Asynchronous Behavior Trees with Memory aimed at Aerial Vehicles with Redundancy in Flight Controller Complex aircraft systems are becoming a target for automation. For successful operation, they require both efficient and readable mission execution system (MES). Flight control computer (FCC) units, as well as all important subsystems, are often duplicated. Discrete nature of MES does not allow small differences in data flow among redundant FCCs which are acceptable for continuous control algorithms. Therefore, mission state consistency has to be specifically maintained. We present a novel MES which includes FCC state synchronization. To achieve this result we developed the new concept of Asynchronous Behavior Tree with Memory (ABTM) and proposed a state synchronization algorithm. The implemented system was tested and proven to work in a realtime simulation of High Altitude Pseudo Satellite (HAPS) mission.

Analysis and Exploitation of Synchronized Parallel Executions in Behavior Trees Behavior Trees (BTs) are becoming a popular tool to model the behaviors of autonomous agents in the computer game and the robotics industry. One of the key advantages of BTs lies in their composability, where complex behaviors can be built by composing simpler ones. The parallel composition is the one with the highest potential since the complexity of composing preexisting behaviors in parallel is much lower than the one needed using classical control architectures as finite state machines. However, the parallel composition is rarely used due to the underlying concurrency problems that are similar to the ones faced in concurrent programming. In this paper, we define two synchronization techniques to tackle the concurrency problems in BTs compositions and we show how to exploit them to improve behavior predictability. Also, we introduce measures to assess execution performance and we show how design choices can affect them. To illustrate the proposed framework, we provide a set of experiments using the R1 robot and we gather statisticallysignificant data.