MBLDP-R: A Multiple Biomolecules Based Rapid Life Detection Protocol Embedded in a Rover Scientific Subsystem for Soil Sample Analysis

Zaman, Akib; Ashraf, Fardeen; Khan, Haseena; Noshin, Faria; Samir, Oiliullah; Rayhan, A.; Nazifa, Sadia Nur; Chowdhury, Hafsah Mahzabin; Rahman, Md. Lutfor

doi:10.21203/rs.3.rs-1263964/v1

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…A heterogeneous UGV-UAV team of robots could leverage the strengths of both platforms; the high vantage viewpoint afforded by the aerial drones and the close access to the ground of the rovers. Forest rovers may be adapted to provide a range of ground-based measurements such as physical samples of the soil ( Zaman et al, 2022 ). They could pause to take reliable pollen or greenhouse gas samples close to the ground ( Grau Ruiz and O’Brolchain, 2022 ; Gupta et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

An embarrassingly simple approach for visual navigation of forest environments

et al. 2023

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Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform’s camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions.

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Section: Discussionmentioning

confidence: 99%