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

A Survey and Analysis of Multi-Robot Coordination

International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper

Avoiding target congestion on the navigation of robotic swarms

Robotic swarms are decentralized systems formed by a large number of robots. A common problem encountered in a swarm is congestion, as a great number of robots often must move towards the same region. This happens when robots have a common target, for example during foraging or waypoint navigation. We propose three algorithms to alleviate congestion: in the first, some robots stop moving towards the target for a random number of iterations; in the second, we divide the scenario in two regions: one for the robots that are moving towards the target, and another for the robots that are leaving the target; in the third, we combine the two previous algorithms. We evaluate our algorithms in simulation, where we show that all of them effectively improve navigation. Moreover, we perform an experimental analysis in the real world with ten robots, and show that all our approaches improve navigation with statistical significance

Unsynchronized Distributed Motion Planning with Safety Guarantees under Second-Order Dynamics

Robots are increasingly found to operate together in the same environment where they must coordinate their motion. Such an operation is simple if the motion is quasi-static. Under second-order dynamics, the problem becomes challenging even for a known environment. Planning must guarantee safety by ensuring collision-free paths for the considered period by not bringing the robot to states where collisions are inevitable. This can be addressed with communication among robots, but it becomes complicated when the replanning cycles of different robots are not synchronized and robots make planning decisions at different times. This thesis shows how to guarantee safety for communicating second-order vehicles, whose replanning rates do not coincide, through a distributed motion planning framework without a global time reference. The method is evaluated through simulation where each robot has its own address space, and communicates with message passing. A proof of safety is presented, and simulation results are used to investigate performance of the framework

Human-Comfortable Collision Free Navigation for Personal Aerial Vehicles

Semi- or fully-autonomous Personal Aerial Vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this work, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the Optimal Reciprocal Collision Avoidance strategy [1]. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of 10 quadcopters flying outdoors

Human-Comfortable Collision Free Navigation for Personal Aerial Vehicles

Semi- or fully-autonomous Personal Aerial Vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this work, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the Optimal Reciprocal Collision Avoidance strategy [1]. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of 10 quadcopters flying outdoors

Perpetual flight in flow fields

Tese de Doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Detect-And-Avoid (DAA) Systems

The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is "generic" in that it makes no assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Accordingly, GRACE is well suited to research applications where alerting and guidance is a central feature and requirements are fluid involving a wide range of aviation technologies. GRACE has been used at NASA in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests

Optimization and Mathematical Modelling for Path Planning of Co-operative Intra-logistics Automated Vehicles

Small indoor Autonomous Vehicles have revolutionized the operation of pick-pack-and-ship warehouses. The challenges for path planning and co-operation in this domain stem from uncontrolled environments including workspaces shared with humans and human-operated vehicles. Solutions are needed which scale up to the largest existing sites with thousands of vehicles and beyond. These challenges might be familiar to anyone modelling road traffic control with the introduction of Autonomous Vehicles, but key differences in the level of decision autonomy lead to different approaches to conflict-resolution. This thesis proposes a decomposition of site-wide conflict-free motion planning into individual shortest paths though a roadmap representing the free space across the site, zone-based speed optimization to resolve conflicts in the vicinity of one intersection and individual path optimization for local obstacles. In numerical tests the individual path optimization based on clothoid basis functions created paths traversable by different vehicle configurations (steering rate limit, lateral acceleration limit and wheelbase) only by choosing an appropriate maximum longitudinal speed. Using two clothoid segments per convex region was sufficient to reach any goal, and the problem could be solved reliably and quickly with sequential quadratic programming due to the approximate graph method used to determine a good sequence of obstacle-free regions to the local goal. A design for zone-based intersection management, obtained by minimizing a linear objective subject to quadratic constraints was refined by the addition of a messaging interface compatible with the path adaptations based on clothoids. A new approximation of the differential constraints was evaluated in a multi-agent simulation of an elementary intersection layout. The proposed FIFO ordering heuristic converted the problem into a linear program. Interior point methods either found a solution quickly or showed that the problem was infeasible, unlike a quadratic constraint formulation with ordering flexibility. Subsequent tests on more complex multi-lane intersection geometries showed the quadratic constraint formulation converged to significantly better solutions than FIFO at the cost of longer and unpredictable search time. Both effects were magnified as the number of vehicles increased. To properly address site-wide conflict-free motion planning, it is essential that the local solutions are compatible with each other at the zone boundaries. The intersection management design was refined with new boundary constraints to ensure compatibility and smooth transitions without the need for a backup system. In numerical tests it was found that the additional boundary constraints were sufficient to ensure smooth transitions on an idealized map including two intersections