Designing a controller for the cooperative transport of a payload using quadcopter-type unmanned aerial vehicles (UAVs) is a very challenging task in control theory because these vehicles are underactuated mechanical systems. This paper presents a novel robust adaptive formation control design for the cooperative transport of a suspended payload by ropes using two underactuated quadcopters in the presence of external disturbances and parametric uncertainties. The structure of the proposed controller is divided into two subsystems: fully actuated and underactuated. An integral sliding mode adaptive control strategy is proposed for the fully actuated subsystem, and for the underactuated subsystem, an adaptive control strategy based on the combination of Backstepping and sliding mode is proposed. Then, the control parameters of the sliding surfaces of both control subsystems are adaptively tuned by a neural network. In addition, to improve the robustness of the proposed controller, a disturbance observer is incorporated to estimate and compensate the lumped disturbances. The asymptotic stability of the cooperative transport system is verified with the Lyapunov theorem. Finally, numerical simulations are performed in MATLAB/Simulink environment, and the results show that the proposed controller successfully transports the payload safely and without oscillations. Moreover, the desired formation pattern is maintained throughout the flight task, even with external disturbances and parametric uncertainties.

Teleoperated robotics in recent years has proven to be valuable support in EOD tasks; a remarkable improvement in the systems that control these robots has been the Natural User Interfaces (NUI); however, the research that implements this type of system does not focus on the stability of the robotic arm movements, necessary for this type of applications due to the danger of working with explosives. In this paper, we propose the implementation of an Optimal Signal Processing for a NUI interface based on the Leap Motion (LM) controller. The main objective of this research is to correctly identify the intentional movements of the operator, achieve high stability of the robotic gripper and suppress the physiological tremors from the hand of the operator, considering not to increase the mental workload and not decrease the usability of the system. The signal processing proposed in this paper is composed of three filtering algorithms: Kalman, FIR, and moving average with a threshold. In addition, the obtained results are compared with the most representative processing of recent research using LM for robotic arm control. To evaluate and validate the proposed signal processing, a target path tracking test, a stability analysis of the robotic gripper, and a performance analysis in the execution of Pick and Place tasks, NASA-TLX and SUS questionnaires are developed. Finally, the proposed Optimal Signal processing is implemented in the DOBOT-MAGICIAN and tested by police officers of the EOD Unit-Arequipa (UDEX-AQP); the results indicate a reduction of the average Vibration of 31.61% and the Target Path Tracking error of 67.57%.

This article presents a new robust formation control approach for a system of multiple Quadcopter Unmanned Aerial Vehicle (QUAV) in complex and challenging flight scenarios with large lumped uncertainties. The aim is to achieve fast convergence to the desired formation pattern, high tracking accuracy, and solid robustness. To this end, a novel finite-time nonlinear control strategy, Adaptive Backstepping Recursive Integral Non-singular Fast Terminal Sliding Mode Control (ABRINFTSMC), is designed. The formation control system of each QUAV, based on ABRINFTSMC, is divided into an underactuated position subsystem for the outer loop and a fully actuated attitude subsystem for the inner loop. A Robust Finite-Time Distributed Consensus Formation Control Protocol (RFDCFCP) is developed for the outer loop to ensure the convergence of the aircraft to the desired formation pattern in a finite time and maintain its alignment throughout the flight mission. For the inner loop, a Robust Finite-Time Attitude Stabilization Control (RFASC) is developed to allow the aircraft to track the desired attitude generated by the outer loop in a finite time. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory to ensure convergence of all formation tracking errors to the origin in finite time. Finally, numerical simulations are performed under different scenarios to validate the performance of the proposed method, and a comprehensive comparative study with recent formation control approaches is carried out. The results confirm that the proposed formation control exhibits superior performance regarding fast convergence, tracking accuracy, and robustness, crucial features for multi-QUAV system flight formation.

