A negative imaginary robust formation control scheme for networked multi-tilt tricopters utilizing an inner-loop sliding-mode control technique ?

This paper proposes a robust formation control scheme for networked multi-tilt tricopter UAVs utilizing the Negative Imaginary (NI) and Positive Real (PR) theory. A Sliding Mode Control (SMC) scheme is designed for a multi-tilt tricopter to ensure stable hovering at a desired height. Then, a modified Subspace-based system identification algorithm is devised to identify a six-by-six NI model of the inner-loop-SMC-controlled tricopter in the continuous-time domain by exploiting the Laguerre filter. A two-loop formation control scheme has been developed for networked multi-tilt tricopters where the inner loop of each tricopter applies the SMC scheme, and the outer loop implements a distributed output feedback controller that satisfies the ‘mixed’ Strictly NI (SNI) + Strictly PR (SPR) system properties. Subsequently, we have established the robustness of the proposed scheme against NI/PR-type uncertainties and sudden loss of agents. The eigenvalue loci (also known as characteristic loci) technique is used instead of the Lyapunov-based approach to prove the asymptotic stability of the formation control scheme. An in-depth simulation case study was performed on a group of six inner-loop-SMC-controlled multi-tilt tricopters connected via a network to achieve a formation control mission, even in the presence of uncertainties

Properties of interconnected negative imaginary systems and extension to formation‐containment control of networked multi‐UAV systems with experimental validation results

This paper extends the properties of a positive feedback interconnection of two negative imaginary (NI) systems to multi-agent NI systems and proposes a new formation-containment control methodology relying on the characteristic loci technique. Inspired by recent applications of NI and passivity-based control techniques in the multi-agent systems (MAS) domain, a new formation-tracking and containment control scheme is developed for a class of networked multi-UAV systems. The proposed scheme offers a two-stage and two-loop control configuration where the inner loop uses a cascaded PID controller to ensure stable hovering of the UAVs, and the outer loop deploys a distributed “mixed” SNI and strictly passive controller to achieve the formation-containment objectives. This scheme works with a dynamic output feedback control strategy; hence, it offers advantages when the full-state measurement is not possible. In contrast to the well-known Lyapunov theory-based cooperative control schemes, the present one exploits the characteristic loci technique to prove the formation-tracking and containment phenomena theoretically. The paper also provides experimental validation results on a fleet of Crazyflie 2.1 nano quadcopters.<br/

Model predictive control of connected spacecraft formation.

In this contribution the authors discuss the application of Model Predictive Control (MPC) to achieve a connected network formation of spacecrafts. A set of three spacecrafts are used to achieve in-plane formation which are initially in a connected network. Two scenarios including formation control and formation control with collision avoidance in a leader-follower configuration is addressed through simulation studies. The aspect of MPC stability and network connectivity is also addressed briefly in the context of formation control

Detecting and tracking the position of suspicious objects using vision system.

Vision-based object tracking is crucial for both civil and military applications. A range of hazards to cyber safety, vital infrastructure, and public privacy are posed by the rise of drones, or unmanned aerial vehicles (UAV). As a result, identifying suspicious drones/UAV is a serious issue that has attracted attention recently. The key focus of this research is to develop a unique virtual coloured marker based tracking algorithm to recognise and predict the pose of a detected object within the camera field-of-view. After detecting the object, proposed method begins by determining the area of detected object as reference-contour. Following that, a Virtual-Bounding Box (V-BB) is developed over the reference-contour by meeting the minimum area of contour criteria. In order to track and estimate the precise location of the detected object in two-dimensions during observations, a Virtual Dynamic Crossline with a Virtual Static Graph (VDC-VSG) was constructed to follow the motion of V-BB, which is considered as a virtual coloured marker. Additionally, the virtual coloured marker helps to avoid issues linked to ambient lighting and chromatic variation. To some extent, it can function efficiently during obstructions like rapid position fluctuations, low resolution and noises etc. The efficacy of the developed algorithm is evaluated by testing with significant number of aerial sequences, including benchmark footage and the outputs were outstanding, with better results. The suggested method will support future industry of computer vision-based intelligent systems. Potential applications of the proposed method includes object detection and analysis applied to the field of security and defence

Tracking and estimation of surgical instrument position and angle in surgical robot using vision system.

A da Vinci robot endoscopic-camera gives surgeons a magnified 2D view of the operating area, but additional time is required to detect and estimate the location of the surgical-instrument during an operation. The main focus and novelty of this research is to develop a new virtual coloured marker-based tracking algorithm for estimating the posture and orientation of the instrument. Initially, the developed algorithm begins by determining the coloured area of the instrument as reference-contour. Followed by a new Virtual-Rotating Bounding Rectangle (V-RBR) created over the reference-contour by meeting the minimum area of contour criteria. Additionally, a new Virtual Dynamic Multi-line Crossbar and a Virtual Static Graph (VDMC-VSG) was constructed to trace the movement of V-RBR, which helps to estimate the pose and angle of the targeted instrument in 2D during observations. V-RBR is considered as virtual coloured marker, it avoids ambient illumination-related difficulties. The proposed approach performed excellently in Gazebo-simulation and the overall accuracy is 91.3 % obtained by comparing with Robot Operating System (ROS)-based Transform measuring system, which uses robot kinematics

Vision based relative position estimation in surgical robotics.

Teleoperation-based Robotic-Assisted Minimally In-vasive Surgery (RAMIS) has gained immense popularity in medical field. However, limited physical interaction between the surgeon and patient poses a significant challenge. In RAMIS, the surgeon operates the robotic system remotely, which can diminish the personal connection and raise concerns about immediate responsiveness to unforeseen situations. Additionally, patients may perceive RAMIS as riskier due to potential technological failures and a lack of direct surgeon control. Surgeons have identified accidental clashes between surgical instruments and tissues as a critical issue. This work presents a technique that measures the distance between a surgical tool and tissue by extracting feature points from a Static Virtual Marker (SVM) and employing a classic feature detection algorithm Fast Oriented and Rotated Brief (ORB). Using a customized surgical robot and a ROS-based transform measurement system, this approach was successfully validated in the Gazebo simulation environment, offering safer surgical operations