Optimal Market-based Multi-Robot Task Allocation via Strategic Pricing

Abstract

Abstract-Auction and market-based mechanisms are among the most popular methods for distributed task allocation in multirobot systems. Most of these mechanisms were designed in a heuristic way and analysis of the quality of the resulting assignment solution is rare. This paper presents a new market-based multi-robot task allocation algorithm that produces optimal assignments. Rather than adopting a buyer's "selfish" bidding perspective as in previous auction/market-based approaches, the proposed method approaches auctioning from a merchant's point of view, producing a pricing policy that responds to cliques of customers. The algorithm uses price escalation to clear a market of all its goods, producing a state of equilibrium that satisfies both the merchant and customers. The proposed method can be used as a general assignment algorithm as it has a time complexity (O(n 3 lgn)) close to the fastest state-of-the-art algorithms (O(n 3 )) but is extremely easy to implement. As in previous research, the economic model reflects the distributed nature of markets inherently: in this paper it leads directly to a decentralized method ideally suited for distributed multi-robot systems.

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

“…There are various techniques for multi-robot task scheduling such as market-based [28,29], bio-inspired [30,31], and hybrid approaches [32]. In the market-based approach, the task is divided into a set of subtasks and the robots bid to carry out these subtasks.…”

Hybrid mission planning with coalition formation

“…The bid-ask system encodes all the information about availability of services and cost to perform those services in a single number; thus, limiting the communication overhead to the ask and bid prices. Market based approaches to task allocation have also been developed for optimal assignment in fault-tolerant networks (Liu and Shell, 2013), wireless sensor networks (Mezei et al, 2013;Haghighi, 2014), and sequentially interdependent tasks in swarm robotics (Brutschy et al, 2014).…”

Financialized methods for market-based multi-sensor fusion

“…This was validated in simulation and later applied to physically embodied robotic systems by Zlot and Stenz [15,16]. A greater degree of formalization to market-based task allocation was provided by Liu and Shell who expanded previous approaches with bounds on complexity [17]. Such market-based techniques were very well suited to task allocation.…”