Although communication delays can disrupt multiagent systems, most of the existing multiagent trajectory planners lack a strategy to address this issue. State-of-the-art approaches typically assume perfect communication environments, which is hardly realistic in real-world experiments. This paper presents Robust MADER (RMADER), a decentralized and asynchronous multiagent trajectory planner that can handle communication delays among agents. By broadcasting both the newly optimized trajectory and the committed trajectory, and by performing a delay check step, RMADER is able to guarantee safety even under communication delay. RMADER was validated through extensive simulation and hardware flight experiments and achieved a 100% success rate of collisionfree trajectory generation, outperforming state-of-the-art approaches.

Detection of high-energy laser strikes is key to the survivability of military assets in future warfare. The introduction of laser weapon systems demands the capability to quickly detect these strikes without disrupting the stealth capability of military craft with active sensing technologies. We explored the use of thermoelectric generators (TEGs) as self-powered passive sensors to detect such strikes. Experiments were conducted using lasers of various power ratings, wavelengths, and beam sizes to strike 2 × 2 cm 2 commercially available TEGs arranged in different configurations. Open-circuit voltage and short-circuit current responses of TEGs struck with 808-, 1070-, and 1980-nm lasers at irradiance levels between 8.5 and 509.3 W∕cm 2 and spot sizes between 2 and 8 mm are compared. TEG surface temperatures indicate that the sensor can survive temperatures nearing 400°C. TEG open-circuit voltage magnitudes correlate more strongly with net incident laser power than with specific irradiance levels, and linearity is limited by Seebeck coefficient variation with temperature. Open-circuit voltage responses are characterized by 10% to 90% rise times of ∼2 to 10 s despite surface temperatures not reaching equilibrium. With open-circuit voltage as the sensing parameter, detection thresholds three times above the standard deviation noise level can be exceeded within 300 ms of the start of a laser strike with irradiance levels of ∼200 W∕cm 2. Potential harvested power levels as high as 16 mW are estimated based on measured electrical responses. A multiphysics finite-element model corresponding to the experiments was developed to further optimization of a lightweight, lowprofile TEG sensor for detection of high-energy laser strikes. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Fully decentralized, multiagent trajectory planners enable complex tasks like search and rescue or package delivery by ensuring safe navigation in unknown environments. However, deconflicting trajectories with other agents and ensuring collision-free paths in a fully decentralized setting is complicated by dynamic elements and localization uncertainty. To this end, this paper presents (1) an uncertainty-aware multiagent trajectory planner and (2) an image segmentationbased frame alignment pipeline. The uncertainty-aware planner propagates uncertainty associated with the future motion of detected obstacles, and by incorporating this propagated uncertainty into optimization constraints, the planner effectively navigates around obstacles. Unlike conventional methods that emphasize explicit obstacle tracking, our approach integrates implicit tracking. Sharing trajectories between agents can cause potential collisions due to frame misalignment. Addressing this, we introduce a novel frame alignment pipeline that rectifies inter-agent frame misalignment. This method leverages a zeroshot image segmentation model for detecting objects in the environment and a data association framework based on geometric consistency for map alignment. Our approach accurately aligns frames with only 0.18 m and 2.7 • of mean frame alignment error in our most challenging simulation scenario. In addition, we conducted hardware experiments and successfully achieved 0.29 m and 2.59 • of frame alignment error. Together with the alignment framework, our planner ensures safe navigation in unknown environments and collision avoidance in decentralized settings.