José Guerrero

Multi-robot coalition formation in real-time scenarios

Task allocation is one of the main issues to be addressed in multi-robot systems, especially when the robots form coalitions and the tasks have to be fulfilled before a deadline. In general, it is difficult to foresee the time required by a coalition to finish a task because it depends, among other factors, on the physical interference. Interference is a phenomenon produced when two or more robots want to access the same point simultaneously. This paper presents a new model to predict the time to execute a task. Thanks to this model, the robots needed to carry out a task before a deadline can be determined. Within this framework, the robots learn the interference and therefore, the coalitions utility, from their past experience using an on-line Support Vector Regression method (SVR). Furthermore, the SVR model is used together with a new auction method called Double Round auction (DR). It will be demonstrated that by combining the interference model and the DR process, the total utility of the system significantly increases compared to classical auction approaches. This is the first auction method that includes the physical interference effects and that can determine the coalition size during the execution time to address tasks with deadlines. Transport like tasks run on a simulator and on real robots have been used to validate the proposed solutions.

Mini Rotorcraft Flight Formation Control Using Bounded Inputs

In this paper, the flight formation control and trajectory tracking control design of multiple mini rotorcraft systems are discussed. The dynamic model of a mini rotorcraft is presented using the Newton-Euler formalism. Our approach is based on a leader/follower structure of multiple robot systems. The centroid of the coordinated control subsystem is used for trajectory tracking purposes. A nonlinear controller based on separated saturations and a multi-agent consensus algorithm is developed. The analytic results are supported by simulation tests. Experimental results include yaw coordination and tracking only.

UAV Path Planning for Structure Inspection in Windy Environments

In this paper, we consider the structure inspection problem using a miniature unmanned aerial vehicle (UAV). The influence of the wind on the UAV behavior and onboard energy limitations are important parameters that must be taken into account in the structure inspection problem. To tackle these problems, we derive three methods to inspect a structure. First, we develop a Zermelo-Traveling Salesman Problem (TSP) method to compute the optimal route to inspect a simple virtual structure. Second, we derive a method that combines meshing techniques with the Zermelo-TSP method. In this approach, the inspection coordinates for the interest points are obtained automatically by means of a meshing algorithm, then, the Zermelo-TSP method is used to compute the time-optimal route to inspect all the interest points in minimal time. Finally, we derive a method for structure inspection based on the Zermelo-Vehicle Routing Problem (VRP). These methods have been validated in a simulated environment.

Quadrotors Formation Control

In this paper, the impact of the medium access protocols on the average consensus problem over wireless networks for a group of quadrotors is established. The stabilization of each helicopter is guaranteed using a simple and bounded nonlinear control strategy. We study the case of a group of quadrotors communicating over a wireless network considering both directed and undirected graphs of information flow. It turns out that the media access control (MAC) protocols have a direct impact in both convergence time and average consensus solution, i.e., the solution of the average consensus is no longer the average of the initial conditions. It will be shown that the solution for the average consensus problem over a wireless network depends directly on the MAC algorithm. Simulations are provided to demonstrate the theoretical results. In addition, to validate the control strategy some experimental tests have been carried out to control the yaw angle of two quadrotors.

Vision Based Tracking for a Quadrotor Using Vanishing Points

In this paper, a vision based line tracking control strategy for mini-rotorcraft is presented. In order to estimate the 3-D position of the mini-rotorcraft over the trajectory a vanishing points technique is used. A dynamic model is derived employing the Newton–Euler approach and a nonlinear controller to stabilize, in closed-loop system, this mathematical model is proposed. To validate the theoretical results, a real-time embedded control system has been developed. The performance of the vision and control algorithms has been tested when the helicopter has tracked a line painted in a wall. The experimental results have shown the good behavior of the control laws.