Explosive ordnance disposal (EOD) operations are hazardous due to the volatile and sensitive nature of these devices. EOD robots have improved these tasks, but their high cost limits accessibility for security institutions that do not have sufficient funds. This article presents the design, implementation, and validation of a low-cost EOD robot named JVC-02, specifically designed for use in explosive hazardous environments to safeguard the safety of police officers of the Explosives Disposal Unit (UDEX) of Arequipa, Peru. To achieve this goal, the essential requirements for this type of robot were compiled, referencing the capabilities of Rescue Robots from RoboCup. Additionally, the Quality Function Deployment (QFD) methodology was used to identify the needs and requirements of UDEX police officers. Based on this information, a modular approach to robot design was developed, utilizing commercial off-the-shelf components to facilitate maintenance and repair. The JVC-02 was integrated with a 5-DoF manipulator and a two-finger mechanical gripper to perform dexterity tasks, along with a tracked locomotion mechanism, which enables effective movement, and a three-camera vision system to facilitate exploration tasks. Finally, field tests were conducted in real scenarios to evaluate and experimentally validate the capabilities of the JVC-02 robot, assessing its mobility, dexterity, and exploration skills. Additionally, real EOD missions were carried out in which UDEX agents intervened and controlled the robot. The results demonstrate that the JVC-02 robot possesses strong capabilities for real EOD applications, excelling in intuitive operation, low cost, and ease of maintenance.

To improve the efficiency of human-robot interaction (HRI) in hazardous ordnance manipulation tasks, the design of an advanced control system for a robotic manipulator needs to be investigated. This article presents a robust finite-time adaptive nonlinear control system designed to address the trajectory tracking control of explosive ordnance disposal (EOD) robotic manipulator, considering the actuator dynamics and subject to external disturbances and uncertainties. First, the theoretical design of the controller is developed. In order to achieve fast convergence, high tracking accuracy, and strong robustness, which are fundamental features in EOD applications, a Backstepping Fast Terminal Sliding Mode Control (BFTSMC) strategy is proposed. The sliding surface coefficients are adaptively tuned in real-time by a neural network, where the backpropagation algorithm updates the neural network weight. The combination of the BFTSMC strategy and the adaptive neural network scheme results in the Adaptive Backstepping Fast Terminal Sliding Mode Control (ABFTSMC) strategy, achieving improved control system performance. The stability of the closed-loop control system is demonstrated using Lyapunov theory to guarantee the convergence of tracking errors to the origin in finite time. Next, a detailed description of the hardware system, encompassing sensors, actuators, and controller boards, is provided for the practical implementation of the ABFTSMC strategy. Finally, the proposed control system is experimentally validated through several tests on a real EOD robotic manipulator, and an extensive comparison analysis with other control approaches is performed. The results evidence that the ABFTSMC strategy demonstrates high robustness, fast convergence in finite time, and good tracking accuracy, which are crucial qualities in real-world EOD applications.

The advancement of multimodal interfaces aims to provide an intuitive user interface to improve the performance of various tasks. Teleoperated robotics in Explosive Ordnance Disposal (EOD) tasks require the operator to perform complex maneuvering tasks to control a robotic arm. Problems such as loss of depth perception, degradation of visual perception, system delay, and a high mental workload in the control of the robotic elements make these tasks require extensive training periods, extensive knowledge of the robot operation and demand a great effort from the operator to perform a task efficiently and avoid catastrophic situations in handling explosive packages. To solve this, a multimodal interface is proposed based on the creation of a virtual operating environment, where the operator uses a combination of three interfaces: a visual interface through a Virtual Reality Head Mounted Display (VRHMD), a natural user interface (NUI) and a predictive display based interface. The proposed system is evaluated with the participation of thirteen agents with experience in explosive ordnance disposal tasks; the results obtained are divided into objective and subjective results through time measurements of task completion, success rate, usability through System Usability Scale questionnaire (SUS), mental workload through NASA Task Load Index questionnaire (NASA TLX) in Pick and Place tasks, which constitute the primary task type in EOD robotics tasks. The proposed multimodal interface is proven to have a considerably higher efficiency than the conventional keyboard and monitor-based control interface, specifically achieving an improvement in task completion times of 67%. 14%, an improvement in task completion rate of 11.54%, a decrease in overall mental workload of 65.18%, and an improvement in usability of 198.12%.