ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery.

INTRODUCTION: Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and degradation at unpredictable times. Immediate medical care at these austere locations requires semi-autonomous teleoperated systems, which enable the completion of medical procedures even under interrupted networks while isolating the medics from the dangers of the battlefield. However, to achieve autonomy for complex surgical and critical care procedures, robots require extensive programming or massive libraries of surgical skill demonstrations to learn effective policies using machine learning algorithms. Although such datasets are achievable for simple tasks, providing a large number of demonstrations for surgical maneuvers is not practical. This article presents a method for learning from demonstration, combining knowledge from demonstrations to eliminate reward shaping in reinforcement learning (RL). In addition to reducing the data required for training, the self-supervised nature of RL, in conjunction with expert knowledge-driven rewards, produces more generalizable policies tolerant to dynamic environment changes. A multimodal representation for interaction enables learning complex contact-rich surgical maneuvers. The effectiveness of the approach is shown using the cricothyroidotomy task, as it is a standard procedure seen in critical care to open the airway. In addition, we also provide a method for segmenting the teleoperator's demonstration into subtasks and classifying the subtasks using sequence modeling. MATERIALS AND METHODS: A database of demonstrations for the cricothyroidotomy task was collected, comprising six fundamental maneuvers referred to as surgemes. The dataset was collected by teleoperating a collaborative robotic platform-SuperBaxter, with modified surgical grippers. Then, two learning models are developed for processing the dataset-one for automatic segmentation of the task demonstrations into a sequence of surgemes and the second for classifying each segment into labeled surgemes. Finally, a multimodal off-policy RL with rewards learned from demonstrations was developed to learn the surgeme execution from these demonstrations. RESULTS: The task segmentation model has an accuracy of 98.2%. The surgeme classification model using the proposed interaction features achieved a classification accuracy of 96.25% averaged across all surgemes compared to 87.08% without these features and 85.4% using a support vector machine classifier. Finally, the robot execution achieved a task success rate of 93.5% compared to baselines of behavioral cloning (78.3%) and a twin-delayed deep deterministic policy gradient with shaped rewards (82.6%). CONCLUSIONS: Results indicate that the proposed interaction features for the segmentation and classification of surgical tasks improve classification accuracy. The proposed method for learning surgemes from demonstrations exceeds popular methods for skill learning. The effectiveness of the proposed approach demonstrates the potential for future remote telemedicine on battlefields.

Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming.

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

Precision technologies are revolutionizing animal agriculture by enhancing the management of animal welfare and productivity. To fully realize the potential benefits of precision livestock farming (PLF), the development and application of digital technologies are needed to facilitate the responsible and sustainable intensification of livestock production over the next several decades. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring audibility in value chains while assuaging concerns associated with labor shortages. In this paper, we analyze the multilayered network of sensors, actuators, communication, and analytics currently in use in PLF. We analyze the various aspects of sensing, communication, networking, and intelligence on the farm leveraging dairy farms as an example system. We also discuss the potential implications of advancements in communication, robotics, and artificial intelligence on the security and welfare of animals.

Sub-GHz In-Body to Out-of-Body Communication Channel Modeling for Ruminant Animals for Smart Animal Agriculture.

Sensors in and around the environment becoming ubiquitous has ushered in the age of smart animal agriculture which has the potential to greatly improve animal health and productivity. The data gathered from sensors dwelling in animal agriculture settings have made farms a part of the IoT space leading to active research in developing efficient communication methodologies for farm networks. This study focuses on the first hop of farm networks where data from inside the body of animals is communicated to a node dwelling outside the body. Novel experimental methods are used to calculate the channel loss at sub-GHz frequencies (100-900 MHz) to characterize the in-body to out-of-body (IBOB) communication channel in large animals. A first-of-its-kind 3D bovine modeling is done with computer vision techniques for detailed morphological features of the animal body to perform Finite Element Method based Electromagnetic simulations. The results of the simulations are experimentally validated to build a complete channel modeling methodology for IBOB animal-body-communication. The 3D bovine model is made available publicly on GitHub. The results illustrate that an IBOB communication channel is realizable from the rumen to the collar of ruminants with [Formula: see text] path loss at sub-GHz frequencies making communication feasible. The developed methodology has been illustrated for ruminants but can also be used for other IBOB studies. An efficient communication architecture can be formed using the channel modeling technique illustrated for IBOB communication in animals paving the way for the design and development of future smart animal agriculture systems.

