sCAM: An Untethered Insertable Laparoscopic Surgical Camera Robot

Fully insertable robotic imaging devices represent a promising future of minimally invasive laparoscopic vision. Emerging research efforts in this field have resulted in several proof-of-concept prototypes. One common drawback of these designs derives from their clumsy tethering wires which not only cause operational interference but also reduce camera mobility. Meanwhile, these insertable laparoscopic cameras are manipulated without any pose information or haptic feedback, which results in open loop motion control and raises concerns about surgical safety caused by inappropriate use of force.This dissertation proposes, implements, and validates an untethered insertable laparoscopic surgical camera (sCAM) robot. Contributions presented in this work include: (1) feasibility of an untethered fully insertable laparoscopic surgical camera, (2) camera-tissue interaction characterization and force sensing, (3) pose estimation, visualization, and feedback with sCAM, and (4) robotic-assisted closed-loop laparoscopic camera control. Borrowing the principle of spherical motors, camera anchoring and actuation are achieved through transabdominal magnetic coupling in a stator-rotor manner. To avoid the tethering wires, laparoscopic vision and control communication are realized with dedicated wireless links based on onboard power. A non-invasive indirect approach is proposed to provide real-time camera-tissue interaction force measurement, which, assisted by camera-tissue interaction modeling, predicts stress distribution over the tissue surface. Meanwhile, the camera pose is remotely estimated and visualized using complementary filtering based on onboard motion sensing. Facilitated by the force measurement and pose estimation, robotic-assisted closed-loop control has been realized in a double-loop control scheme with shared autonomy between surgeons and the robotic controller.The sCAM has brought robotic laparoscopic imaging one step further toward less invasiveness and more dexterity. Initial ex vivo test results have verified functions of the implemented sCAM design and the proposed force measurement and pose estimation approaches, demonstrating the technical feasibility of a tetherless insertable laparoscopic camera. Robotic-assisted control has shown its potential to free surgeons from low-level intricate camera manipulation workload and improve precision and intuitiveness in laparoscopic imaging

Aerial Robotics for Inspection and Maintenance

Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

Experimental analysis of using radar as an extrinsic sensor for human-robot collaboration

Collaborative robots are expected to be an integral part of driving the fourth industrial revolution that is soon expected to happen. In human-robot collaboration, the robot and a human share a common workspace and work on a common task. Here the safety of a human working with the robot is of utmost importance. A collaborative robot usually consists of various sensors to ensure the safety of a human working with the robot. This research mainly focuses on establishing a safe environment for a human working alongside a robot by mounting an FMCW radar as an extrinsic sensor, through which the workspace of the robot is monitored. A customized tracking algorithm is developed for the sensor used in this study by including a dynamically varying gating threshold, and information about consecutive missed detections to track and localize the human around the workspace of the robot. The performance of the proposed system in successfully establishing a safe human-robot collaboration is examined across a few scenarios that arise when a single human operator is working alongside a robot, with the radar operating in different modes. An OptiTrack Motion Capture System is used as ground truth to validate the efficacy of the proposed system

Kinova modular robot arms for service robotics applications

This article presents Kinova's modular robotic systems, including the robots JACO2 and MICO2, actuators and grippers. Kinova designs and manufactures robotics platforms and components that are simple, sexy and safe under two business units: Assistive Robotics empowers people living with disabilities to push beyond their current boundaries and limitations while Service Robotics empowers people in industry to interact with their environment more efficiently and safely. Kinova is based in Boisbriand, Québec, Canada. Its technologies are exploited in over 25 countries and are used in many applications, including as service robotics, physical assistance, medical applications, mobile manipulation, rehabilitation, teleoperation and in research in different areas such as computer vision, artificial intelligence, grasping, planning and control interfaces. The article describes Kinova's hardware platforms, their different control modes (position, velocity and torque), control features and possible control interfaces. Integration to other systems and application examples are also presented