Bid adjustment

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A Closed-Loop Bid Adjustment Method of Dynamic Task Allocation of Robots... 

Section 2 elaborates on the proposed bid adjustment mechanism, while Section 3 describes its implementation for simulatiOTi of free-range automated guided vehicles serving at a container terminal to demonstrate the effectiveness of the adjustment mechanism. Section 4 draws conclusion and discusses the future work. 

The Closed-Loop Bid Adjustment Method 2.1 The Task Auction Architecture... 

To damp out huge fluctuations and to reflect more reliable estimations, a series of previous adjustments should be taken into account. Moreover, since the working environment is changing dynamically, older track records are deemed relatively obsolete as time elapses. Hence, a time-discounting factor, a, where 0[Pg.86]

In practise, the latest three terms are sufficient for adjustment of the bid price. The complete form of the proposed bid adjustment mechanism is given in (7.3). 

The task being auctioned is therefore assigned to the robot that submitted the lowest adjusted bid price, based on (7.3). As such, this closed-loop bid adjustment mechanism can improve bidding accuracy, considerably enhancing the overall team performance. 

Step 5 The robot compares the actual cost with the proposed bid price, and updates the bid adjustment. 

Step 6 The robot logs the bid adjustment into its related track record and calculates the averaged adjustment value. 

The closed-loop bid adjustment mechanism is incorporated with a multi-robot dynamic task allocation module in a simulator, which also includes a module for motion planning of a fleet of robots. The details of this motion planning approach can be found in [21]. 

Since these adjustment values indicate the discrepancies between the bid prices and the related actual costs, it verifies that, with the closed-loop bid adjustment mechanism in auctions, the discrepancies between the actual costs and the bid prices were effectively reduced. With the improved bid prices, tasks were assigned... 

This simulation verifies that the closed-loop bid adjustment mechanism can stably reduce the discrepancies between the bidding prices and the actual costs, even with some dynamic situations during operation. 

Figure 7.5 presents a comparison of the overall team performances, in terms of operational time. The one without bid adjustment took 436.6 min, while the other one with bid adjustment consumed 362.4 min. With the proposed bid adjustment mechanism, the bid prices for different types of tasks were adjusted and improved according to the dynamic conditions during operation. As a result, the discrepancies between the bid prices and the related actual costs could be effectively reduced. Competent AGVs that offered more reliable bid prices were awarded the auctioned containers. A considerable improvement of 17% in overall team performance was achieved. 

This chapter presents an auction-based approach with the closed-loop bid adjustment mechanism to dynamic task allocation for robots. The bid adjustment mechanism fine-tunes bid prices based on the performance track records of each robot. A simulator is developed, with a case study of AGVs transporting containers, to validate this task allocation approach. Simulation results show that the bid adjustment mechanism can effectively reduce the discrepancies between the submitted bid prices and the corresponding actual costs of tasks. The stability of the approach is also verified in light of some operational uncertainties. This bid adjustment mechanism enhances the likelihood of allocating tasks to competent robots that submit more accurate bids, and as a result, improves the overall team performance substantially. 

Zhu WK, Choi SH (2011) An auction-based approach with closed-loop bid adjustment to dynamic task allocation in robot teams. In Proceedings of the world congress on engineering 2011 (WCE 2011). Lecture Notes in Engineering and Computer Science, London, UK, 6-8 July 2011, pp 1016-1066... 

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