Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.Unión Europea ARCAS FP7-ICT-287617Unión Europea H2020-ICT-644271Unión europea H2020-ICT-73166

Empirical measurements of small unmanned aerial vehicle co-axial rotor systems

Small unmanned aerial vehicles (SUAV) are beginning to dominate the area of intelligence, surveillance, target acquisition and reconnaissance (ISTAR) in forward operating battlefield scenarios. Of particular interest are vertical take-off and landing (VTOL) variants. Within this category co-axial rotor designs have been adopted due to their inherent advantages of size and power to weight ratio. The inter-rotor spacing attribute of a co-axial rotor system appears to offer insight into the optimum design characteristic. The H/D ratio has been cited as a significant factor in many research papers, but to date has lacked an empirical value or an optimal dimensionless condition. In this paper the H/D ratio of a SUAV has been explored thoroughly, reviewing the performance of these systems at incremental stages, the findings from this study have shown that a range of H/D ratios in the region of (0.41-0.65) is advantageous in the performance of SUAV systems. This finding lends itself to the theory of inter-rotor spacing as a non-dimensionally similar figure, which cannot be applied across a spectrum of systems; this could be attributed to the viscous losses of flight at low Reynolds Numbers (< 50,000

Visual-based SLAM configurations for cooperative multi-UAV systems with a lead agent: an observability-based approach

In this work, the problem of the cooperative visual-based SLAM for the class of multi-UA systems that integrates a lead agent has been addressed. In these kinds of systems, a team of aerial robots flying in formation must follow a dynamic lead agent, which can be another aerial robot, vehicle or even a human. A fundamental problem that must be addressed for these kinds of systems has to do with the estimation of the states of the aerial robots as well as the state of the lead agent. In this work, the use of a cooperative visual-based SLAM approach is studied in order to solve the above problem. In this case, three different system configurations are proposed and investigated by means of an intensive nonlinear observability analysis. In addition, a high-level control scheme is proposed that allows to control the formation of the UAVs with respect to the lead agent. In this work, several theoretical results are obtained, together with an extensive set of computer simulations which are presented in order to numerically validate the proposal and to show that it can perform well under different circumstances (e.g., GPS-challenging environments). That is, the proposed method is able to operate robustly under many conditions providing a good position estimation of the aerial vehicles and the lead agent as well.Peer ReviewedPostprint (published version

Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411

An Aerial Robotics Investigation into the Stability, Coordination, and Movement of Strategies for Directing Swarm and Formation of Autonomous MAVs and Diverse Groups of Driverless Vehicles (UGVs)

This study will discuss the matter of movement communication and preparation of tight configurations of land & flying robots. Remotely Operated Cars (UGVs) and Unmanned Aerial Vehicles (UAVs), in specific Micro Aerial Vehicles (MAVs), would be used to fix circumstances where a creation of UGVs and UAVs, in specific Micro Aerial Vehicles (MAVs), should counteract their velocity and direction to finish a mission of traffic sequence to a targeted area. The motion planning and stabilisation strategy given here is a useful tool for deploying closely collaborating robot teams including both outdoor and indoor settings. The installation of large groups of Micro Aerial Vehicles (MAVs) in a legitimate (indoor and outdoor) environment without the use of auxiliary positioning applications (such as Vicon or GPS) is indeed a natural development in the area of autonomously flying systems. Stability, control, and trajectory planning techniques for guiding swarm or configurations of unmanned MAVs, as well as diverse groups with Unmanned Ground Vehicles (UGVs) operating alongside MAVs, will be discussed in greater detail. These approaches discussed all are designed for the use of inter squads in true complex scenarios even without necessity for worldwide translation or motion capture systems, as they are predicated on board optical comparative localisation of single MAVs. This multi - objective optimisation being an enabler for the introduction of swarming of tiny autonomous drones beyond the labs with equipment for precise robot positioning. Model Predictive Control (MPC) is being used to address a formations to goal territory issue, and the form drive idea is based on a simulated approach. The Particle swarm optimization approach is utilised for digital leader trajectories planning, as well as control and stabilisation of follows (MAVs and UGVs). The proposed technique could be tested in the future using a range of simulation and practical tests