Invited review: Sensor technologies for real-time monitoring of the rumen environment.

Quantifying digestive and fermentative processes within the rumen environment has been the subject of decades of research; however, our existing research methodologies preclude time-sensitive and spatially explicit investigation of this system. To better understand the temporal and spatial dynamics of the rumen environment, real-time and in situ monitoring of various chemical and physical parameters in the rumen through implantable microsensor technologies is a practical solution. Moreover, such sensors could contribute to the next generation of precision livestock farming, provided sufficient wireless data networking and computing systems are incorporated. In this review, various microsensor technologies applicable to real-time metabolic monitoring for ruminants are introduced, including the detection of parameters for rumen metabolism, such as pH, temperature, histamine concentrations, and volatile fatty acid concentrations. The working mechanisms and requirements of the sensors are summarized with respect to the selected target parameters. Lastly, future challenges and perspectives of this research field are discussed.

Occupant-centric robotic air filtration and planning for classrooms for Safer school reopening amid respiratory pandemics.

Coexisting with the current COVID-19 pandemic is a global reality that comes with unique challenges impacting daily interactions, business, and facility maintenance. A monumental challenge accompanied is continuous and effective disinfection of shared spaces, such as office/school buildings, elevators, classrooms, and cafeterias. Although ultraviolet light and chemical sprays are routines for indoor disinfection, they irritate humans, hence can only be used when the facility is unoccupied. Stationary air filtration systems, while being irritation-free and commonly available, fail to protect all occupants due to limitations in air circulation and diffusion. Hence, we present a novel collaborative robot (cobot) disinfection system equipped with a Bernoulli Air Filtration Module, with a design that minimizes disturbance to the surrounding airflow and maneuverability among occupants for maximum coverage. The influence of robotic air filtration on dosage at neighbors of a coughing source is analyzed with derivations from a Computational Fluid Dynamics (CFD) simulation. Based on the analysis, the novel occupant-centric online rerouting algorithm decides the path of the robot. The rerouting ensures effective air filtration that minimizes the risk of occupants under their detected layout. The proposed system was tested on a 2 × 3 seating grid (empty seats allowed) in a classroom, and the worst-case dosage for all occupants was chosen as the metric. The system reduced the worst-case dosage among all occupants by 26% and 19% compared to a stationary air filtration system with the same flow rate, and a robotic air filtration system that traverses all the seats but without occupant-centric planning of its path, respectively. Hence, we validated the effectiveness of the proposed robotic air filtration system.

In-body to Out-of-body Communication Channel Modeling for Ruminant Animals for Smart Animal Agriculture.

Continuous real-time health monitoring in animals is essential for ensuring animal welfare. In ruminants like cows, rumen health is closely intertwined with overall animal health. Therefore, in-situ monitoring of rumen health is critical. However, this demands in-body to out-of-body communication of sensor data. In this paper, we devise a method of channel modeling for a cow using experiments and FEM based simulations at 400 MHz. This technique can be further employed across all frequencies to characterize the communication channel for the development of a channel architecture that efficiently exploits its properties.

A robust method for rotation estimation using spherical harmonics representation.

This paper presents a robust method for 3D object rotation estimation using spherical harmonics representation and the unit quaternion vector. The proposed method provides a closed-form solution for rotation estimation without recurrence relations or searching for point correspondences between two objects. The rotation estimation problem is casted as a minimization problem, which finds the optimum rotation angles between two objects of interest in the frequency domain. The optimum rotation angles are obtained by calculating the unit quaternion vector from a symmetric matrix, which is constructed from the two sets of spherical harmonics coefficients using eigendecomposition technique. Our experimental results on hundreds of 3D objects show that our proposed method is very accurate in rotation estimation, robust to noisy data, missing surface points, and can handle intra-class variability between 3D objects.