Sequential Single-item Auction Research Articles

A Framework for Simultaneous Task Allocation and Planning under Uncertainty

We present novel techniques for simultaneous task allocation and planning in multi-robot systems operating under uncertainty. By performing task allocation and planning simultaneously, allocations are informed by individual robot behaviour, creating more efficient team behaviour. We go beyond existing work by planning for task reallocation across the team given a model of partial task satisfaction under potential robot failures and uncertain action outcomes. We model the problem using Markov decision processes, with tasks encoded in co-safe linear temporal logic, and optimise for the expected number of tasks completed by the team. To avoid the inherent complexity of joint models, we propose an alternative model that simultaneously considers task allocation and planning, but in a sequential fashion. We then build a joint policy from the sequential policy obtained from our model, thus allowing for concurrent policy execution. Furthermore, to enable adaptation in the case of robot failures, we consider replanning from failure states and propose an approach to preemptively replan in an anytime fashion, replanning for more probable failure states first. Our method also allows us to quantify the performance of the team by providing an analysis of properties, such as the expected number of completed tasks under concurrent policy execution. We implement and extensively evaluate our approach on a range of scenarios. We compare its performance to a state-of-the-art baseline in decoupled task allocation and planning: sequential single-item auctions. Our approach outperforms the baseline in terms of computation time and the number of times replanning is required on robot failure.

An Improved Intelligent Auction Mechanism for Emergency Material Delivery

Emergency material delivery is vital to disaster emergency rescue. Herein, the framework of the emergency material delivery system (EMDS) with the unmanned aerial vehicle (UAV) as the vehicle is proposed, and the problem is modeled into a multi-trip time-dependent dynamic vehicle routing problem with split-delivery (MTTDDVRP-SD) in combination with the rescue reality, which provides decision support for planning disaster relief material. Due to the universality of dynamic interference in the process of material delivery, an optimization algorithm based on the traditional intelligent auction mechanism is proposed to avoid system performance degradation or even collapse. The algorithm adds pre-authorization and sequential auction mechanisms to the traditional auction mechanism, where the pre-authorization mechanism improves the capability performance of the system when there is no interference during the rescue process and the sequential auction mechanism improves the resilience performance of the system when it faces interferences. Finally, considering three types of interference comprehensively, which includes new task generations, task unexpected changes and UAV’s number decreases, the proposed algorithm is compared with DTAP (DTA based on sequential single item auctions) and CBBA-PR (consensus-based bundle algorithms-partial replanning) algorithms under different dynamic interference intensity scenarios for simulation experimental from two perspectives of the capability performance and resilience performance. The results of Friedman’s test with 99% confidence interval indicate that the proposed algorithm can effectively improve the capability performance and resilience performance of EMDS.

Decentral task allocation for industrial AGV-systems with routing constraints

Automated guided vehicles (AGVs) form a large and important part of the logistics transportation systems in today's industry and are widely used. One of the main problems in the control of AGV systems is the routing problem, where all robots attempt to execute all of their allocated tasks without congestions with other robots. This is a complex problem and a large variety of solutions exist in literature to this purpose. One possible solution that is not widely investigated in literature is the possibility of including routing information as a constraint in the task allocation process in order to obtain a more balanced allocation that could reduce possible congestions. In task allocation literature, costs for a robot to execute a task are largely estimated without considering possible delays that can occur due to congestions when moving to a task. In this paper, the observed research gap of including routing constraints in the task allocation problem is addressed by proposing a decentralized task allocation algorithm based on sequential single-item (SSI) auctions with the implementation of delegate-MAS (DMAS) as a routing constraint in the bidding process. Our new approach is benchmarked to an SSI-solver that does not take routing constraints into account.

