Drones

In this paper, we present a physical interaction scheme for omnidirectional multirotor aerial vehicles (MRAVs) equipped with fixedly tilted non-coplanar propellers, based on an admittance control architecture. An external wrench observer is employed to estimate the interaction wrench at the end-effector, hence eliminating the need for an additional force/torque sensor. We show that using the nominal allocation matrix in this class of admittance controllers can lead to a contact loss during complex interaction scenarios due to unmodeled and state-dependent aerodynamics effects. To address this issue, we propose a method for identifying the wrench map across different regions of the vehicle’s orientation in using free-flight experimental data. This is achieved by formulating a Quadratic Programming (QP) optimization whose solution provides the best approximation of the wrench map for a given orientation of the MRAV. The effectiveness of this approach is experimentally demonstrated, including static point contacts at various orientations, sliding contact, and peg-in-hole tasks.

High-altitude low-speed aerostats are ideal unmanned platforms for communication coverage, remote sensing, environmental monitoring, aviation support, and other applications. To address practical operational needs such as rapid emergency deployment, this paper proposes a path planning method for low-speed aerostats based on the Markov decision process (MDP). The method is optimized to minimize deployment time while accounting for discrepancies between forecasted and actual wind fields. An uncertain wind field model is established to incorporate wind-related uncertainties into the MDP framework, with key parameters—including the state space, action set, immediate reward, and transition probability—designed accordingly. A mathematical model is formulated to address the global path planning problem under complex constraints, such as horizontal wind resistance capability, altitude control capacity, and flight time requirements. Simulation results demonstrate that the proposed method enables aerostats to achieve optimal 2D and 3D path planning under complex constraints. Furthermore, regional reachability is quantitatively analyzed, providing technical support for the rapid deployment of aerostats to target areas in practical applications. The core innovations of this work lie in the integration of a probabilistic wind uncertainty model with a constraint-aware MDP framework, enabling optimal 3D path planning and quantitative reachability analysis for high-altitude low-speed aerostats.

Improvements in thermal infrared imaging provide new opportunities for drone-based wildlife surveys. The use of thermal sensors can be limited by ambient temperatures and vegetation cover, which can limit opportunities to survey during optimal biological seasons. Pre-programming isotherm settings in thermal cameras has the potential to allow surveys during warmer environmental conditions. We evaluated night-time surveys of white-tailed deer (Odocoileus virginianus) using isotherm settings in a 102 ha enclosed property in South Texas during February (winter) and July (summer) 2022. Detection probabilities were 0.84 and 0.65 during winter and summer, respectively. Percent woody cover was 48.1% and 60.7% during these seasons, respectively. The seasonal pattern in detection probabilities met expectations in terms of visibility bias caused by canopy cover. Despite different detection probabilities among seasons, population estimates were similar because distance sampling accounted for visibility bias. The use of isotherm settings allowed us to survey during temperatures previously thought to be too warm for ideal contrast (~21 °C vs. 30 °C), which provides more opportunities to survey during biologically important seasons typically associated with warm temperatures (i.e., fawning and antlerogenesis). We recommend the use of distance sampling methods to evaluate and correct for visibility bias during thermal-based drone surveys because detections of focal species may vary with vegetation.