Connectivity is an important requirement for wireless sensor networks especially in real-time monitoring and data transfer applications. However, node movements and failures change the structure of the initial deployed network, which can result in partitioning of the communication graph. We are proposing a method for maintaining and repairing the communication network of a dynamic wireless sensor network. We assume there are robots whose motion we can control and there are nodes whose motion we cannot control. At the heart of our method lies a novel metric, k-redundancy, which is a measure of the importance of a node to the connectivity of a network. We show that this metric can also be used to estimate the repair time for a network. Finally, we show the effectiveness of our method with extensive simulations and its feasibility with experiments on real robots and motes.

Abstract — The DPC algorithm developed in our previous work is an efficient way of computing optimal trajectories for multiple robots in a distributed fashion with timeparameterized constraints on the distances between pairs of robots. In the present work we extend DPC to the problem of multiple task execution. While this extended problem inherits all the objectives, complexities and constraints of the basic DPC algorithm, each robot is also given an unordered set of tasks that it has to execute before it reaches its goal. There is no specific order imposed on the tasks assigned to a particular robot. The algorithm decides the order of execution of the tasks such that an optimal solution is attained while the time-parametrized distance constraints are satisfied along with successful execution of the tasks. We solve this problem by designing a “State-task Graph ” that represents a product of the state-space graph and the task graph. We then develop an efficient heuristic function for performing searches in this graph. I.

Autonomous robot coordination has several real-world applications where effective usage of robots can provide significant increase in efficiency, reduction in cost and higher security for people. However, providing coordination and cooperation for mobile robots has several challenges because of the physical limitations and ineffi-cient planning methods. In order to overcome these problems, we are proposing an emergent task allocation system for autonomous mobile robots which have limited communication and sensing capabilities. In this system, each robot uses only local information to find the optimal distribution of tasks among itself and its neighbors. Since robots can have different information about the tasks, environment and avail-able robots, the solution found by each robot can be inconsistent with each other. In order to avoid this, robots exchange information using negotiation strategies and elim-inate inconsistencies. We show that as the information exchange among the robots is increased, the solution converges to the global optimal solution. The application we have chosen to test our system is target detection and tracking. This application re-quires robots to keep detected targets in their sensing ranges, provide communication

The Multirobot Task Allocation (MRTA) paradigm is widely used in multirobot cooperation schemes, e.g. for observation, surveillance and tracking missions. Market-based approaches have yielded effective distributed solutions for such missions, showing the ability to manage heterogeneous, dynamic and robust robot teams. Two major challenges remain however poorly tackled: the management of inter-robot and inter-task communication constraints, and the use of a rich task formalism to model complex missions. This paper presents our investigations to treat these two aspects. The inter-robot and inter-task communication constraints are explicitly handled in the task allocation process, through simple geometric models and thanks to temporal scheduling skills. Rich tasks are allocated using a treebased task formalism that allows to treat complex missions with task ordering. Current work has shown it to be able to handle more complex tasks and to give better solution than MRTA systems working on simple task structures. In our work we will try to go further in this investigation.

Abstract—We consider the problem of exploring an unknown environment with a pair of mobile robots. The goal is to make the robots meet (or rendezvous) in minimum time such that there is a maximum speed gain of the exploration task. The key challenge in achieving this goal is to rendezvous with the least possible dependency on communication. This single constraint involves several sub-problems: finding unique potential rendezvous locations in the environment, ranking these locations based on their uniqueness and synchronizing with the other robot to meet at one of the locations at a scheduled time. In addition, these tasks are to be performed simultaneously while exploring and mapping the environment. We propose an approach for efficiently combining the exploration and rendezvous tasks by considering the cost of reaching a rendezvous location and the reward of its uniqueness. This cost and reward model is