Decentral task allocation for industrial AGV-systems with resource constraints

Automated Guided Vehicles (AGVs) form a large and important part of the logistics transportation systems in today's industry and are widely used, especially in Europe. Today's AGV-systems offered by global manufacturers almost all operate under some form of centralized control where a single central controller coordinates the entire fleet of AGVs. There is a trend towards decentralized control of these systems where AGVs make individual decisions that promote the flexibility, robustness and scalability of transport. However, its practical implementation seems to be in its infancy. In addition to the lack of practical implementation of decentralized control in industrial AGV-systems, we have observed a research gap in intelligent resource management of AGV-systems, which we have tried to address in previous work by proposing a more intelligent resource management approach. In this paper, we have addressed both the perceived lack of practical decentralized AGV control and the lack of intelligent resource management by proposing a decentralized task allocation algorithm based on sequential single-item auctions, taking into account resource constraints, and in which our intelligent resource management approach from previous work is introduced. We have benchmarked our new approach to a genetic algorithm-based task-allocation solver that uses “threshold-100”-charging as a resource management strategy. The result of the proposal is a decentralized task-allocation architecture under resource constraints that could be used in current AGV-systems to add more decentralized features to the fleet.

Sequential single-item auction improvements for heterogeneous multi-robot routing

We introduce new auction bidding and resolution algorithms to improve multi-robot sequential single-item auctions for heterogeneous systems. We consider two objectives, minimising the energy usage and time required to complete all tasks. Sequential single-item auctions are computationally inexpensive while producing efficient task allocations for homogeneous robots, but produce less efficient allocations for heterogeneous robots. Our algorithms provide consistent and significant (up to 20%) improvements for both objectives for a number of scenarios relative to the standard auction process, as tested in MATLAB simulations. Interestingly, our algorithms produce faster task completion even in homogeneous systems. We also introduce a new algorithm for sequential single-item auctions when robots have partial knowledge of their environment. We illustrate its improved performance and analyse its sensitivity, showing that precise tuning is not essential for faster and more efficient task completion. These improvements can reduce energy usage and task completion times for both indoor and outdoor robots in a variety of fields.

Decentralized multi-robot allocation of tasks with temporal and precedence constraints

We present an auction-based method for a team of robots to allocate and execute tasks that have temporal and precedence constraints. Temporal constraints are expressed as time windows, within which a task must be executed. The robots use our priority-based iterated sequential single-item auction algorithm to allocate tasks among themselves and keep track of their individual schedules. A key innovation is in decoupling precedence constraints from temporal constraints and dealing with them separately. We demonstrate the performance of the allocation method and show how it can be extended to handle failures and delays during task execution. We leverage the power of simulation as a tool to analyze the robustness of schedules. Data collected during simulations are used to compute well-known indexes that measure the risk of delay and failure in the robots’ schedules. We demonstrate the effectiveness of our method in simulation and with real robot experiments.

Distributed on-line dynamic task assignment for multi-robot patrolling

Multi-robot patrolling is a key feature for various applications related to surveillance and security, and it has been studied from several different perspectives, ranging from techniques that devise optimal off-line strategies to implemented systems. However, still few approaches consider on-line decision techniques that can cope with uncertainty and non-determinism in robot behaviors. In this article we address on-line coordination, by casting the multi-robot patrolling problem as a task assignment problem and proposing two solution techniques: DTA-Greedy, which is a baseline greedy approach, and DTAP, which is based on sequential single-item auctions. We evaluate the performance of our system in a realistic simulation environment (built with ROS and stage) as well as on real robotic platforms. In particular, in the simulated environment we compare our task assignment approaches with previous off-line and on-line methods. Our results confirm that on-line coordination approaches improve the performance of the multi-robot patrolling system in real environments, and that coordination approaches that employ more informed coordination protocols (e.g., DTAP) achieve better performances with respect to state-of-the-art online approaches (e.g., SEBS) in scenarios where interferences among robots are likely to occur. Moreover, the deployment on real platforms (three Turtlebots in an office environment) shows that our on-line approaches can successfully coordinate the robots achieving good patrolling behaviors when facing typical uncertainty and noise (e.g., localization and navigation errors) associated to real platforms.

