Julianna Hoitt | Searcharxiv

arXiv2026

Re-Solving the Shepherding Problem: Lead When Possible, Herd When Necessary

Designing systems for autonomous transport of groups of living agents has received a lot of attention in recent years due to a wealth of important potential applications. Biomimetic approaches are often sought, and a range of herding algorithms, inspired by how dogs herd sheep, as well as leadership algorithms mimicking leader-follower systems, have been introduced. However, they suffer from a common problem: shepherding algorithms require that agents evade the shepherd, and leading algorithms require that agents follow. This can cause problems in real-world applications where the behavioral responses of the agents to a transporter are likely to be heterogeneous over both long and short timescales. Here, we introduce an algorithm that adaptively switches between leading and herding depending on the response it receives from the agents to mitigate this problem. We show via simulation that this mixed algorithm can transport groups with any follower and evader composition, and we compare its performance with lead-only and herd-only algorithms. We also show that the mixed algorithm can deal with groups where individual agents randomly switch their strategy over time, as long as sufficient time is provided to complete the task relative to the switching rate. Given that our algorithm overcomes issues associated with herd-only and lead-only algorithms and might also, as a side effect, mitigate the issue of habituation to robotic transporters, it takes us one step closer to realizing many of the proposed applications for these types of algorithms.

q-bio.OT↗

arXiv2025

Effective resource allocation to combat invasions of the spotted lanternfly (Lycorma delicatula) and similar pests

The spotted lanternfly is rapidly establishing itself as a major insect pest with global implications. Despite significant management efforts, its spread continues in invaded regions, and refined management strategies are required. A recent study introduced a model that generalized the results of empirical control efficacy studies by incorporating population dynamics and incomplete delivery. In particular, a generalized population growth formula was derived, providing the minimum proportion of a population that must be treated with a given control to induce population decline. However, this model could not address the more relevant question of how best to deploy a control to minimize population growth. Here, we extend this model and formula to address this question in various settings. When the effect of control is proportional to effort, we show that exhaustive sequential deployment of stage-specific controls, ordered by efficacy, is optimal. When control effects exhibit diminishing returns with effort, we derive a formula for when to switch controls, providing an effective strategy in situations where management resources may vary or be cut at any time. We also use numerical global optimization methods to obtain strategies when resources are fixed a priori. Both strategies outperform random deployment, which can result in disastrous outcomes. Our results demonstrate that adopting an effective strategy for deploying stage-specific controls is essential for managing the spotted lanternfly. However, we found no papers addressing this topic in the lanternfly literature, nor information on whether such strategies are used. Given the limited resources available to combat the lanternfly, it is critical that they are used effectively. The approach introduced here, along with other optimization methods used in related fields, can contribute to this goal.

q-bio.OT↗