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Incentive-Compatible and Distributed Allocation for Robotic Service Provision Through Contract Theory

Yuhan Zhao, Quanyan Zhu

TL;DR

This work proposes a contract-based approach to enable incentive-compatible service selection and develops a distributed allocation algorithm that incorporates robot dynamics and collision avoidance to allocate service robots and address scalability concerns associated with increasing numbers of service robots and users.

Abstract

Robot allocation plays an essential role in facilitating robotic service provision across various domains. Yet the increasing number of users and the uncertainties regarding the users' true service requirements have posed challenges for the service provider in effectively allocating service robots to users to meet their needs. In this work, we first propose a contract-based approach to enable incentive-compatible service selection so that the service provider can effectively reduce the user's service uncertainties for precise service provision. Then, we develop a distributed allocation algorithm that incorporates robot dynamics and collision avoidance to allocate service robots and address scalability concerns associated with increasing numbers of service robots and users. We conduct simulations in eight scenarios to validate our approach. Comparative analysis against the robust allocation paradigm and two alternative uncertainty reduction strategies demonstrates that our approach achieves better allocation efficiency and accuracy.

Incentive-Compatible and Distributed Allocation for Robotic Service Provision Through Contract Theory

TL;DR

This work proposes a contract-based approach to enable incentive-compatible service selection and develops a distributed allocation algorithm that incorporates robot dynamics and collision avoidance to allocate service robots and address scalability concerns associated with increasing numbers of service robots and users.

Abstract

Robot allocation plays an essential role in facilitating robotic service provision across various domains. Yet the increasing number of users and the uncertainties regarding the users' true service requirements have posed challenges for the service provider in effectively allocating service robots to users to meet their needs. In this work, we first propose a contract-based approach to enable incentive-compatible service selection so that the service provider can effectively reduce the user's service uncertainties for precise service provision. Then, we develop a distributed allocation algorithm that incorporates robot dynamics and collision avoidance to allocate service robots and address scalability concerns associated with increasing numbers of service robots and users. We conduct simulations in eight scenarios to validate our approach. Comparative analysis against the robust allocation paradigm and two alternative uncertainty reduction strategies demonstrates that our approach achieves better allocation efficiency and accuracy.
Paper Structure (18 sections, 2 theorems, 24 equations, 3 figures, 4 tables, 1 algorithm)

This paper contains 18 sections, 2 theorems, 24 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. Basic Settings
  5. User Model
  6. Service Provider Model
  7. Contract Analysis and Distributed Implementation
  8. Payment Sub-Problem
  9. Interpretation of Prop. \ref{['prop:1']}
  10. Allocation Sub-Problem
  11. Contract with Distributed Allocation
  12. Simulations and Evaluations
  13. Simulation Settings
  14. Simulation Results of Contract-based Allocation
  15. Comparison with Other Uncertainty Reduction
  16. ...and 3 more sections

Key Result

proposition thmcounterproposition

Assume $g(1) < \frac{K}{K-1}$. Given the allocation variable $\langle \bm{b}, \bm{x} \rangle$, the optimal payment that corresponds to $b^k_{ij} = 1$ is given by The payment $\rho^k_{ij}$ that corresponds to $b^k_{ij} = 0$ is trivially zero since no robot is assigned to the user.

Figures (3)

  • Figure 1: The service provider assigns various service robots to users based on their specific service requests. However, users may inaccurately report their needs, resulting in resource mismatches. We propose a contract-based approach, including optimal payment design and distributed allocation, to ensure accurate and efficient allocation of service robots to designated users.
  • Figure 2: The allocation trajectories of the robot with different service types in Scenario 8 (five service types and 100 users). After identifying the user type with the optimal payment, all service robots with different types successfully go to users using our distributed allocation algorithm. Some zigzag trajectories are mainly due to collision avoidance. The dashed lines indicate the user assignment after the allocation.
  • Figure 3: Case-by-case locational energy differences per 50 simulations for different scenarios. The energy difference is obtained by subtracting our result from comparing methods.

Theorems & Definitions (6)

  • remark thmcounterremark
  • proposition thmcounterproposition
  • proof
  • remark thmcounterremark
  • proposition thmcounterproposition
  • proof