Auction like Task Allocation and Motion Coordination Strategies for Multi-Robot Transport Tasks (original) (raw)
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Simple auctions with performance guarantees for multi-robot task allocation
… and Systems, 2004. …, 2004
We consider the problem of allocating a number of exploration tasks to a team of mobile robots. Each task consists of a target location that needs to be visited by a robot. The objective of the allocation is to minimize the total cost, that is, the sum of the travel costs of all robots for visiting all targets. We show that finding an optimal allocation is an NP-hard problem, even in known environments. The main contribution of this paper is PRIM ALLOCATION, a simple and fast approximate algorithm for allocating targets to robots which provably computes allocations whose total cost is at most twice as large as the optimal total cost. We then cast PRIM ALLOCATION in terms of a multi-round single-item auction where robots bid on targets, which allows for a decentralized implementation. To the best of our knowledge, PRIM ALLOCATION is the first auction-based allocation algorithm that provides a guarantee on the quality of its allocations. Our experimental results in a multi-robot simulator demonstrate that PRIM ALLOCATION is fast and results in close-to-optimal allocations despite its simplicity and decentralized nature. In particular, it needs an order of magnitude fewer bids than a computationally intensive allocation algorithm based on combinatorial auctions, yet its allocations are at least as good.
Expert Systems with Applications, 2011
This paper aims to solve the balanced multi-robot task allocation problem. Multi-robot systems are becoming more and more significant in industrial, commercial and scientific applications. Effectively allocating tasks to multi-robots i.e. utilizing all robots in a cost effective manner becomes a tedious process. The current attempts made by the researchers concentrate only on minimizing the distance between the robots and the tasks, and not much importance is given to the balancing of work loads among robots. It is also found from the literature that the multi-robot system is analogous to Multiple Travelling Salesman Problem (MTSP). This paper attempts to develop mechanism to address the above two issues with objective of minimizing the distance travelled by 'm' robots and balancing the work load between 'm' robots equally. The proposed approach has two fold, first develops a mathematical model for balanced multirobot task allocation problem, and secondly proposes a methodology to solve the model in three stages. Stage I groups the 'N' tasks into 'n' clusters of tasks using K-means clustering technique with the objective of minimizing the distance between the tasks, stage II calculates the travel cost of robot and clusters combination, stage III allocates the robot to the clusters in order to utilise all robot in a cost effective manner.
Performance analysis of bid calculation methods in multirobot market-based task allocation
Turkish Journal of Electrical Engineering Computer Sciences, 2013
In this study, the empirical results of a market-based task allocation method for heterogeneous and homogeneous robot teams and different types of tasks in 2 different environments are presented. The proposed method allocates robots to tasks through a parallel multiitem auction-based process. The main contribution of the proposed method is energy-based bid calculations, which take into account both the heterogeneity of the robot team and features of the tasks. The multirobot task allocation problem is considered as the optimal assignment problem and the Hungarian algorithm is used to clear the auctions. Simulations are carried out using energy-based, distance-based, and time-based bid calculation methods. The methods are implemented using a 3-type task set: cleaning a space, carrying an object, and monitoring. The tasks may have different sensitivities and/or priority levels. Simulations show that robot-task allocations of all of the methods result in similar utility values when single-type and/or same-featured tasks are used. However, for different-type and/or different-featured tasks, the proposed energy-based bid calculation method assigns a greater number of high-sensitivity tasks compared to the other 2 methods while consuming almost the same amount of energy in both environments. Additionally, the energy-based method has a filtering behavior for high-priority tasks. These properties of the proposed method increase the efficiency of the robot team.
Dynamic task allocation for robots via auctions
Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006
We present an auction-based method for dynamic allocation of tasks to robots. The robots operate in a 2D environment for which they have a map. Tasks are locations in the map that have to be visited by the robots, in any order. Unexpected obstacles and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are rebid every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks and to reduce the overall time for task completion. We present experimental results that we have obtained in simulation using Player/Stage.
Multiple Robots Avoid Humans To Get the Jobs Done: An Approach to Human-aware Task Allocation
2021 European Conference on Mobile Robots (ECMR), 2021
Multi-robot Task Allocation (MRTA) is the problem of assigning tasks to robots subject to a performance objective. Among existing approaches to MRTA, auction-based methods are widely used. In an auction-based method, each robot typically computes its Euclidean distance to all the given tasks, and those values are bids based on which a global auctioneer allocates the tasks to them. Although simple to compute, these approaches result in an inefficient navigation of robots to reach the tasks in an environment populated with humans. We overcome this limitation by augmenting bids in an auction-based MRTA method with knowledge of human motions. As a result, this augmented task allocation method may, for instance, assign a task to a robot which is further away so long as the robot avoids possibly congested places. We validate the approach through simulated fleets of robots in a shopping centre and a small-scale warehouse environment. Our results show significant improvement over the allocation that ignores knowledge of human dynamics.
Auctions for task allocation to robots
We present an auction-based method for dynamic allocation of tasks to robots. The robots operate in a 2D environment for which they have a map. Tasks are locations in the map that have to be visited by the robots, in any order. Unexpected obstacles and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are rebid every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks and to reduce the overall time for task completion. We present experimental results that we have obtained in simulation using Player/Stage.
Auction-based multi-robot task allocation in COMSTAR
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems - AAMAS '07, 2007
Over the past few years, swarm based systems have emerged as an attractive paradigm for building large scale distributed systems composed of numerous independent but coordinating units. In our previous work, we have developed a protoype system called COMSTAR (Cooperative Multi-agent Systems for automatic TArget Recognition) using a swarm of unmanned aerial vehicles(UAVs) that is capable of identifying targets in software simulations of reconnaissance operations. Experimental results from the simulations of the COMSTAR system show that task selection among the UAVs is a crucial operation that determines the overall efficiency of the system. Previously described techniques for task selection among swarm units use a centralized server such as a ground control station to coordinate the activities of the swarm units. However, such systems are not truly distributed since the behavior of the swarm units is predominantly directed by the centralized server's task allocation algorithm. In this paper we focus on the problem of distributed task selection in a swarmed system where each swarm unit decides on the tasks it will execute by sharing information and coordinating its actions with other swarm units without the intervention of a centralized ground control station supervising its activities. Specifically, we build our task selection algorithm on an auction-based algorithm for task selection in robotic swarms described by Kalra et al. We report experimental results in a simulated environment with 18 robots and 20 tasks and compare the performance of our auction-based algorithm with other heuristic-based task selection strategies in multi-agent swarms. Our simulation results show that the auction-based algorithm improves the task completion times by 30 − 60% and reduces the communication overhead by as much as 90% with respect to other heuristic-based strategies maintaining similar performance in load balancing. 1
Auctions for robust task execution in multi-robot teams
We present an auction-based method for dynamic allocation of tasks to robots. The robots have to visit locations in a 2D environment for which they have a map. Unexpected obsta- cles, loss of communication, and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are rebid every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks and to reduce the overall time for task com- pletion. We present experimental results that we have ob- tained in simulation using Player/Stage.
Repeated auctions for robust task execution by a robot team
Robotics and Autonomous Systems, 2010
We present empirical results of an auction-based algorithm for dynamic allocation of tasks to robots. The results have been obtained both in simulation and using real robots. A distinctive feature of our algorithm is its robustness to uncertainties and to robot malfunctions that happen during task execution, when unexpected obstacles, loss of communication, and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are resubmitted for bids every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks, enabling the robots to recover from failures and reducing the overall time for task completion.