Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2211.01813

Developing Modular Autonomous Capabilities for sUAS Operations

Keegan Quigley·Virginia Goodwin·Luis Alvarez·Justin Yao·Yousef Salaman Maclara
View original source ↗Read source document ↗Export metadata ↓

Abstract

Small teams in the field can benefit from the capabilities provided by small Uncrewed Aerial Systems (sUAS) for missions such as reconnaissance, hostile attribution, remote emplacement, and search and rescue. The mobility, communications, and flexible payload capacity of sUAS can offer teams new levels of situational awareness and enable more highly coordinated missions than previously possible. However, piloting such aircraft for specific missions draws personnel away from other mission-critical tasks, increasing the load on remaining personnel while also increasing complexity of operations. For wider adoption and use of sUAS for security and humanitarian missions, safe and robust autonomy must be employed to reduce this burden on small teams. In this paper, we present the development of the Collaborative-UAS for Hostile Attribution, Surveillance, Emplacement, and Reconnaissance (CHASER) testbed, for rapidly prototyping capabilities that will reduce strain on small teams through sensor-guided autonomous control. We attempt to address autonomy needs unfilled by commercial sUAS platforms by creating and testing a series of composable modules that can be configured to support multiple missions. Methods implemented and presented here include radar track correlation, on-board computer vision target detection, target position estimation, closed-loop relative position control, and efficient search of a 3D volume for target acquisition. We configure and test a series of these modules in an example mission, executing a fully autonomous chase of an intruding sUAS in live flight, and demonstrating the success of the modularized autonomy approach. We present performance results from simulation or live flight tests for each module. Lastly, we describe the software architecture that we have developed for flexible controls and comment on how the capabilities presented may enable additional missions.

Explore related subjects

cs.ROcs.SYeess.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Keegan Quigley, Virginia Goodwin, Luis Alvarez, Justin Yao, Yousef Salaman Maclara. 2023-01-27. Developing Modular Autonomous Capabilities for sUAS Operations. https://arxiv.org/abs/2211.01813

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

J-PARSE: Jacobian-based Projection Algorithm for Resolving Singularities Effectively in Inverse Kinematic Control of Serial Manipulators

J-PARSE is an algorithm for smooth first-order inverse kinematic control of a serial manipulator near kinematic singularities. The commanded end-effector velocity is interpreted component-wise, according to the available mobility in each dimension of the task space. First, a substitute ''Safety'' Jacobian matrix is created, keeping the aspect ratio of the manipulability ellipsoid above a threshold value. The desired velocity is then projected onto non-singular and singular directions, and the latter projection scaled down by a factor informed by the altered mobility. A right-inverse of the non-singular Safety Jacobian is applied to the modified command. In the absence of joint limits and collisions, this ensures safe transition into and out of low-mobility configurations, guaranteeing locally stable reaching behavior towards target poses within, on the boundary of, and outside the workspace. The behavior is further guaranteed to be locally asymptotically stable if the starting and target configurations are not exactly singular, even if they are nearly singular. Velocity control with J-PARSE is benchmarked against approaches from the literature, illustrating its use of a single tuning parameter to simultaneously achieve high reaching accuracy and stable behavior. Applications in teleoperation, servoing, and learning are demonstrated. Videos and code are available at https://jparse-manip.github.io/.

cs.RO↗
arXiv2026

NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping

Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encoding statistical motion patterns in a map, but existing representations use discrete spatial sampling and typically require costly offline construction. We propose a continuous spatio-temporal MoD representation based on implicit neural functions that directly map coordinates to the parameters of a Semi-Wrapped Gaussian Mixture Model. This removes the need for discretization and imputation for unevenly sampled regions, enabling smooth generalization across both space and time. Evaluated on two public datasets with real-world people tracking data, our method achieves better accuracy of motion representation and smoother velocity distributions in sparse regions while still being computationally efficient, compared to available baselines. The proposed approach demonstrates a powerful and efficient way of modeling complex human motion patterns and high performance in the trajectory prediction downstream task. The code is publicly available at https://github.com/test-bai-cpu/nemo-map.

cs.RO↗
arXiv2026

Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots

The calibration of extrinsic parameters and clock offsets between sensors for high-accuracy performance in underwater SLAM systems remains insufficiently explored. Existing methods for Doppler Velocity Log (DVL) calibration are either constrained to specific sensor configurations or rely on oversimplified assumptions, and none jointly estimate translational extrinsics and time offsets. We propose a Unified Iterative Calibration (UIC) framework for general DVL sensor setups, formulated as a Maximum A Posteriori (MAP) estimation with a Gaussian Process (GP) motion prior for high-fidelity motion interpolation. UIC alternates between efficient GP-based motion state updates and gradient-based calibration variable updates, supported by a provably statistically consistent sequential initialization scheme. The proposed UIC can be applied to IMU, cameras and other modalities as co-sensors. We release an open-source DVL-camera calibration toolbox. Beyond underwater applications, several aspects of UIC-such as the integration of GP priors for MAP-based calibration and the design of provably reliable initialization procedures-are broadly applicable to other multi-sensor calibration problems. Finally, simulations and real-world tests validate our approach.

cs.RO↗