29 research outputs found
Distributed tree rearrangements for reachability and robust connectivity.
Abstract. We study maintenance of network connectivity in robotic swarms with discrete-time communications and continuous-time motion capabilities. Assuming a network topology induced by spatial proximity, we propose a coordination scheme which guarantees connectivity of the network by maintaining a spanning tree at all times. Our algorithm is capable of repairing the spanning tree in the event of link failure, and of transitioning from any initial tree to any other tree which is a subgraph of the communications graph
Coordination of Cooperative Multi-Robot Teams
This thesis is about cooperation of multiple robots that have a common
task they should fulfill, i.e., how multi-robot systems behave in cooperative
scenarios. Cooperation is a very important aspect in robotics, because
multiple robots can solve a task more quickly or efficiently in many situations.
Specific points of interest are, how the effectiveness of the group of
robots completing a task can be improved and how the amount of communication
and computational requirements can be reduced. The importance
of this topic lies in applications like search and rescue scenarios, where
time can be a critical factor and a certain robustness and reliability are
required. Further the communication can be limited by various factors
and operating (multiple) robots can be a highly complicated task.
A typical search and rescue mission as considered in this thesis begins
with the deployment of the robot team in an unknown or partly known
environment. The team can be heterogeneous in the sense that it consists
of pairs of air and ground robots that assist each other. The air vehicle –
abbreviated as UAV – stays within vision range of the ground vehicle or
UGV. Therefrom, it provides sensing information with a camera or similar
sensor that might not be available to the UGV due to distance, perspective
or occlusion. A new approach to fully use the available movement range
is presented and analyzed theoretically and in simulations. The UAV
moves according to a dynamic coverage algorithm which is combined with
a tracking controller to guarantee the visibility limitation is kept.
Since the environment is at least partly unknown, an exploration method
is necessary to gather information about the situation and possible targets
or areas of interest. Exploring the unknown regions in a short amount
of time is solved by approaching points on the frontier between known
and unknown territory. To this end, a basic approach for single robot
exploration that uses the traveling salesman problem is extended to multirobot
exploration. The coordination, which is a central aspect of the
cooperative exploration process, is realized with a pairwise optimization
procedure. This new algorithm uses minimum spanning trees for cost
estimation and is inspired by one of the many multi-robot coordination
methods from the related literature. Again, theoretical and simulated as
well as statistical analysis are used as methods to evaluate the approach.
After the exploration is complete, a map of the environment with possible
regions of higher importance is known by the robot team. To stay
useful and ready for any further events, the robots now switch to a monitoring
state where they spread out to cover the area in an optimal manner.
The optimality is measured with a criterion that can be derived into a distributed
control law. This leads to splitting of the robots into areas of
Voronoi cells where each robot has a maximum distance to other robots
and can sense any events within its assigned cell. A new variant of these
Voronoi cells is introduced. They are limited by visibility and depend on
a delta-contraction of the environment, which leads to automatic collision
avoidance. The combination of these two aspects leads to a coverage
control algorithm that works in nonconvex environments and has advantageous
properties compared to related work
Cooperative Search by Multiple Unmanned Aerial Vehicles in a Nonconvex Environment
This paper presents a distributed cooperative search algorithm for multiple unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities in a nonconvex environment. The objective is to control multiple UAVs to find several unknown targets deployed in a given region, while minimizing the expected search time and avoiding obstacles. First, an asynchronous distributed cooperative search framework is proposed by integrating the information update into the coverage control scheme. And an adaptive density function is designed based on the real-time updated probability map and uncertainty map, which can balance target detection and environment exploration. Second, in order to handle nonconvex environment with arbitrary obstacles, a new transformation method is proposed to transform the cooperative search problem in the nonconvex region into an equivalent one in the convex region. Furthermore, a control strategy for cooperative search is proposed to plan feasible trajectories for UAVs under the kinematic constraints, and the convergence is proved by LaSalle’s invariance principle. Finally, by simulation results, it can be seen that our proposed algorithm is effective to handle the search problem in the nonconvex environment and efficient to find targets in shorter time compared with other algorithms
Metodología para el uso de la técnica de localización de raíces en la planeación de rutas para robots móviles
This paper shows the analysis and the implementation methodology of the technique of dynamic systems roots location used in free-obstacle path planning for mobile robots. First of all, the analysis and morphologic behavior identification of the paths depending on roots location in complex plane are performed, where paths type and their attraction and repulsion features in the presence of other roots similarly to the obtained with artificial potential fields are identified. An implementation methodology for this technique of mobile robots path planning is proposed, starting from three different methods of roots location for obstacles in the scene. Those techniques change depending on the obstacle key points selected for roots, such as borders, crossing points with original path, center and vertices. Finally, a behavior analysis of general technique and the effectiveness of each tried method is performed, doing 20 tests for each one, obtaining a value of 65% for the selected method. Modifications and possible improvements to this methodology are also proposed.Este artículo presenta el análisis y la metodología de implementación de la técnica de localización de raíces de sistemas dinámicos para la planeación de rutas libres de obstáculos para robots móviles. En primera instancia, se realiza un análisis e identificación del comportamiento morfológico de las trayectorias en dependencia de la ubicación de las raíces en el plano complejo, identificándose el tipo de trayectorias curvas y la característica de atracción y repulsión de estas en presencia de otras raíces, de forma similar al obtenido con la técnica de cargas de potencial artificial. Se plantea una metodología para implementación de esta técnica para la planeación de rutas de robots móviles, partiendo de tres métodos diferentes de ubicación de las raíces para los obstáculos presentes en el escenario. Dichas técnicas varían dependiendo de los puntos clave del obstáculo que son seleccionados para las raíces, tales como los bordes, los cruces con las trayectoria original, el centro y los vértices. Finalmente, se realiza un análisis de funcionamiento de la técnica en general y de la efectividad cada uno de los métodos evaluados, bajo 20 pruebas para cada uno, obteniendo un valor del 65% para el método seleccionado. También se proponen modificaciones o posibles mejoras a la metodología propuesta