Evaluation of Auction-Based Multi-Robot Routing by Parallel Simulation

Auction methods are a promising approximation approach for distributed routing including multi-robot routing, where targets on a map need to be allocated to agents while a team objective is satisfied. While many algorithms based on sequential single-item (SSI) auctions have been presented, they are currently evaluated by serial simulation where agents serially calculate their bids on a single machine. We consider a scenario where a bidding algorithm incurs significant computational overhead due to on-demand calculations of the shortest distances on a road map. We evaluate the bidding algorithm under parallel simulations where agents perform bid calculations simultaneously on a parallel machine, and reveal that the algorithm suffers from severe synchronization overhead ignored by serial simulation. We also present the broadcasting and speculation techniques to alleviate such synchronization overhead. Our empirical results on multi-robot routing variants show that both techniques improve the efficiency of parallelization, and speculation achieves more significant improvement.

Resource Allocation for Multiple-User AF-OFDMA Systems Using the Auction Framework

We develop auction-based algorithms for joint allocation of resources, i.e., power profiles at the source and relay nodes and subcarrier assignment profile for multiple-user amplify-and-forward (AF) orthogonal frequency-division multiple-access (OFDMA) systems. The first algorithm is based on sequential single-item auction, where each user submits a bid based on either the marginal increase or the relative marginal increase in the data rate using the subcarrier. The first bidding strategy maximizes the sum data rate, whereas the second bidding strategy maximizes the fairness index. In both cases, the subcarrier is assigned to the user who submits the highest bid. The algorithm proceeds in a sequential fashion until all subcarriers are assigned. To reduce the synchronized interactions between the base station and the users, we propose a one-shot auction algorithm, where each user submits bids for all subcarriers at once based on the Shapley value, a well-known cooperative-game theoretic concept. The user evaluates each subcarrier based on an estimation of the Shapley value. The subcarriers are then assigned based on the submitted bids using an iterative algorithm that maximizes the fairness index. The throughput and fairness indices are used to evaluate the performance of the proposed algorithms. Numerical results are used to show the merits of each algorithm.

Minimising Undesired Task Costs in Multi-Robot Task Allocation Problems with In-Schedule Dependencies

In multi-robot task allocation problems with in-schedule dependencies, tasks with high costs have a large influence on the total time required for a team of robots to complete all tasks. We reduce this influence by calculating a novel task cost dispersion value that measures robots' collective preference for each task. By modifying the winner determination phase of sequential single-item auctions, our approach inspects the bids for every task to identify tasks which robots collectively consider to be high cost and ensures these tasks are allocated prior to other tasks.Our empirical results show this method provides a significant reduction in the total time required to complete all tasks.

Combinatorial Auctions for Player Selection in the Indian Premier League (IPL)

In 2008, the benefits of team formation by auction first became apparent in the field of professional cricket. The Indian Premier League organized sequential single-item auctions to allocate 77 cricketers to eight teams that participated in a Twenty20 cricket tournament. We argue in this article that the auction format used resulted in distortion of player valuations. We recommend a combinatorial auction scheme MRPF which enables bidders to exploit the complementary nature of the resources. A laboratory experiment undertaken to test the feasibility of MRPF indicated that it would result in a more efficient auction system.

Development of the residual power prediction system for mobile robots

The article presents a multiple residual power prediction system to be applied in mobile robots or automation fields. The system contains multiple power detection units to measure multiple online power values. Each power detection unit uses four current sensors to measure the current variety, and uses weighted average method and redundant management method to calculate the exact current value, and isolates faulty measurement values. We use the proposed algorithms to be applied in voltage detection of each power detection unit. Then we can calculate the real-time power values according to the current and voltage measurement values. The control core of the power detection unit is HOLTEK microchip, and communicates with the data integration unit via wire I2C interface. The power detection units transmit the measurement values of current and voltage to the controller of the system. The main controller of the power detection system is PC-based system, and communicates with the data integration unit via wire RS232 interface. The main controller of the system can controls each power output ratio of the power sources, and predicts the power loading and the residual power for each power detection unit using auto-regression algorithm, and computes the residual time of mobile robots to work in the free space, and arrange the residual power of the enough power source to the weakness power sources using sequential single-item auction algorithm. In experiment result, the residual power prediction system can adjusts the power sources to increase the working time.