Search — boot

A Jetson AGX Orin report indicates ISP1 fails to power on at boot. This prevents expected camera/ISP functionality from being available after startup.

A Jetson AGX Orin Developer Kit is reported completely dead with no power response. No additional context is provided.

A user reports a Jetson Orin Nano booting issue. No further details are included.

Orin Nano Super Dev Kit shows an MSS SDRAM init failure (err 0x48480112) though the module was previously functional. This prevents the system from booting normally.

Jetson ORIN is reported as not coming out of recovery. This blocks normal boot and device provisioning.

User reports Orin NX 16G boot failed. This prevents use of the module/device.

User reports a first boot issue with Jetson Orin Nano dev kit. This blocks initial setup.

Xavier NX with new DRAM modules won’t flash/boot with L4T 32.7.1; user requests PCN overlay or recommended path. This blocks bringing up new modules on existing software.

User reports difficulty booting from Thor IGX Mini. This blocks platform setup and testing.

User asks why kernel and dtb partitions were removed on Thor. This indicates confusion about platform boot/partition layout changes.

User asks (in Chinese) how to modify the default boot order on a system adapted for JetPack 6.1.2. This is a boot configuration question impacting deployment.

PKC key revocation reportedly does not work on L4T R36.5.0. This undermines secure provisioning and lifecycle management.

Jetson 4.6.0 Xavier NX board with Micron 16Gbit memory hits an MB1 boot issue. This prevents boot and blocks device use.

Reliable real-time 3D localization is essential for multi-UAV navigation, collision avoidance, and coordinated flight, yet onboard estimates can degrade under GNSS multipath, non-line-of-sight reception, vertical drift, and intentional interference. This paper presents a decentralized, lightweight 3D position-refinement layer that improves robustness by fusing each Unmanned Aerial Vehicle (UAV)'s local estimate with neighbor-shared state summaries and inter-UAV range or proximity constraints. The method performs uncertainty-aware neighborhood fusion by weighting each UAV's prior according to its reported covariance and weighting neighbor constraints according to link quality, ranging uncertainty, and a learned trust score. To support practical deployment, the framework explicitly handles cold start and temporary localization loss by inflating or substituting weak priors, allowing trusted neighborhood constraints to bootstrap and stabilize estimates until absolute sensing recovers. To mitigate the impact of faulty or malicious participants, each UAV applies a local range-consistency check, smoothed over time, to down-weight or exclude neighbors whose reported positions are incompatible with observed inter-UAV distances. Simulation experiments with 10 UAVs in a 3D volume show that the proposed refinement substantially reduces mean localization error during cold start, remains competitive after local estimators stabilize, and maintains lower error as the fraction of malicious nodes increases compared with fusion without trust. These results suggest that the approach can serve as a practical resilience layer for swarm operation in challenging environments.

Vision-Language Models (VLMs) have demonstrated exceptional general reasoning capabilities. However, their performance in embodied navigation remains hindered by a scarcity of aligned open-world vision and robot control data. Despite simulators providing a cost-effective alternative for data collection, the inherent reliance on photorealistic simulations often limits the transferability of learned policies. To this end, we propose \textit{\textbf{S}andbox-\textbf{A}bstracted \textbf{G}rounded \textbf{E}xperience} (\textbf{\textit{SAGE}}), a framework that enables agents to learn within a physics-grounded semantic abstraction rather than a photorealistic simulation, mimicking the human capacity for mental simulation where plans are rehearsed in simplified physics abstractions before execution. \textit{SAGE} system operates via three synergistic phases: (1) \textit{Genesis}: constructing diverse, physics-constrained semantic environments to bootstrap experience; (2) \textit{Evolution}: distilling experiences through Reinforcement Learning (RL), utilizing a novel asymmetric adaptive clipping mechanism to stabilize updates; (3) \textit{Navigation}: bridging the abstract policy to open-world control. We demonstrate that \textit{SAGE} significantly improves planner-assisted embodied navigation, achieving a 53.21\% LLM-Match Success Rate on A-EQA (+9.7\% over baseline), while showing encouraging transfer to physical indoor robot deployment.