# Edge Accelerated Robot Navigation With Collaborative Motion Planning

arXiv: https://arxiv.org/abs/2311.08983 · HTML: https://mlsanity.com/paper/2311.08983/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2023.

## Built on by (most cited first, 5 of 5)

- [Opportunistic Collaborative Planning with Large Vision Model Guided Control and Joint Query-Service Optimization](https://mlsanity.com/paper/2504.18057/) (2025)
- [Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication](https://mlsanity.com/paper/2510.22718/) (2025)
- [IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking](https://mlsanity.com/paper/2606.08729/) (2026)
- [Unveiling Uncertainty-Aware Autonomous Cooperative Learning Based Planning Strategy](https://mlsanity.com/paper/2510.11041/) (2025)
- [SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control](https://mlsanity.com/paper/2608.21175/) (2026)

## Abstract (arXiv)

Low-cost distributed robots suffer from limited onboard computing power, resulting in excessive computation time when navigating in cluttered environments. This paper presents Edge Accelerated Robot Navigation (EARN), to achieve real-time collision avoidance by adopting collaborative motion planning (CMP). As such, each robot can dynamically switch between a conservative motion planner executed locally to guarantee safety (e.g., path-following) and an aggressive motion planner executed non-locally to guarantee efficiency (e.g., overtaking). In contrast to existing motion planning approaches that ignore the interdependency between low-level motion planning and high-level resource allocation, EARN adopts model predictive switching (MPS) that maximizes the expected switching gain with respect to robot states and actions under computation and communication resource constraints. The MPS problem is solved by a tightly-coupled decision making and motion planning framework based on bilevel mixed-integer nonlinear programming and penalty dual decomposition. We validate the performance of EARN in indoor simulation, outdoor simulation, and real-world environments. Experiments show that EARN achieves significantly smaller navigation time and higher success rates than state-of-the-art navigation approaches.