Continuum robots are increasingly used in minimally invasive surgeries. To date, the concentric tube mechanism and the cable-driven mechanism have been two prevalent mechanisms for constructing continuum robots. As these two mechanisms complement each other, it is worth exploring the possibility of combining them together. In this paper, we investigate the dexterity of three continuum robots combining both mechanisms. Indices based on the concept of orientability are introduced to analyze the distribution of the dexterity. A Monte Carlo method is used to calculate the dexterity distribution across the workspace. Particularly, directional dexterity indices are put forward to describe the dexterity along different axes. Results imply that evenly allocating degrees of freedom (DOFs) among the segments achieves the best workspace and dexterity. Otherwise, assigning more DOFs to the proximal segment tends to enlarge the workspace and adding more DOFs to the distal segment tends to improve the dexterity. In addition, the dexterity along different axes can vary significantly and thus requires special attention when applying the robot to specific surgical procedures.

In this paper, a system that uses an algorithm for target detection and navigation and a multirotor Unmanned Aerial Vehicle (UAV) for finding a ground target and inspecting it closely is presented. The system can also be used for accurate and safe delivery of payloads or spot spraying applications in site-specific crop management. A downward-looking camera attached to a multirotor is used to find the target on the ground. The UAV descends to the target and hovers above the target for a few seconds to inspect the target. A high-level decision algorithm based on an OODA (observe, orient, decide, and act) loop was developed as a solution to address the problem. Navigation of the UAV was achieved by continuously sending local position messages to the autopilot via Mavros. The proposed system performed hovering above the target in three different stages: locate, descend, and hover. The system was tested in multiple trials, in simulations and outdoor tests, from heights of 10 m to 40 m. Results show that the system is highly reliable and robust to sensor errors, drift, and external disturbance.

A coverage algorithm is an algorithm that deploys a strategy as to how to cover all points in terms of a given area using some set of sensors. In the past decades a lot of research has gone into development of coverage algorithms. Initially, the focus was coverage of structured and semi-structured indoor areas, but with time and development of better sensors and introduction of GPS, the focus has turned to outdoor coverage. Due to the unstructured nature of an outdoor environment, covering an outdoor area with all its obstacles and simultaneously performing reliable localization is a difficult task. In this paper, two path planning algorithms suitable for solving outdoor coverage tasks are introduced. The algorithms take into account the kinematic constraints of an under-actuated car-like vehicle, minimize trajectory curvatures, and dynamically avoid detected obstacles in the vicinity, all in real-time. We demonstrate the performance of the coverage algorithm in the field by achieving 95% coverage using an autonomous tractor mower without the aid of any absolute localization system or constraints on the physical boundaries of the area.

This paper presents an enhanced algorithm for matching laser scan maps using histogram correlations. The histogram representation effectively summarizes a map's salient features such that pairs of maps can be matched efficiently without any prior guess as to their alignment. The histogram matching algorithm has been enhanced in order to work well in outdoor unstructured environments by using entropy metrics, weighted histograms and proper thresholding of quality metrics. Thus our large-scale scan-matching SLAM implementation has a vastly improved ability to close large loops in real-time even when odometry is not available. Our experimental results have demonstrated a successful mapping of the largest area ever mapped to date using only a single laser scanner. We also demonstrate our ability to solve the lost robot problem by localizing a robot to a previously built map without any prior initialization.

This paper describes the development and testing of a robotic system for charging blast holes in underground mining. The automation system supports four main tactical functions: detection of blast holes; teleoperated arm pose control; automatic arm pose control; and human-in-the-loop visual servoing. We present the system architecture, and analyze the major components, Hole detection is crucial for automating the process, and we discuss theoretical and practical aspects in detail. The sensors used are laser range finders and cameras installed in the end effector. For automatic insertion, we consider image processing techniques to support visual servoing the tool to the hole. We also discuss issues surrounding the control of heavy-duty mining manipulators, in particular, friction, stiction, and actuator saturation.