17 research outputs found
Present and Future of SLAM in Extreme Underground Environments
This paper reports on the state of the art in underground SLAM by discussing
different SLAM strategies and results across six teams that participated in the
three-year-long SubT competition. In particular, the paper has four main goals.
First, we review the algorithms, architectures, and systems adopted by the
teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to
approach for virtually all teams in the competition), heterogeneous multi-robot
operation (including both aerial and ground robots), and real-world underground
operation (from the presence of obscurants to the need to handle tight
computational constraints). We do not shy away from discussing the dirty
details behind the different SubT SLAM systems, which are often omitted from
technical papers. Second, we discuss the maturity of the field by highlighting
what is possible with the current SLAM systems and what we believe is within
reach with some good systems engineering. Third, we outline what we believe are
fundamental open problems, that are likely to require further research to break
through. Finally, we provide a list of open-source SLAM implementations and
datasets that have been produced during the SubT challenge and related efforts,
and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE
Transactions on Robotics for pre-approva
NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTARÂżs demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology,
under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and
Defense Advanced Research Projects Agency (DARPA)
Relationships of rice yield and quality based on genotype by trait (GT) biplot
ABSTRACT An experiment was conducted to examine the influencing characters on rice by using 64 rice genotypes, including four local landraces, four released cultivars and 56 mutant lines (M5) derived from these genotypes, with application of the genotype by trait (GT) biplot methodology. The first two principal components (PC1 and PC2) accounted for 46.6% of total variation in 64 genotypes. The polygon view of GT biplot suggested seven sections for 64 genotypes. The vertex G38 had good amounts of grain yield, panicle length, hundred grain weight, internodes length, plant height and fertility percentage. Generally based on vector view it was demonstrated that the selection of high grain yield would be performed via thousand grain weight, panicle weight and number of filled grain per panicle. These traits should be considered simultaneously as effective selection criteria evolving high yielding rice genotypes because of their large contribution to grain yield. The genotypes G2, G4 and G7 could be considered for the developing of desirable progenies in the selection strategy of rice improvement programs. This study revealed GT biplot can graphically display the interrelationships among traits. In conclusion, it is recommended the use of GGE biplot to identify superior genotypes for simultaneous improvement of several traits
Tolerance Induction by CD40 Blocking through Specific Antibody in Dendritic Cells
Blocking antibodies are valuable tools for inhibiting the specific receptor- ligand interactions. The interaction of co-stimulatory molecules on the antigen presenting cells with their ligands on T cells is an essential step for T cell activation. In the present study, the effect of blocking antibody against CD40 on its T cell stimulatory potential is investigated.
The DCs (dendritic cells) were collected from the mice spleens and then cultured in vitro. We used purified rat anti-mice CD40 (Clone HM40-3) (BD USA) as a blocking antibody and the appropriate titer of the blocking antibody was determined by flow cytometry. The DCs were then treated by antibody and used in MLR assay.
The results of these experiments showed that CD40 blockade were associated with the increase in the of IL-4 secretion, shifting the DCs to stimulate Th2 cytokine production by the allogenic T cells, while the secretion of IL-12 by DCs decreased. Similarly, the DCs with reduced CD40 expression poorly responded to alloantigen stimulation in the MLR.
Collectively, these results emphasize the importance of CD40 pathway in tolerogenic DCs generation and also support the idea that downregulation of CD40 is effective in inhibiting the allostimulatory function
LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time Underground 3D Mapping
Lidar odometry has attracted considerable attention as a robust localization
method for autonomous robots operating in complex GNSS-denied environments.
However, achieving reliable and efficient performance on heterogeneous
platforms in large-scale environments remains an open challenge due to the
limitations of onboard computation and memory resources needed for autonomous
operation. In this work, we present LOCUS 2.0, a robust and
computationally-efficient \lidar odometry system for real-time underground 3D
mapping. LOCUS 2.0 includes a novel normals-based \morrell{Generalized
Iterative Closest Point (GICP)} formulation that reduces the computation time
of point cloud alignment, an adaptive voxel grid filter that maintains the
desired computation load regardless of the environment's geometry, and a
sliding-window map approach that bounds the memory consumption. The proposed
approach is shown to be suitable to be deployed on heterogeneous robotic
platforms involved in large-scale explorations under severe computation and
memory constraints. We demonstrate LOCUS 2.0, a key element of the CoSTAR
team's entry in the DARPA Subterranean Challenge, across various underground
scenarios.
We release LOCUS 2.0 as an open-source library and also release a
\lidar-based odometry dataset in challenging and large-scale underground
environments. The dataset features legged and wheeled platforms in multiple
environments including fog, dust, darkness, and geometrically degenerate
surroundings with a total of of operations and of distance
traveled
Comparison of Three Techniques for Generation of Tolerogenic Dendritic Cells: siRNA, Oligonucleotide Antisense, and Antibody Blocking
Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM
Multi-robot SLAM systems in GPS-denied environments require loop closures to
maintain a drift-free centralized map. With an increasing number of robots and
size of the environment, checking and computing the transformation for all the
loop closure candidates becomes computationally infeasible. In this work, we
describe a loop closure module that is able to prioritize which loop closures
to compute based on the underlying pose graph, the proximity to known beacons,
and the characteristics of the point clouds. We validate this system in the
context of the DARPA Subterranean Challenge and on numerous challenging
underground datasets and demonstrate the ability of this system to generate and
maintain a map with low error. We find that our proposed techniques are able to
select effective loop closures which results in 51% mean reduction in median
error when compared to an odometric solution and 75% mean reduction in median
error when compared to a baseline version of this system with no
prioritization. We also find our proposed system is able to find a lower error
in the mission time of one hour when compared to a system that processes every
possible loop closure in four and a half hours. The code and dataset for this
work can be found https://github.com/NeBula-Autonomy/LAM