An important aspect of dynamic urban coverage is how building collision avoidance is incorporated into the overall coverage mission. We consider a multi-agent urban dynamic coverage problem in which a team of flying agents uses downward facing cameras to observe the street-level environment outside of buildings. Cameras are assumed to be ineffective above a maximum altitude (lower than building height), such that agents must move around or over buildings to complete their mission. The main objective of this paper is to compare three different building avoidance strategies that are compatible with dynamic ergodic methods. To provide context for these results, we also compare our results to three other common coverage methods including: boustrophedon coverage (lawn-mower sweep), Voronoi region based coverage, and a naive grid method. All algorithms are evaluated in simulation with respect to four performance metrics (percent coverage, revisit count, revisit time, and the integral of area viewed over time), across team sizes ranging from 1 to 25 agents, and in five types of urban environments of varying density and height. We find that the relative performance of algorithms changes based on the ratio of team size to search area, as well the height and density characteristics of the urban environment.

In multi-agent collaborative search missions, task allocation is required to determine which agents will perform which tasks. We propose a new approach for decentralized task allocation based on a decentralized genetic algorithm (GA). The approach parallelizes a genetic algorithm across the team of agents, making efficient use of their computational resources. In the proposed approach, the agents continuously search for and share better solutions during task execution. We conducted simulation experiments to compare the decentralized GA approach and several existing approaches. Two objectives were considered: a min-sum objective (minimizing the total distance traveled by all agents) and a min-time objective (minimizing the time to visit all locations of interest). The results showed that the decentralized GA approach yielded task allocations that were better on the min-time objective than those created by existing approaches and solutions that were reasonable on the min-sum objective. The decentralized GA improved min-time performance by an average of 5.6% on the larger instances. The results indicate that decentralized evolutionary approaches have a strong potential for solving the decentralized task allocation problem.

Modern aircraft increasingly rely on electric power for subsystems that have traditionally run on mechanical power. The complexity and safety-criticality of aircraft electric power systems have therefore increased, rendering the design of these systems more challenging. This work is motivated by the potential that correct-by-construction reactive controller synthesis tools may have in increasing the effectiveness of the electric power system design cycle. In particular, we have built an experimental hardware platform that captures some key elements of aircraft electric power systems within a simplified setting. We intend to use this platform for validating the applicability of theoretical advances in correct-by-construction control synthesis and for studying implementation-related challenges. We demonstrate a simple design workflow from formal specifications to auto-generated code that can run on software models and be used in hardware implementation. We show some preliminary results with different control architectures on the developed hardware testbed.

This paper describes TuLiP, a Python-based software toolbox for the synthesis of embedded control software that is provably correct with respect to an expressive subset of linear temporal logic (LTL) specifications. TuLiP combines routines for (1) finite state abstraction of control systems, (2) digital design synthesis from LTL specifications, and (3) receding horizon planning. The underlying digital design synthesis routine treats the environment as adversary; hence, the resulting controller is guaranteed to be correct for any admissible environment profile. TuLiP applies the receding horizon framework, allowing the synthesis problem to be broken into a set of smaller problems, and consequently alleviating the computational complexity of the synthesis procedure, while preserving the correctness guarantee.