Search for: All records

Abstract Red tide is caused by the accumulation of Karenia (K.) brevis, which produces brevetoxin (BTx), a neurotoxin. Excreted BTx is incorporated into sea spray aerosol (SSA), which is created from the bursting of bubbles at the ocean’s surface. For the first time, this study measures the enrichment factor of BTx in K. brevis algal aerosol. During red-tide events in 2021 and 2022, aerosol and water samples were collected from Gulf Coast beaches in Southwest Florida with various levels of K. brevis growth. The concentrations of BTx in SSA were measured using an enzyme-linked immunosorbent assay kit. The concentrations of both aerosolized BTx and organic matter (OM) were normalized using that of sodium ions and were shown to be significantly higher than those observed in seawater. Lipophilic BTx is present in SSA at concentrations that are 2-4 orders of magnitude higher than seawater, and 1-2 orders of magnitude higher than concentrations of OM in SSA. Enrichment of aerosolized BTx was also simulated in the algal culture tank with two different aerosol generation methods. The estimated activity coefficient (order of 1019) of BTx in bulk seawater using the inorganic thermodynamic model indicates very poor solubility of BTx in seawater and supports its enrichment in ocean surfaces and SSA. Examining the enrichment factors of BTx and organic matter in SSA contributes to our comprehension of the potential respiratory challenges posed by inhaled algal aerosols during red tide occurrences. In addition, enriched BTx in the uppermost layer of the ocean during red tide blooms can adversely influence animals that inhabit in tide flats with neurological and respiratory impacts. 

The ability to autonomously modify their environment dramatically increases the capability of robots to operate in unstructured environments. We develop a specialized construction algorithm and robotic system that can autonomously build motion support structures with previously unseen objects. The approach is based on our prior work on adaptive ramp building algorithms, but it eliminates the assumption of having specialized building materials that simplify manipulation and planning for stability. Utilizing irregularly shaped stones makes the problem significantly more challenging since the outcome of individual placements is sensitive to details of contact geometry and friction, which are difficult to observe. To reuse the same high-level algorithm, we develop a new physics-based planner that explicitly considers the uncertainty produced by incomplete in-situ sensing and imprecision during pickup and placement. We demonstrate the approach on a robotic system that uses a newly developed gripper to reliably pick up stones with minimal additional sensors or complex grasp planning. The resulting system can build structures with more than 70 stones, which in turn provide traversable paths to previously inaccessible locations. 

Abstract Dry stacking with found, minimally processed rocks is a useful capability when it comes to autonomous construction. However, it is a difficult planning problem since both the state and action space are continuous, and structural stability is strongly affected by complex friction and contact constraints. We propose an algorithmic approach for autonomous construction from a collection of irregularly shaped objects. The structure planning is calculated in simulation by first considering geometric and physical constraints to find a small set of feasible actions and then refined by using a hierarchical filter based on heuristics. These plans are then executed open-loop with a robotic arm equipped with a wrist RGB-D camera. Experimental results show that the proposed planning algorithm can significantly improve the state of the art robotics dry-stacking techniques.