# RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments

arXiv: https://arxiv.org/abs/2210.00192 · HTML: https://mlsanity.com/paper/2210.00192/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2022.

## Built on by (most cited first, 13 of 13)

- [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024)
- [Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning](https://mlsanity.com/paper/2604.01681/) (2026)
- [SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios](https://mlsanity.com/paper/2501.16754/) (2025)
- [Opportunistic Collaborative Planning with Large Vision Model Guided Control and Joint Query-Service Optimization](https://mlsanity.com/paper/2504.18057/) (2025)
- [Agentic Self-Evolutionary Replanning for Embodied Navigation](https://mlsanity.com/paper/2603.02772/) (2026)
- [IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking](https://mlsanity.com/paper/2606.08729/) (2026)
- [iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning](https://mlsanity.com/paper/2305.13663/) (2023)
- [Green Robotic Mixed Reality with Gaussian Splatting](https://mlsanity.com/paper/2504.13697/) (2025)
- [LLM-Driven Scenario-Aware Planning for Autonomous Driving](https://mlsanity.com/paper/2601.21876/) (2026)
- [Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks](https://mlsanity.com/paper/2604.10662/) (2026)
- [Event-Adaptive Motion Planning with Distilled Vision-Language Model in Safety-Critical Situations](https://mlsanity.com/paper/2606.25629/) (2026)
- [SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control](https://mlsanity.com/paper/2608.21175/) (2026)
- [From Semantic Decisions to Feasible Trajectories: Self-Evolving LLM-Guided Optimal Control for Narrow-Space Parking](https://mlsanity.com/paper/2609.24631/) (2026)

## Abstract (arXiv)

Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in both simulation and real-world settings. Experimental results show that the proposed method generates smooth collision-free trajectories with less computation time compared with other benchmarks and performs robustly in cluttered environments. The source code is available at https://github.com/hanruihua/RDA_planner.