Moving-Landmark Assisted Distributed Learning Based Decentralized Cooperative Localization (DL-DCL) with Fault Tolerance
Authors: Shubhankar Gupta, Suresh Sundaram
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations involving sensor failures inducing around 40-60 times increase in the nominal bias show DL-DCL s estimation performance to be approximately 40% better than the well-known covariance-based estimate fusion methods. For the evaluation of DL-DCL s implementability and fault-tolerance capability in practice, a high-fidelity simulation is carried out in Gazebo with ROS2. |
| Researcher Affiliation | Academia | Shubhankar Gupta, Suresh Sundaram Artificial Intelligence and Robotics Lab (AIRL) Department of Aerospace Engineering Indian Institute of Science, Bengaluru, Karnataka, India shubhankarg@iisc.ac.in, vssuresh@iisc.ac.in |
| Pseudocode | Yes | Algorithm 1: DL-DCL algorithm for the ith agent, i [N] |
| Open Source Code | Yes | For the evaluation of its sim2real aspect, DL-DCL is also simulated in Gazebo with ROS2 (video and Git Hub links in the supplementary document1). |
| Open Datasets | No | The paper describes a simulation scenario rather than using a publicly available dataset, stating 'Results are averaged over 50 simulation runs. Each simulation run is carried out for a horizon of T = 1400 discrete time steps, with a sampling period of T = 0.1 second.' No concrete access information for a public dataset is provided. |
| Dataset Splits | No | The paper describes a simulation scenario and does not mention explicit training, validation, or test dataset splits. It states: 'Results are averaged over 50 simulation runs. Each simulation run is carried out for a horizon of T = 1400 discrete time steps, with a sampling period of T = 0.1 second.' |
| Hardware Specification | No | The paper mentions 'a high-fidelity simulation is carried out in Gazebo with ROS2' but does not specify any hardware details such as CPU, GPU models, or memory. |
| Software Dependencies | No | The paper mentions 'Gazebo with ROS2' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For the DL-DCL algorithm, the learning parameters are set to the values ηw = 2 and ηγ = 2 via parameter tuning. DL-DCL periodically resets its cumulative loss variables to zero after every To = 200 discrete time steps to avoid bias build-up during learning. The noise νx t,i and νϕ t,i in the IMUs (...) is assumed to be Gaussian with a mean of 0.05m and 0.5 deg., respectively, with a covariance of 0.1 (0.05)2m2 and 0.1 (0.05)2rad.2, respectively. |