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Rectilinear forms of snake-like robotic locomotion are anticipated to be an advantage in obstacle-strewn scenarios characterizing urban disaster zones, subterranean collapses, and other natural environments. The elongated, laterally narrow footprint associated with these motion strategies is well suited to traversal of confined spaces and narrow pathways. Navigation and path planning in the absence of global sensing, however, remains a pivotal challenge to be addressed prior to practical deployment of these robotic mechanisms. Several challenges related to visual processing and localization need to be resolved to to enable navigation. As a first pass in this direction, we equip a wireless, monocular color camera to the head of a robotic snake. Visiual odometry and mapping from ORB-SLAM permits self-localization in planar, obstacle strewn environments. Ground plane traversability segmentation in conjunction with perception-space collision detection permits path planning for navigation. A previously presented dynamical reduction of rectilinear snake locomotion to a non-holonomic kinematic vehicle informs both SLAM and planning. The simplified motion model is then applied to track planned trajectories through an obstacle configuration. This navigational framework enables a snake-like robotic platform to autonomously navigate and traverse unknown scenarios with only monocular vision. 

Context. Protostellar jets are an important agent of star formation feedback, tightly connected with the mass-accretion process. The history of jet formation and mass ejection provides constraints on the mass accretion history and on the nature of the driving source. Aims. We characterize the time-variability of the mass-ejection phenomena at work in the class 0 protostellar phase in order to better understand the dynamics of the outflowing gas and bring more constraints on the origin of the jet chemical composition and the mass-accretion history. Methods. Using the NOrthern Extended Millimeter Array (NOEMA) interferometer, we have observed the emission of the CO 2–1 and SO N J = 5 4 –4 3 rotational transitions at an angular resolution of 1.0″ (820 au) and 0.4″ (330 au), respectively, toward the intermediate-mass class 0 protostellar system Cep E. Results. The CO high-velocity jet emission reveals a central component of ≤400 au diameter associated with high-velocity molecular knots that is also detected in SO, surrounded by a collimated layer of entrained gas. The gas layer appears to be accelerated along the main axis over a length scale δ 0 ~ 700 au, while its diameter gradually increases up to several 1000 au at 2000 au from the protostar. The jet is fragmented into 18 knots of mass ~10 −3 M ⊙ , unevenly distributed between the northern and southern lobes, with velocity variations up to 15 km s −1 close to the protostar. This is well below the jet terminal velocities in the northern (+ 65 km s −1 ) and southern (−125 km s −1 ) lobes. The knot interval distribution is approximately bimodal on a timescale of ~50–80 yr, which is close to the jet-driving protostar Cep E-A and ~150–20 yr at larger distances >12″. The mass-loss rates derived from knot masses are steady overall, with values of 2.7 × 10 −5 M ⊙ yr −1 and 8.9 × 10 −6 M ⊙ yr −1 in the northern and southern lobe, respectively. Conclusions. The interaction of the ambient protostellar material with high-velocity knots drives the formation of a molecular layer around the jet. This accounts for the higher mass-loss rate in the northern lobe. The jet dynamics are well accounted for by a simple precession model with a period of 2000 yr and a mass-ejection period of 55 yr. 

We present an overview of GProM, a generic provenance middleware for relational databases. The sys- tem supports diverse provenance and annotation management tasks through query instrumentation, i.e., compiling a declarative frontend language with provenance-specific features into the query language of a backend database system. In addition to introducing GProM, we also discuss research contributions related to GProM including the first provenance model and capture mechanism for transaction prove- nance, a unified framework for answering why- and why-not provenance questions, and provenance- aware query optimization. Furthermore, by means of the example of post-mortem debugging of transac- tions, we demonstrate how novel applications of provenance are made possible by GProM.