Energy-Optimal Collision-Free Motion Planning for Multiaxis Motion Systems: An Alternating Quadratic Programming Approach

Yiming Zhao, Yebin Wang, Meng Chu Zhou, Jing Wu

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


This work investigates energy-optimal motion planning for a class of multiaxis motion systems where the system dynamics are linear time-invariant and decoupled in each axis. Solving the problem in a reliable and efficient manner remains challenging owing to the presence of various constraints on control and states, nonconvexity in its cost function, and obstacles. This paper shows how the cost function can be convexified by considering the system dynamics, while decomposing decision variables to obtain a convex representation of collision avoidance constraints. With the convexified cost function and constraints, the original problem is decomposed into two quadratic programming (QP) problems. An alternating quadratic programming (AQP) algorithm is proposed to solve both the QP problems alternatingly and iteratively till convergence. Requiring an initial feasible trajectory as a guess, AQP necessarily converges to an energy-efficient solution that is homotopic to the initial guess. Under certain circumstances, AQP is guaranteed to produce a local optimum. Simulation demonstrates that AQP is computationally efficient and reliable while claiming comparable energy saving as the mixed-integer QP approach. Note to Practitioners-This paper presents an energy-optimal motion planning algorithm that can be easily implemented on a class of multiaxis motion systems. Main advantages of the proposed algorithm are: 1) it produces a trajectory resulting in lower but comparable energy efficiency as the global optimum; 2) it is guaranteed to provide an energy-efficient and constraint-compliant trajectory, and thus is reliable; 3) it requires a low computational load and can be deployed on a wide range of applications; and 4) its implementation is straightforward to any engineer with basic knowledge of numerical methods.

Original languageEnglish (US)
Article number8466117
Pages (from-to)327-338
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Issue number1
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • Constrained optimization
  • minimum energy
  • motion planning
  • obstacle avoidance
  • quadratic programming (QP)


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