A Berry Picking Robot With A Hybrid Soft-Rigid Arm: Design and Task Space Control

Uppalapati, Naveen Kumar; Walt, Benjamin Thomas; Havens, Aaron; Mahdian, Armeen; Chowdhary, Girish; Krishnan, Girish

doi:10.15607/rss.2020.xvi.027

Cited by 19 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CONCLUSION Accurate 3D shape reconstruction of a soft continuum arm is essential for applications that require accurate interactions with the environment. The lack of accurate cost effective solutions for this problem makes it difficult to deploy autonomous SCAs in real world scenarios [5]. In this paper, a vision based approach for estimating the SCA's shape was proposed.…”

Section: Results and Analysismentioning

confidence: 99%

“…The work presented in this paper will be built upon to develop autonomous capabilities for SCA's that would be useful in applications such as fruit harvesting [5], robotic care-giving, and surgery. More specifically, it can be applied as a feedback loop for controlling the SCA's end-effector to a desired position.…”

Section: Results and Analysismentioning

confidence: 99%

“…The material elasticity combined with inherent damping enables safe and adaptable interaction with the surroundings. SCAs can be useful for several applications such as surgery [2], agriculture [5], search and rescue [6] etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization

AlBeladi¹,

Krishnan²,

Belabbas³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Interest in soft continuum arms has increased as their inherent material elasticity enables safe and adaptive interactions with the environment. However to achieve full autonomy in these arms, accurate three-dimensional shape sensing is needed. Vision-based solutions have been found to be effective in estimating the shape of soft continuum arms. In this paper, a vision-based shape estimator that utilizes a geometric strain based representation for the soft continuum arm's shape, is proposed. This representation reduces the dimension of the curved shape to a finite set of strain basis functions, thereby allowing for efficient optimization for the shape that best fits the observed image. Experimental results demonstrate the effectiveness of the proposed approach in estimating the end effector with accuracy less than the soft arm's radius. Multiple basis functions are also analyzed and compared for the specific soft continuum arm in use.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization

AlBeladi¹,

Krishnan²,

Belabbas³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our system, called CropFollow, uses monocular RGB images from an on-board front-facing camera to steer the robot to autonomously traverse in between crop rows in harsh, visually cluttered, uneven, and variable real-world agricultural fields. Robust and reliable autonomous navigation of such under-canopy robots has the potential to enable a number of practical and scientific applications: High-throughput plant phenotyping [43,37,68,66,58,25], ultra-precise pesticide treatments, mechanical weeding [41], plant manipulation [17,61], and cover crop planting [64,62] Such applications are not possible with overcanopy larger tractors and UAVs, and are crucial for increasing agricultural sustainability [55,22].Autonomous row-following is a foundational capability for robots that need to navigate between crop rows in agricultural fields. Such robots cannot rely on RTK (Real-Time Kinematic)-GPS [21] based methods which are used for overthe-canopy autonomy (e.g.…”

mentioning

confidence: 99%

Learned Visual Navigation for Under-Canopy Agricultural Robots

Sivakumar

Modi

Gasparino

et al. 2021

Robotics: Science and Systems XVII

Self Cite

View full text Add to dashboard Cite

This paper describes a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for overthe-canopy drones or larger agricultural equipment. However, autonomously navigating them under the canopy presents a number of challenges: unreliable GPS and LiDAR, high cost of sensing, challenging farm terrain, clutter due to leaves and weeds, and large variability in appearance over the season and across crop types. We address these challenges by building a modular system that leverages machine learning for robust and generalizable perception from monocular RGB images from low-cost cameras, and model predictive control for accurate control in challenging terrain. Our system, CropFollow, is able to autonomously drive 485 meters per intervention on average, outperforming a state-of-the-art LiDAR based system (286 meters per intervention) in extensive field testing spanning over 25 km. I. INTRODUCTIONThis paper describes the design of a visually-guided navigation system for compact, low-cost, under-canopy agricultural robots for commodity row-crops (corn, soybean, sugarcane etc), such as that shown in Figure 1. Our system, called CropFollow, uses monocular RGB images from an on-board front-facing camera to steer the robot to autonomously traverse in between crop rows in harsh, visually cluttered, uneven, and variable real-world agricultural fields. Robust and reliable autonomous navigation of such under-canopy robots has the potential to enable a number of practical and scientific applications: High-throughput plant phenotyping [43,37,68,66,58,25], ultra-precise pesticide treatments, mechanical weeding [41], plant manipulation [17,61], and cover crop planting [64,62] Such applications are not possible with overcanopy larger tractors and UAVs, and are crucial for increasing agricultural sustainability [55,22].Autonomous row-following is a foundational capability for robots that need to navigate between crop rows in agricultural fields. Such robots cannot rely on RTK (Real-Time Kinematic)-GPS [21] based methods which are used for overthe-canopy autonomy (e.g. for drones, tractors, and combine Project website with data and videos: https://ansivakumar.github.io/ learned-visual-navigation/. Correspondence to {av7,girishc}@illinois.edu. * Girish Chowdhary and Saurabh Gupta contributed equally and are listed alphabetically.

show abstract

“…In order to demonstrate our physics-informed reconstruction method, we consider a slender and soft robotic arm integrated within a videotracking environment. We focus primarily on Fiber Reinforced Elastomeric Enclosures (FREEs [25]), due to their high degree of shape reconfigurability, versatility, cost effectiveness and overall promise for applications, from manipulation [23] to agriculture [49]. FREEs are pneumatic slender actuators embedding in their elastomeric shell inextensible fibers which, depending on the winding pattern and upon pressurization, lead to bending, twisting or elongation.…”

mentioning

confidence: 99%

A physics-informed, vision-based method to reconstruct all deformation modes in slender bodies

Kim¹,

Chang²,

Shih³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This paper is concerned with the problem of estimating (interpolating and smoothing) the shape (pose and the six modes of deformation) of a slender flexible body from multiple camera measurements. This problem is important in both biology, where slender, soft, and elastic structures are ubiquitously encountered across species, and in engineering, particularly in the area of soft robotics. The proposed mathematical formulation for shape estimation is physics-informed, based on the use of the special Cosserat rod theory whose equations encode slender body mechanics in the presence of bending, shearing, twisting and stretching. The approach is used to derive numerical algorithms which are experimentally demonstrated for fiber reinforced and cable-driven soft robot arms. These experimental demonstrations show that the methodology is accurate (<5 mm error, three times less than the arm diameter) and robust to noise and uncertainties.

show abstract

A Berry Picking Robot With A Hybrid Soft-Rigid Arm: Design and Task Space Control

Cited by 19 publications

References 0 publications

Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization

Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization

Learned Visual Navigation for Under-Canopy Agricultural Robots

A physics-informed, vision-based method to reconstruct all deformation modes in slender bodies

Contact Info

Product

Resources

About