Technical Report: Asynchronous Distributed Trajectory Estimation of Multi-Robot Systems
2026-07-01 • Robotics
Robotics
AI summaryⓘ
The authors developed a new method for multiple agents to estimate the positions and movements of robots even when communication and calculations aren't perfectly synchronized. They created a way for agents to share less information—cutting communications by up to 97%—while still making very accurate estimates. Their method quickly finds the best guess of the robots' states and works better than existing approaches in tests and real robot experiments, handling delays that vary a lot in length.
distributed trajectory estimationasynchronous algorithmsblock coordinate descentmaximum a posteriori estimationsliding windowmulti-robot systemsstate estimationcommunication delaysconvergence rate
Authors
Adam Pooley, Matthew Hale
Abstract
Distributed trajectory estimation arises in many applications across robotics, but existing implementations typically do not consider asynchrony in agents' communications and computations. Therefore, we propose an asynchronous block coordinate descent algorithm for distributed trajectory estimation. We consider a team of agents that observes a team of robots and estimates their states over a sliding window. The agents solve an approximation of the maximum a posteriori estimation problem, which we derive. We show this approximation introduces negligible errors and eliminates up to 96.9% of communications among agents. Next, we prove that agents' iterates converge exponentially fast to the optimal estimate of the robots' states. Simulations show that this approach has up to 64% less error than a comparable state-of-the-art algorithm. Experiments on mobile robots show the robustness of this approach to delays whose lengths span three orders of magnitude.