Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

[No abstract available

Collision Avoidance and Navigation of UAS Using Vision-Based Proportional Navigation

Electro-optical devices have received considerable interest due to their light weight, low cost, and low algorithm requirements with respect to computational power. In this thesis, vision-based guidance laws are developed to provide sense and avoid capabilities for unmanned aerial vehicles (UAVs) operating in complex environments with multiple static and dynamic collision threats. These collision avoidance guidance laws are based on the principle of proportional navigation (Pro-Nav), which states that a UAV is on a collision course with another vehicle or object if the line-of-sight (LOS) angles to the object remain constant. The guidance laws are designed for use with monocular electro-optical devices, which provide information on the LOS angles to potential collision threats, but not the range. The development of these guidance laws propagates from an investigation into numerous methods of Pro-Nav based guidance, including the use of LOS rate thresholding, avoidance of the most imminent threat detected, and objective-based cost optimization. The collision avoidance guidance laws were applied to nonlinear, six degree-of-freedom UAV models in various simulation environments including a varying number of static and dynamic obstacles. A final form of the avoidance law, determined from these simulation studies, was applied to a small-scale UAV model flying through a virtual urban environment, which utilizes camera-in-the-loop simulation techniques. The final results of these studies showed that the most effective approach was to implement a cost function-based avoidance law that includes a term based on the Pro-Nav intercept heading for a desired waypoint and avoidance terms for all obstacles in view that pose a collision threat. Obstacle avoidance headings in the cost function are based on the difference in the obstacle LOS rates from the magnitude of the minimum safe LOS rate. When applied to UAV simulations in a virtual urban environment, this guidance law provided successful avoidance for the case of a single building, maintained a safe heading through an urban canyon, and determined the safest path through a complex urban layout. For the case of the complex urban layout, a single collision during flight occurred due to a lack of visual feature points to contribute to the avoidance law calculation