82 research outputs found
Autonomous Surgical Robotics at Task and Subtask Levels
The revolution of minimally invasive procedures had a significant influence on surgical practice, opening the way to laparoscopic surgery, then evolving into robotics surgery. Teleoperated master-slave robots, such as the da Vinci Surgical System, has become a standard of care during the last few decades, performing over a million procedures
per year worldwide. Many believe that the next big step in the evolution of surgery is partial automation, which would ease the cognitive load on the surgeon, making them possible to pay more attention on the critical parts of the intervention. Partial and sequential introduction and increase of autonomous capabilities could provide a safe way towards Surgery 4.0. Unfortunately, autonomy in the given environment, consisting mostly of soft organs, suffers from grave difficulties. In this chapter, the current research directions of subtask automation in surgery are to be presented, introducing the recent advances in motion planning, perception, and human-machine interaction, along with the limitations of the task-level autonomy
Experimental System Identification, Feed-Forward Control, and Hysteresis Compensation of a 2-DOF Mechanism
A Host-Based Intrusion Detection System Using Architectural Features to Improve Sophisticated Denial-of-Service Attack Detections
Automatic Text Document Summarization Using Graph Based Centrality Measures on Lexical Network
Effect of diluting agent Al2O3 on the combustion velocity of NiO/Al aliminothermic system and apparent activation energy calculations
11scopu
Three-dimensional Path Planning for Underwater Vehicles Based on an Improved Ant Colony Optimization Algorithm
Three-dimensional path planning for underwater vehicles is an important problem that focuses on optimizing the route
with consideration of various constraints in a complex underwater environment. In this paper, an improved ant colony
optimization (IACO) algorithm based on pheromone exclusion is proposed to solve the underwater vehicle 3D path
planning problem. The IACO algorithm can balance the tasks of exploration and development in the ant search path, and
enable the ants in the search process to explore initially and develop subsequently. Then, the underwater vehicle can find
the safe path by connecting the chosen nodes of the 3D mesh while avoiding the threat area. This new approach can
overcome common disadvantages of the basic ant colony algorithm, such as falling into local extremum, poor quality,
and low accuracy. Experimental comparative results demonstrate that this proposed IACO method is more effective and
feasible in underwater vehicle 3D path planning than the basic ACO model
- …