Technology Category
Intelligent Robots: Learning & Optimization for ROBPlanning, Routing, and Scheduling: Replanning and Plan RepairMultiagent Systems: Multiagent Planning
Solving the Right Problem with Multi-Robot Formations
🤖 AI Summary
In multi-robot formation control, abstracting complex cost functions into fixed geometric configurations (e.g., square or diamond formations) leads to misalignment between shape abstraction and the true optimization objective—particularly under dynamic environmental conditions, degrading performance. This paper proposes a dynamic formation planning framework comprising two stages: (1) target configuration generation via relative-position optimization under a weighted surrogate cost function; and (2) robust tracking using a Lyapunov-stable, non-cooperative formation controller. We theoretically derive invariance conditions for the weighting parameters, enabling efficient approximation of nonlinear, nonsmooth cost functions. Simulation results demonstrate that the approach reduces single-objective cost by over 75% and achieves 20–40% cost reduction under multi-objective collaborative optimization—significantly outperforming static-shape formation strategies.
Technology Category
Application Category
📝 Abstract
Formation control simplifies minimizing multi-robot cost functions by encoding a cost function as a shape the robots maintain. However, by reducing complex cost functions to formations, discrepancies arise between maintaining the shape and minimizing the original cost function. For example, a Diamond or Box formation shape is often used for protecting all members of the formation. When more information about the surrounding environment becomes available, a static shape often no longer minimizes the original protection cost. We propose a formation planner to reduce mismatch between a formation and the cost function while still leveraging efficient formation controllers. Our formation planner is a two-step optimization problem that identifies desired relative robot positions. We first solve a constrained problem to estimate non-linear and non-differentiable costs with a weighted sum of surrogate cost functions. We theoretically analyze this problem and identify situations where weights do not need to be updated. The weighted, surrogate cost function is then minimized using relative positions between robots. The desired relative positions are realized using a non-cooperative formation controller derived from Lyapunov's direct approach. We then demonstrate the efficacy of this approach for military-like costs such as protection and obstacle avoidance. In simulations, we show a formation planner can reduce a single cost by over 75%. When minimizing a variety of cost functions simultaneously, using a formation planner with adaptive weights can reduce the cost by 20-40%. Formation planning provides better performance by minimizing a surrogate cost function that closely approximates the original cost function instead of relying on a shape abstraction.
Problem
Research questions and friction points this paper is trying to address.
Reduces mismatch between formation shapes and original cost functions
Optimizes relative robot positions using weighted surrogate costs
Improves performance for protection and obstacle avoidance tasks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-step optimization identifies robot relative positions
Weighted surrogate cost approximates original non-linear costs
Non-cooperative formation controller realizes desired positions
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2024-09-20arXiv.orgCitations: 1
