Search

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system. � 2014, Springer-Verlag Berlin Heidelberg.

Conference of 2014 IEEE International Conference on Communications Workshops, ICC 2014 ; Conference Date: 10 June 2014 Through 14 June 2014; Conference Code:107217; International audience; Small cell overlay combined with unitary frequency reuse is one of the most promising deployment options to satisfy the ever increasing rate demand in beyond 4G cellular networks. It has been shown that cooperation between small base stations and macro base stations has a great potential in these hierarchical deployments. However, a significant number of cooperation strategies require that small base stations be reached through low latency and high speed backhaul links. The latency of this backhaul is often an obstacle to efficient cooperation between macro cell and small cell layers. To answer this problem we provide a protocol based on hybrid ARQ procedure combined with a parsimonious use of over-the-air communications between small base stations and macro base stations. The proposed protocol is compatible with latency figures that are observed on affordable digital subscriber links. The performance of this protocol are assessed and compared to conventional LTE hybrid ARQ strategy using theoretical derivations and numerical experiments. Significant gains are observed on the reverse links in 3GPP LTE context.

The concept of cooperation and distribution as known from grammar systems is introduced to spiking neural P systems (in short, SN P systems) in which each neuron has a finite number of sets (called components) of rules. During computations, at each step only one of the components can be active for the whole system and one of the enabled rules from this active component of each neuron fires. The switching between the components occurs under different cooperation strategies. This paper considers the terminating mode, in which the switching occurs when no rule is enabled in the active component of any neuron in the system. By introducing this new mechanism, the computational power of asynchronous and sequential SN P systems with standard rules is investigated. The results are that asynchronous standard SN P systems with two components and strongly sequential unbounded SN P systems with two components are Turing complete. � Springer International Publishing Switzerland 2014.