A review on machine learning in flexible surgical and interventional robots: Where we are and where we are going

Minimally Invasive Procedures (MIPs) emerged as an alternative to more invasive surgical approaches, offering patient benefits such as smaller incisions, less pain, and shorter hospital stay. In one class of MIPs, where natural body lumens or small incisions are used to access deeper anatomical locations, Flexible Surgical and Interventional...

A self-guided approach for navigation in a minimalistic foraging robotic swarm

We present a biologically inspired design for swarm foraging based on ant’s pheromone deployment, where the swarm is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and the swarm is fully decentralized and needs no infrastructure in place. Additionally, the system only requires...

Autonomous Navigation for Robot-Assisted Intraluminal and Endovascular Procedures: A Systematic Review

Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and vessels. As navigation through lumens and vessels is quite complex, interest grows to establish autonomous navigation techniques for IP and EI for reaching the target...

Learning Interactively to Resolve Ambiguity in Sensor Policy Fusion for Robot

This work applies interactive imitation learning for the navigation of a mobile robot. The algorithm"Learning Interactively to Resolve Ambiguity in Sensor Policy Fusion" (LIRA-SPF) is introduced in the field of machine learning for robot navigation. This algorithm extends on existing methods by allowing the ambiguity-free fusion of existing...

Where to go next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require...

Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment....

Safe and natural navigation in dynamic environments, learned from human behavior

Social Navigation is the task of robot motion planning in an environment shared with humans.This is an especially hard sub-problem of motion planning because the planner has to dealwith a dynamic, continuous and unpredictable environment. We present a local motionplanner, namely Neural Network Model Predictive Control, for autonomous ground...

Design, Development and Testing of a Navigator to Guide Minimally Invasive Osteotomy Surgery

Corrective osteotomy is the treatment for malunion of bone segments, which is required after insufficient fracture management. To perform this procedure adequately, 3D imaging is used to make a virtual plan of the osteotomy. In order to transfer this virtual plan to the patient in the operating room, a navigation method is required. Existing...