Decentralized dynamic task allocation for UAVs with limited communication range

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem

Implementing the Leader Development That Counts

Effective leader development is too often the first casualty of high demands placed on leaders, from corporate America to the U.S. military. A comparison of these entities’ common leader development programs and workforce feedback reveals insufficient strategies and competing priorities. Organizations succumbing to these obstacles unknowingly find themselves trapped in adverse cycles of leadership development, perpetuating undernourished talent and mediocre performance. This problem will not fix itself. Organizations must refocus efforts to understand and implement a leader driven, interpersonally focused, and culturally ingrained brand of leader development to maximize available talent in crafting their envisioned organization