Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty (2106.12111v3)

Abstract: This paper develops a stochastic programming framework for multi-agent systems where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed sub-tasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Due to their inherent flexibility and robustness, multi-agent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multi-agent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk (CVaR) is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.

Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty

The paper "Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty" by Bo Fu et al. addresses the multifaceted challenge of scheduling and allocating tasks to heterogeneous robotic teams in environments rife with uncertainty. This work is significant as it innovatively integrates stochastic programming approaches to tackle the coupled problems of task decomposition, assignment, and scheduling, which have traditionally been approached in isolation.

The authors propose a framework that is particularly adept at managing complex environments where both task requirements and agent capabilities are subject to stochastic variation. A central contribution of this paper is the development of a representation scheme where agent capabilities are modeled as vectors of random distributions, and task requirements are verified by a generalizable binary function. This schema is robust in handling dynamic scenarios such as pandemic service coordination, search and rescue operations, and delivery systems utilizing heterogeneous vehicular resources.

In addressing the challenges inherent in this domain, the framework incorporates Conditional Value at Risk (CVaR) into its optimization objective. CVaR is employed as a robust metric to ensure that the plans generated are not only cost-effective but also have a high probability of success under uncertainty. This choice is validated by experiments demonstrating that the framework can scale to handle up to 140 agents and 40 tasks, producing plans that strike a balance between cost and success probability.

The paper offers several practical insights:

1. Complex Task Decomposition: The ability to dynamically decompose tasks based on real-time capability estimation of heterogeneous agents allows for flexible adaptation to changes in the environment.
2. Generalizable Framework: By employing a stochastic model, the framework maintains generalizability across different operational scenarios without the need for significant recalibration.
3. Risk-Aware Task Scheduling: Incorporating risk metrics directly into the task assignment and scheduling processes innovatively addresses the uncertainty, a departure from methods that rely heavily on deterministic inputs.
4. Efficient Algorithmic Implementation: The proposed two-step process, decoupling flow assignment from individual routing tasks into a solvable network flow problem, enhances computational efficiency. This method stands out in its scalability, which is pivotal in real-world applications.

Future research could explore further integration with learning systems to dynamically adjust risk parameters based on historical task success rates. There is also potential for extending the framework to distributed architectures to improve resilience and robustness further, particularly in environments where communication might be limited.

This paper advances both theoretical and practical aspects of multi-agent systems, particularly in contexts where uncertainty and variability are intrinsic. The application of CVaR in task scheduling outlines a promising direction for future exploration in both autonomous systems and broader AI optimization tasks.