Physical interference impact in multi-robot task allocation auction methods

Task allocation is one of the main problems in multi-robot systems. Among other factors, to get a good task allocation, we have to take into account the physical interference effects between robots, that is, when two or more robots want to access to the same point at the same time. This paper analyzes interference impact using auction methods, one of the most popular task allocation systems. We show how the performance of the auction utility function can be improved if interference impact is included in it. We also provide a framework to simplify one of the main problems of all auction systems which is finding a good utility function. Our method has been tested using transport like tasks, where each object must be transported to a delivery point before a deadline. This is a simple task that is very useful to isolate the interference effects

Auction and Swarm Multi-Robot Task Allocation Algorithms in Real Time Scenarios

A group of several autonomous robots (multi-robot system) can perform tasks that with only one of them would be impossible to carry out or would take much more time, moreover, they are more robust and even can be cheaper, etc than systems with a single robot. In general, the problems that have to be solved to benefit from all these advantages are divided into three main stages: task division/planning, task allocation and motion planning. Task division stage, consists on dividing the general and complex mission into simple tasks that can be carried out by a robot and, if it’s necessary, scheduling those simpler goals. The task allocation step will select the best robot or group of robots to execute each goal. Finally, the motion planning issues involve the robots’ motions coordination to get the assigned tasks. Although these steps have been explained as independent and sequential stages, they are tightly connected and the decisions made in one level affect the whole system performance. For example, in Zlot & Stentz (2006) the authors proposed a task allocation method that combined planning, task decomposition and task allocation. Although this and similar efforts can be found in the literature. Each one of these steps is still an open problem. This paper will be focused on multi robot task allocation (MRTA) issues, without taking into account the other problems. Task allocation is one of the main problems in multi-robot systems, very especially when the tasks must be executed before deadlines, that is, in real-time scenarios. In most cases this problem is an NP-hard problem, and therefore nowadays there is not any algorithm that in a reasonable computing time gives the optimal tasks allocation. Two main paradigms have been proposed in recent years to try to manage this problem in both real-time and non real-time scenarios: swarm and auction methods. At present, there does not exist any study that compares both strategies when the robots must carry out tasks with soft or hard real-time restrictions. Other works, for example Kalra & Martinoli (2006), compare auction and swarm methods but using tasks without deadlines. Therefore, the first objective of this work is to compare all those methods under different scenarios to identify the weak points of each paradigm when a deadline is assigned to the tasks. Firstly, our work presents and study three auction-like strategies based on existing ones: Sequential Unordered Auction (SUA), Earliest Deadline First Auction (EDFA) and Sequential Best Pair Auction (SBPA). In all these cases there is a central auctioneer who receives the bids from all the robots and decides the allocation of the tasks. These strategies differ between them on the way the auctioneer announces the tasks to the robots and on the

Multi-Robot Task Allocation Method for Heterogeneous Tasks with Priorities

Task allocation is a complex and open problem for multi-robot systems and very especially if a priority is associated to each task. In this paper, we present a method to allocate tasks with priorities in a team of heterogeneous robots. The system is partially inspired on auction and thresholds-based methods and tries to determine the optimum number of robots that are needed to solve specific tasks taking into account their priorities and characteristics. Thus, we can minimize the interference effect between robots and increase the system performance. The method has been extensively tested for a modification of the well-known foraging task, using different kinds of robots. Experimental results are presented to show the benefits of the proposed method.

Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions

Multi-robot task allocation is one of the main problems to address in order to design a multi-robot system, very especially when robots form coalitions that must carry out tasks before a deadline. A lot of factors affect the performance of these systems and among them, this paper is focused on the physical interference effect, produced when two or more robots want to access the same point simultaneously. To our best knowledge, this paper presents the first formal description of multi-robot task allocation that includes a model of interference. Thanks to this description, the complexity of the allocation problem is analyzed. Moreover, the main contribution of this paper is to provide the conditions under which the optimal solution of the aforementioned allocation problem can be obtained solving an integer linear problem. The optimal results are compared to previous allocation algorithms already proposed by the first two authors of this paper and with a new method proposed in this paper. The results obtained show how the new task allocation algorithms reach up more than an 80% of the median of the optimal solution, outperforming previous auction algorithms with a huge reduction of the execution time.

Vision-based autonomous hovering for a miniature quad-rotor

In this paper, a vision-based scheme for the autonomous hovering of a miniature quad-rotor is developed. Cameras are used to estimate the position and the translational velocity of the vehicle. The dynamic model of the miniature quad-rotor is developed using the Newton-Euler approach. A nonlinear controller based on a separated saturation control strategy for a miniature quad-rotor is presented. To validate the theoretical results, an embedded control system for the miniature quad-rotor has been developed. Thus, the analytic results are supported by experimental tests. Experimental results have validated the proposed control strategy.