Public reporting on drone warfare — aggregated daily.
Researchers experimentally validated a leader-follower control system where a UGV generates motion and a UAV follows using only real-time state data, not predefined paths. The test compared two nonlinear control strategies for the ground leader, establishing a state-based cooperation model distinct from classical trajectory synchronization—relevant for heterogeneous teaming without centralized planning.
Researchers proved that pairing an unmanned ground vehicle with an aerial scout reduces the competitive ratio for navigating routes with unknown blockages from 2k−1 to 2(k−1)v_G/(v_G+v_A)+1, where k is the number of disjoint paths and v denotes vehicle speed. An optimal path-partitioning algorithm assigns ground inspection to the UGV and aerial survey to the UAV, improving operational efficiency in contested or GPS-denied environments.
A combined Kalman filter–Soft Actor–Critic algorithm improves UAV relay coordination for UGV swarms by filtering sensor noise before feeding state estimates to the learning agent. The method addresses a core problem in ground swarms operating in terrain-blocked environments: noisy observations that degrade relay trajectory decisions.
Researchers developed scenario-based thermal management models for unmanned ground vehicles integrating AI, power electronics, and battery systems using Simulink and GT-SUITE co-simulation. The approach enables operators to predict and manage heat dissipation across multiple onboard subsystems before deployment.
A framework using digital stigmergy—where UAVs deposit information only on event occurrence—reduced communication payload between heterogeneous aerial and ground platforms by 94.44% versus continuous updates while meeting search and verification time targets. The result demonstrates that communication-sparse coordination can scale multi-robot search missions without sacrificing mission completion.
A proof-of-concept platform combines ground and aerial unmanned vehicles with robot-assisted patient care to extract casualties from chemical, biological, radiological and nuclear environments without exposing rescue personnel. The system demonstrates technical feasibility for autonomous evacuation in high-threat, inaccessible terrain where conventional methods fail.
A reproducible Python package integrates environment dynamics, radio adaptation, and graph-based networking for system-level evaluation of software-defined radio control across unmanned maritime and ground platforms. The tool supports paired prediction-horizon studies across four scenarios, enabling assessment of dynamic reconfiguration costs in multi-domain teaming.
A foot-oriented control system allows operators to command unmanned ground vehicles by foot positioning, generating speed and steering inputs. Four operators successfully completed tasks using the interface, though preliminary results favored joystick control. The finding demonstrates technical feasibility of an alternative teleoperation mode for scenarios where hands remain occupied.
A relative-initialization and online-replanning framework enables UAVs to dock with ground vehicles for battery recovery without external localization infrastructure. Using UWB triangulation and onboard IMU data, the method jointly optimizes docking points for energy efficiency while managing UGV kinematics and obstacle avoidance—extending mission duration in denied environments.
Researchers present CAVEAT, a collaborative target-recognition system that fuses observations from heterogeneous UAS, UGV, and USV platforms using vision-language models to disambiguate targets under sensor degradation and viewpoint variation. The approach addresses hallucination risk—where models misidentify objects with high confidence—critical for multi-domain teaming where manual reconciliation is slow and error-prone.
Researchers demonstrated that camouflage-matched fiducial markers can georeference LiDAR and vision-based mapping from unmanned ground and aerial vehicles without GNSS. The method reduced drift by up to 99%, enabling autonomous platforms to maintain consistent spatial references in environments where satellite signals are unavailable.
Researchers assembled a manually annotated dataset of 6,106 ship images from multiple sources, including UAV and USV imagery captured across varying weather, illumination, and time conditions. The dataset enables development of maritime detection models robust to operational variability—a capability analysts need as unmanned platforms expand in naval surveillance missions.
Researchers demonstrated a causal Kalman filter method that processes delayed acoustic corrections without full historical replay, reducing position error from 1.062 m to 0.456 m in 154 paired recordings at 1.5-s acoustic delay. The approach matches replay accuracy within 1.03% while cutting computation per update by 127-fold, enabling real-time UUV navigation under acoustic positioning constraints.
Researchers augmented particle filters to account for inflated confidence in local estimates when autonomous surface vessels operate without centralized coordination. Testing on lake-based USVs showed the method preserves normal tracking performance while improving accuracy in edge cases where communication is sporadic or absent.
A control framework using conformable fractional calculus demonstrates global asymptotic stability across all initial conditions in unmanned ground vehicles. The approach validates nonlinear controllers effective far from equilibrium states, expanding the envelope of reliable autonomous vehicle operation beyond traditional local-stability guarantees.
SCARAB demonstrates unmanned surface vehicles that self-organize into floating bridges using only onboard sensing, without GPS or radio links. The approach could enable Army river crossings in contested environments where communications are denied, reducing logistics and crew risk compared to traditional bridge systems.
A new open-source repository for unmanned aerial and ground vehicle tracking has been published. The release provides analysts and developers with code for multi-platform tracking applications, addressing integration challenges in heterogeneous unmanned systems operations.
A task-oriented communication system that decides when and what vision data to send to edge servers reduces positioning errors for unmanned vehicles operating without satellite signals. By weighting requests against communication costs, the method improves mean and worst-case performance on budget-constrained wireless links—critical for operations in jammed or GPS-denied environments.
Researchers demonstrated human-robot interaction using large language models to control mixed aerial and ground unmanned vehicle formations within finite time bounds. The approach addresses coordination challenges in heterogeneous swarms where assets have different capabilities and dynamics.
Researchers present VIRGA, a coordination system that keeps unmanned aerial and ground vehicles mutually observable during collaborative missions in obstacle-rich environments. Testing against three existing controllers reveals trade-offs: faster response often sacrifices safety margins, while improved geometric avoidance increases computational cost, preventing stable sensor maintenance.
Researchers demonstrated a coordination framework that synthesizes mission actions from radar, RF, acoustic, and visual data while enforcing safety through independent analogue envelopes that veto unsafe commands. The approach separates interpretation, coordination, and execution layers to maintain auditability and resilience against cyber compromise, jamming, and spoofing—relevant for contested coastal surveillance operations.