Heterogeneous Vehicle Routing and Teaming with Gaussian Distributed Energy Uncertainty

Fu, Bo; Smith, William; Rizzo, Denise; Castanier, Matthew P.; Barton, Kira

doi:10.1109/iros45743.2020.9341433

Cited by 6 publications

(11 citation statements)

References 15 publications

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“…The stochastic vehicle routing problem (SVRP) is a variation of VRP where some of the parameters in the optimization are random distributions. Previous works in the field of SVRP have investigated optimization under uncertain requests number at each POI [15][16][17][18], uncertain time costs for traveling edges or service at a POI [19][20][21][22], and uncertain energy costs [23][24][25]. This work considers the uncertainty in time costs.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

Fu,

Kathuria,

Rizzo

et al. 2022

Preprint

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This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. In the proposed centralized multi-robot planner, a simultaneous matching and routing problem (SMRP) is formulated to match the humans with robot guides according to their selected places of interest (POIs) and generate the routes and schedules for the robots according to uncertain spatial and time estimation. A large neighborhood search algorithm is developed to efficiently find sub-optimal lowcost solutions for the SMRP. The scalability and optimality of the multi-robot planner are evaluated computationally under different numbers of humans, robots, and POIs. The largest case tested involves 50 robots, 250 humans, and 50 POIs. Then, a photo-realistic multi-robot simulation platform was developed based on Habitat-AI to verify the tour guiding performance in an uncertain indoor environment. Results demonstrate that the proposed centralized tour planner is scalable, makes a smooth trade-off in the plans under different environmental constraints, and can lead to robust performance with inaccurate uncertainty estimations (within a certain margin).

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Section: Related Workmentioning

confidence: 99%

Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

Fu,

Kathuria,

Rizzo

et al. 2022

Preprint

Self Cite

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“…In [16], we dealt with a deterministic variation of such a problem, where we assumed exact information (instead of a distribution) of the agent capabilities and task requirements was known. In this work, we develop a generalizable framework for task assignment and scheduling that systematically represents heterogeneous and uncertain task requirements and agent capabilities.…”

Section: A Contributionsmentioning

confidence: 99%

“…The requirement to drive these functions to 1 could be satisfied with appropriate team formation planning. In our model, this requirement can be encoded as linear constraints [16]. In this representation, the only part that could introduces nonconvexity is the logic ∨ which takes the union of two feasible regions.…”

Section: A Heterogeneous Teaming Problem Descriptionmentioning

confidence: 99%

Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty

Fu¹,

Smith²,

Rizzo³

et al. 2021

Preprint

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This paper develops a stochastic programming framework for multi-agent systems where task decomposition, assignment, and scheduling problems are simultaneously optimized. 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 a unique 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 to avoid the non-convex task decomposition enumeration: 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., . Results demonstrate that the framework is scalable, generalizable, and provides low-cost plans that ensure a high probability of success.

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“…While heterogeneity in multi-agent teams may offer benefits, these benefits can only be understood by analyzing optimal heterogeneous team compositions and dynamically distributing tasks among agents in uncertain environments, which remain challenging. Although optimization-based MATA problems can handle constraints between heterogeneous tasks and agents [6], the task demands are usually satisfied upon one-step assignment [8] and analysis of dynamically changing demands affected by agent decisions and capabilities is still limited [7], [9]. In particular, a Dec-POMDP formulation has not been formally utilized by a team of heterogeneous agents to provide the comprehensive modeling of the dynamics between agent capabilities, task demands, and perception accuracy.…”

mentioning

confidence: 99%

“…For more information, see https://creativecommons.org/licenses/by/4.0/ the stability and adaptive behavior of each agent. Heterogeneity in a team is often considered as the difference in rules of engagement or assignment constraints [6], task suitability [7], or functional heterogeneity [8]. In addition to the heterogeneity in agent task-related capabilities, the difference of agent risk tolerance in decision-making, which has rarely been mentioned in the literature of MATA, also plays a significant role in overall performance especially in human-autonomy teaming under uncertain environments.…”

mentioning

confidence: 99%

Impact of Heterogeneity and Risk Aversion on Task Allocation in Multi-Agent Teams

Ghadami

Bayrak

et al. 2021

IEEE Robot. Autom. Lett.

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Cooperative multi-agent decision-making is a ubiquitous problem with many real-world applications. In many practical applications, it is desirable to design a multi-agent team with a heterogeneous composition where the agents can have different capabilities and levels of risk tolerance to address diverse requirements. While heterogeneity in multi-agent teams offers benefits, new challenges arise including how to find optimal heterogeneous team compositions and how to dynamically distribute tasks among agents in complex operations. In this work, we develop an artificial intelligence framework for multi-agent heterogeneous teams to dynamically learn task distributions among agents through reinforcement learning. The framework extends Decentralized Partially Observable Markov Decision Processes (Dec-POMDP) to be compatible to model various types of heterogeneity. We demonstrate our approach with a benchmark problem on a disaster relief scenario. The effect of heterogeneity and risk aversion in agent capabilities and decision-making strategies on the performance of multi-agent teams in uncertain environments is analyzed. Results show that a well-designed heterogeneous team outperforms its homogeneous counterpart and possesses higher adaptivity in uncertain environments.

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Heterogeneous Vehicle Routing and Teaming with Gaussian Distributed Energy Uncertainty

Cited by 6 publications

References 15 publications

Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty

Robust Task Scheduling for Heterogeneous Robot Teams under Capability Uncertainty

Impact of Heterogeneity and Risk Aversion on Task Allocation in Multi-Agent Teams

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