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

Ruihua Han, Shuai Wang, Shuaijun Wang, Zeqing Zhang, Qianru Zhang, Yonina C. Eldar, Qi Hao, Jia Pan

Research output: Contribution to journalArticlepeer-review


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 letter, 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.

Original languageEnglish
Pages (from-to)1715-1722
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
Early online date3 Feb 2023
StatePublished - 1 Mar 2023


  • Collision avoidance
  • constrained motion planning
  • optimization and optimal control

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Computer Science Applications


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