Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

Abstract: We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent. The agent has access to a time-varying transit vehicle network in which it can use multiple modes of transportation. For instance, a drone can either fly or ride on terrain vehicles for segments of their routes. DREAMR is a difficult problem of sequential decision making under uncertainty with both discrete and continuous variables. We design a novel hierarchical hybrid planning framework to solve the DREAMR problem that exploits its structural decomposability. Our framework consists of a global open-loop planning layer that invokes and monitors a local closed-loop execution layer. Additional abstractions allow efficient and seamless interleaving of planning and execution. We create a large-scale simulation for DREAMR problems, with each scenario having hundreds of transportation routes and thousands of connection points. Our algorithmic framework significantly outperforms a receding horizon control baseline, in terms of elapsed time to reach the destination and energy expended by the agent.

• The paper introduces the Hierarchical Hybrid Planning (HHP) framework to solve the Dynamic Real-time Multimodal Routing (DREAMR) problem for autonomous agents in dynamic transit networks.
• HHP uses a hierarchical decomposition, combining global open-loop planning via a DAG with local closed-loop execution to manage discrete and continuous variables under uncertainty.
• Numerical results show HHP outperforms a receding horizon control baseline in metrics like energy usage and time to destination, demonstrating scalability and adaptability for complex applications.

Overview of Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

The paper, "Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning," focuses on a complex issue associated with planning and executing optimal routes for autonomous agents within dynamic transit vehicle networks. This is identified as the Dynamic Real-time Multimodal Routing (DREAMR) problem. This work addresses the deficiencies in existing multimodal routing approaches, particularly their lack of adaptivity for real-time decision-making in dynamic, uncertain environments typical for autonomous agents.

Rooted in the foundation of sequential decision-making under uncertainty, DREAMR incorporates both discrete and continuous variables and seeks to optimize multimodal transportation modes, for instance allowing a drone to interchange between flying or riding on terrestrial vehicles. The proposed solution is a hierarchical hybrid planning framework that efficiently splits the problem into manageable sub-problems, introducing both a global open-loop planning layer and a local closed-loop execution layer. The interleaving of these components is instrumental in achieving seamless and effective decision-making.

Problem Formulation and Approach

DREAMR is rigorously formulated as an Online Stochastic Shortest Path problem involving discrete-time, unconstrained episodic Markov Decision Processes (MDPs). The solution framework leverages a decomposition strategy taking advantage of DREAMR's intrinsic structural properties. A global open-loop planning function utilizes a time-dependent directed acyclic graph (DAG) to accomplish effective route planning, while a closed-loop local layer is responsible for real-time execution of continuous control actions. This hierarchical approach delineates decisions into discrete segments (such as mode and duration of transport), while concurrently managing real-time uncertainties in transit options.

Key Contributions and Results

The novel Hierarchical Hybrid Planning (HHP) framework merits attention for effectively melding multimodal routing techniques with hierarchical stochastic planning. Core contributions include:

• A decomposable representation of DREAMR as an Online Stochastic Shortest Path problem, adeptly handling both discrete and continuous variables.
• The synthesis of graph search algorithms with macro-actions for dynamic decision-making at both global and local levels.
• Demonstrated scalability of the approach, effectively managing networks with numerous routes and connection points.

Numerical results in controlled experiments indicate that the proposed planning framework surpasses a receding horizon control baseline in key performance metrics, including minimized energy expenditure and reduced time to destination.

Implications and Future Directions

The implications of this research are manifold, both practically and theoretically. Practically, the framework offers scalability and adaptability for dynamic multimodal transit networks, suggesting potential applications in urban traffic systems, aerial-ground delivery coordination, search-and-rescue operations, and more. Theoretically, the work expands the horizons of stochastic planning with hierarchical hybrids, setting a foundation for further exploration in the handling of uncertainties in mixed discrete-continuous environments.

Future directions of this work involve extending the framework to accommodate multiple autonomous agents and incorporating additional performance criteria. Developing improved simulation environments and integrating more sophisticated temporal and spatial planning strategies will also be crucial. Furthermore, theoretical validation of the optimality and robustness of this approach could reinforce its applicability and effectiveness in real-world scenarios. This paper sets a promising trajectory for subsequent innovations in advanced planning systems for autonomous networks.