PATS-X is a greenhouse pest suppression system that uses a depth camera with an autonomous micro air vehicle (MAV) to detect, track, and physically intercept flying insects. This study targets guidance and control for reliable aerial-to-aerial interception. Reinforcement learning (RL) is used to learn policies from insect flight recordings. We evaluate control policies at increasing levels of abstraction: direct motor commands, collective thrust and body rates (CTBR), and acceleration. In simulation, lower abstraction levels yield better interception performance; moving from acceleration to motor command reduces the median time to first interception by about 41%. A systematic variation of the observation space reveals that the most effective observations are body frame relative position and velocity, and short temporal histories add no benefit beyond noise filtering. Compared with a state-of-the-art classical benchmark, Fast Response Proportional Navigation (FRPN), the best motor level RL policy in simulation achieves a median first interception time of 0.85 [0.76--1.07]s with 99.1% interception rate, compared with FRPN at 1.90 [1.04--2.80]s and 95.6%. To address the reality gap, we compare how well the different control abstractions transfer to hardware. CTBR policies deploy on hardware with the least performance loss relative to simulation. Motor-level policies also transfer when trained with modest domain randomization (DR) plus an action-difference penalty that limits command jitter and thermal load. Acceleration-level policies did not transfer. In a PATS-X proof of concept, an RL controller deployed on the actual system reached a 95.6% interception rate of virtual moths versus 80.0% for the existing controller. Moreover, the RL controller shortened time-to-first-interception by 0.70s, indicating the potential of RL-based guidance for the PATS-X system.

Insect pest elimination through MAV interception can reduce the need for insecticides and can contribute to sustainable agriculture. In this research, we analyze the feasibility of such solutions through simulated two-player differential games of pursuit and evasion with agents operating on minimalistic sets of biologically-plausible observations and optimized to control constrained vehicle models through deep multi-agent reinforcement learning. Our pursuer and evader agent, representing the quadcopter drone and insect pest respectively, are asymmetric in design, capabilities and objectives. Our results show that our quadcopter pursuer is consistently able to pursue and intercept a reactive insect-inspired evader as well as recordings of actual insect targets, achieving interception rates of 55% and 94% on these respective tasks. In comparison, pursuers alternatively optimized against non-reactive evaders or reactive drone-like evaders with symmetric capabilities, achieve an interception rate of only 42% for the same insect target recordings. Despite these promising results, we conclude that further research is needed to formally establish the superiority of multi-agent optimization in this asymmetric game scenario. Finally, we determine how emergent behavior and strategies resemble nature. During the confrontations, we observe that our pursuer mainly implements pure-pursuit as well as motion camouflage to some degree; drawing comparison to the hunting strategy of dragonfly.