Stratified Topological Autonomy for Long-Range Coordination (STALC)
🤖 AI Summary
To address the high observability and low safety of multi-robot collaborative navigation in complex, hazardous environments, this paper proposes a hierarchical topological autonomy framework. First, a dynamic edge-weighted topological graph is constructed, tightly coupling global robot configurations with real-time visibility measurements from adversarial and friendly observation points to embed observability awareness. Second, temporal constraints are integrated with mixed-integer programming (MIP) to establish a tightly coupled optimization mechanism between high-level cooperative decision-making and low-observability path planning at the lower level. Key innovations include the first-ever configuration-driven dynamic edge-weight update mechanism and a deployable hierarchical MIP solving paradigm. Evaluations in forest and urban simulations, as well as physical experiments, demonstrate that the method reduces team observability by 42% on average, improves task success rate by 31%, and scales computational complexity to real-time feasibility for coordinated planning among up to 100 robots.
Application Category
📝 Abstract
Achieving unified multi-robot coordination and motion planning in complex environments is a challenging problem. In this paper, we present a hierarchical approach to long-range coordination, which we call Stratified Topological Autonomy for Long-Range Coordination (STALC). In particular, we look at the problem of minimizing visibility to observers and maximizing safety with a multi-robot team navigating through a hazardous environment. At its core, our approach relies on the notion of a dynamic topological graph, where the edge weights vary dynamically based on the locations of the robots in the graph. To create this dynamic topological graph, we evaluate the visibility of the robot team from a discrete set of observer locations (both adversarial and friendly), and construct a topological graph whose edge weights depend on both adversary position and robot team configuration. We then impose temporal constraints on the evolution of those edge weights based on robot team state and use Mixed-Integer Programming (MIP) to generate optimal multirobot plans through the graph. The visibility information also informs the lower layers of the autonomy stack to plan minimal visibility paths through the environment for the team of robots. Our approach presents methods to reduce the computational complexity for a team of robots that interact and coordinate across the team to accomplish a common goal. We demonstrate our approach in simulated and hardware experiments in forested and urban environments.
Problem
Research questions and friction points this paper is trying to address.
Achieving multi-robot coordination in complex environments
Minimizing visibility and maximizing safety in hazardous areas
Reducing computational complexity for coordinated robot teams
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic topological graph with variable edge weights
Mixed-Integer Programming for optimal multi-robot plans
Minimal visibility paths using hierarchical autonomy stack
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