We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection...
详细信息
We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortended by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.
Assigning tasks to a set of robots is a fundamental problem in robotics. It consists in finding the best task assignment to the available robots. In this paper, we present two distributed market-based algorithms to so...
详细信息
Assigning tasks to a set of robots is a fundamental problem in robotics. It consists in finding the best task assignment to the available robots. In this paper, we present two distributed market-based algorithms to solve the assignment problem where n robots compete for n tasks with the assumption that each robot can be assigned to only one task. The first algorithm, called DMB, represents a Distributed market-based algorithm where each robot bids for every task. The second algorithm is an extension of the DMB. It consists in swapping tasks between robots in order to improve the efficiency of the whole assignment. We conducted both real-world experimental testing, and MATLAB simulations to evaluate performance of the proposed algorithms and compare them against the centralized Hungarian algorithm in terms of traveled distance. Simulation results show that the IDMB algorithm produces near optimal solutions and in several cases it gives the optimal solution. In addition, we demonstrated the feasibility of our algorithms through real-world experimentation on robots. (C) 2014 Published by Elsevier B.V.
Assigning tasks to a set of robots is a fundamental problem in robotics. It consists in finding the best task assignment to the available robots. In this paper, we present two distributed market-based algorithms to so...
详细信息
Assigning tasks to a set of robots is a fundamental problem in robotics. It consists in finding the best task assignment to the available robots. In this paper, we present two distributed market-based algorithms to solve the assignment problem where n robots compete for n tasks with the assumption that each robot can be assigned to only one task. The first algorithm, called DMB, represents a Distributed market-based algorithm where each robot bids for every task. The second algorithm is an extension of the DMB. It consists in swapping tasks between robots in order to improve the efficiency of the whole assignment. We conducted both real-world experimental testing, and MATLAB simulations to evaluate performance of the proposed algorithms and compare them against the centralized Hungarian algorithm in terms of traveled distance. Simulation results show that the IDMB algorithm produces near optimal solutions and in several cases it gives the optimal solution. In addition, we demonstrated the feasibility of our algorithms through real-world experimentation on robots.
Peer-to-peer live media streaming over the Internet is becoming increasingly more popular, though it is still a challenging problem. Nodes should receive the stream with respect to intrinsic timing constraints, while ...
详细信息
Peer-to-peer live media streaming over the Internet is becoming increasingly more popular, though it is still a challenging problem. Nodes should receive the stream with respect to intrinsic timing constraints, while the overlay should adapt to the changes in the network and the nodes should be incentivized to contribute their resources. In this work, we meet these contradictory requirements simultaneously, by introducing a distributed market model to build an efficient overlay for live media streaming. Using our market model, we construct two different overlay topologies, tree-based and mesh-based, which are the two dominant approaches to the media distribution. First, we build an approximately minimal height multiple-tree data dissemination overlay, called Sepidar. Next, we extend our model, in GLive, to make it more robust in dynamic networks by replacing the tree structure with a mesh. We show in simulation that the mesh-based overlay outperforms the multiple-tree overlay. We compare the performance of our two systems with the state-of-the-art NewCoolstreaming, and observe that they provide better playback continuity and lower playback latency than that of NewCoolstreaming under a variety of experimental scenarios. Although our distributed market model can be run against a random sample of nodes, we improve its convergence time by executing it against a sample of nodes taken from the Gradient overlay. The evaluations show that the streaming overlays converge faster when our market model works on top of the Gradient overlay.
As the use of Unmanned Aerial Systems (UAS) in large-scale operations becomes increasingly more common, a method which efficiently assigns tasks to a fleet of UAS becomes critical. In a disaster response scenario, eff...
详细信息
As the use of Unmanned Aerial Systems (UAS) in large-scale operations becomes increasingly more common, a method which efficiently assigns tasks to a fleet of UAS becomes critical. In a disaster response scenario, effectively assigning tasks can minimize the amount of time to accomplish the mission, potentially reducing economic damage and the loss of live. To aid with ease of implementation, tasks are decomposed to a set of waypoints that must all be visited for the task to be considered accomplished. Optimizing to accomplish all tasks in the minimum time forms a version of a version of the multiple-depot multiple travelling salesman problem (MDMTSP) with a MinMax objective function. In this research, an iterative market-based algorithm was developed to solve the task assignment problem. It was selected not only for its ability to solve the MDMTSP, but also for its ability to handle additional constraints. The market algorithm performs well in benchmarks, finding a solution within a few percent of a known optimal value for several single-depot multiple travelling salesman problems. The algorithm also compares favorably against other solvers for the MDMTSP which approximate the optimum, finding solutions that are comparable, if not slightly better than other methods, but at a greatly reduced time. The ability of the algorithm to quickly find new task assignments when necessary is also showcased, dynamically reallocating tasks if a change is made to either the set of tasks or the fleet. The market-based algorithm is also used to generate task assignments when constraints are placed on vehicle endurance and vehicle dynamics, while tasks may also be heterogeneous. Finally, a distributed version of the market is created with the goal of an eventual integration with existing distributed consensus algorithms and transitioning to a real-world flight test.
暂无评论