small problem instances. In summary, we offer a complete characterization of the new concurrent capabilities of the CONNECTTREE strategy. I

Multi-robot persistent surveillance with connectivity constraints

Mobile robots, especially unmanned aerial vehicles (UAVs), are of increasing interest for surveillance and disaster response scenarios. We consider the problem of multi-robot persistent surveillance with connectivity constraints where robots have to visit sensing locations periodically and maintain a multi-hop connection to a base station. We formally define several problem instances closely related to multi-robot persistent surveillance with connectivity constraints, i.e., connectivity-constrained multi-robot persistent surveillance (CMPS), connectivity-constrained multi-robot reachability (CMR), and connectivity-constrained multi-robot reachability with relay dropping (CMRD), and show that they are all NP-hard on general graph. We introduce three heuristics with different planning horizons for convex grid graphs and combine these with a tree traversal approach which can be applied to a partitioning of non-convex grid graphs (CMPS with tree traversal, CMPSTT). In simulation studies we show that a short horizon greedy approach, which requires parameters to be optimized beforehand, can outperform a full horizon approach, which requires a tour through all sensing locations, if the number of robots is larger than the minimum number of robots required to reach all sensing locations. The minimum number required is the number of robots necessary for building a chain to the farthest sensing location from the base station. Furthermore, we show that partitioning the area and applying the tree traversal approach can achieve a performance similar to the unpartitioned case up to a certain number of robots but requires less optimization time