Clinical results following robotic navigation guidance for sacroiliac joint fusion in 36 patients

OBJECTIVE In this study, the authors aimed to describe a new technique of sacroiliac joint (SIJ) fusion using a robotic navigation guidance system and to document clinical results with patient-reported visual analog scale (VAS) scores. METHODS Patients diagnosed with SIJ dysfunction were surgically treated using 2 hydroxyapatite (HA)–coated, threaded screws with the aid of the robotic navigation system. In a total of 36 patients, 51 SIJs were fused during the study period. Patients’ VAS scores were used to determine clinical improvement in pain. Postoperative imaging at routine intervals during the follow-up period was also performed for assessment of radiological fusion. In addition, complication events were recorded, including reoperations. RESULTS All 36 patients had successful fusion evidenced by both CT and clinical assessment at the final follow-up. Two patients underwent reoperation because of screw loosening. The mean preoperative VAS score was 7.2 ± 1.1, and the mean 12-month postoperative VAS score was 1.6 ± 1.46. This difference was statistically significant (p < 0.05) and demonstrated a substantial clinical improvement in pain. CONCLUSIONS Robotic navigation–assisted SIJ fusion using 2 HA-coated, threaded screws placed across the joint was an acceptable technique that demonstrated reliable clinical results with a significant improvement in patient-reported VAS pain scores.

Robotic external ventricular drain placement for acute neurosurgical care in low-resource settings: feasibility considerations and a prototype design

OBJECTIVE Emergency neurosurgical care in lower-middle-income countries faces pronounced shortages in neurosurgical personnel and infrastructure. In instances of traumatic brain injury (TBI), hydrocephalus, and subarachnoid hemorrhage, the timely placement of external ventricular drains (EVDs) strongly dictates prognosis and can provide necessary stabilization before transfer to a higher-level center of care that has access to neurosurgery. Accordingly, the authors have developed an inexpensive and portable robotic navigation tool to allow surgeons who do not have explicit neurosurgical training to place EVDs. In this article, the authors aimed to highlight income disparities in neurosurgical care, evaluate access to CT imaging around the world, and introduce a novel, inexpensive robotic navigation tool for EVD placement. METHODS By combining the worldwide distribution of neurosurgeons, CT scanners, and gross domestic product with the incidence of TBI, meningitis, and hydrocephalus, the authors identified regions and countries where development of an inexpensive, passive robotic navigation system would be most beneficial and feasible. A prototype of the robotic navigation system was constructed using encoders, 3D-printed components, machined parts, and a printed circuit board. RESULTS Global analysis showed Montenegro, Antigua and Barbuda, and Seychelles to be primary candidates for implementation and feasibility testing of the novel robotic navigation system. To validate the feasibility of the system for further development, its performance was analyzed through an accuracy study resulting in accuracy and repeatability within 1.53 ± 2.50 mm (mean ± 2 × SD, 95% CI). CONCLUSIONS By considering regions of the world that have a shortage of neurosurgeons and a high incidence of EVD placement, the authors were able to provide an analysis of where to prioritize the development of a robotic navigation system. Subsequently, a proof-of-principle prototype has been provided, with sufficient accuracy to target the ventricles for EVD placement.

Fault-Tolerant Partition Resolvability of Cyclic Networks

Graph invariants provide an amazing tool to analyze the abstract structures of networks. The interaction and interconnection between devices, sensors, and service providers have opened the door for an eruption of mobile over the web applications. Structure of web sites containing number of pages can be represented using graph, where web pages are considered to be the vertices, and an edge is a link between two pages. Figuring resolving partition of the graph is an intriguing inquest in graph theory as it has many applications such as sensor design, compound classification in chemistry, robotic navigation, and Internet network. The partition dimension is a graph parameter akin to the concept of metric dimension, and fault-tolerant partition dimension is an advancement in the line of research of partition dimension of the graph. In this paper, we compute fault-tolerant partition dimension of alternate triangular cycle, mirror graph, and tortoise graphs.

Fast Transfer Navigation for Autonomous Robots

Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.

Image-Guided Robotics for Standardized and Automated Biopsy and Ablation

Image-guided robotics for biopsy and ablation aims to minimize procedure times, reduce needle manipulations, radiation, and complications, and enable treatment of larger and more complex tumors, while facilitating standardization for more uniform and improved outcomes. Robotic navigation of needles enables standardized and uniform procedures which enhance reproducibility via real-time precision feedback, while avoiding radiation exposure to the operator. Robots can be integrated with computed tomography (CT), cone beam CT, magnetic resonance imaging, and ultrasound and through various techniques, including stereotaxy, table-mounted, floor-mounted, and patient-mounted robots. The history, challenges, solutions, and questions facing the field of interventional radiology (IR) and interventional oncology are reviewed, to enable responsible clinical adoption and value definition via ergonomics, workflows, business models, and outcome data. IR-integrated robotics is ready for broader adoption. The robots are coming!

Human inspired robotic path planning and heterogeneous robotic mapping

<p>One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains.</p>

Human inspired robotic path planning and heterogeneous robotic mapping

<p>One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains.</p>