Skip to content
All research
Research 03Published · simulation

Energy-Efficient Trajectory Planning for High-DoF Robots

Sinusoidal trajectories with velocity scaling to reduce energy use without sacrificing smoothness or accuracy.

First German Robotics Conference, Nuremberg, 2025 (abstract)

Read on arXiv

System overview

  1. 01Task waypoints
  2. 02Sinusoidal profile
  3. 03Velocity scaling
  4. 04Energy-aware cost
  5. 057-DoF arm (PyBullet)
Simplified flow of Energy-efficient planning, following the published description.

The problem

High-DoF arms on assembly and handling lines spend much of their energy budget on motion that could be smoother. The aim was a planning framework that treats energy, smoothness and accuracy as explicit, competing objectives.

Approach

  1. 01Trajectories are generated as sinusoidal profiles, which avoid abrupt changes in acceleration.
  2. 02Velocity scaling adjusts each segment to reduce energy consumption, evaluated through an energy-aware cost function.
  3. 03The framework is tested on a 7-DoF manipulator model with realistic kinematic and dynamic constraints in PyBullet.

Evidence

The study reports steady cumulative energy consumption without spikes and smooth transitions in simulation. Real-time adjustment and real-robot validation are planned next.

Scope and limitations

Simulation-only. Presented as a peer-reviewed conference abstract; the full text is available on arXiv.

Publication

2025

Conference

Conference abstract

A Robust and Energy-Efficient Trajectory Planning Framework for High-Degree-of-Freedom Robots

First German Robotics Conference, Nuremberg · Peer-reviewed conference abstract · full text on arXiv

Sinusoidal trajectory generation with velocity scaling to reduce energy use while keeping motion smooth and accurate, evaluated on a 7-DoF arm in PyBullet.

arXiv for A Robust and Energy-Efficient Trajectory Planning Framework for High-Degree-of-Freedom Robots (opens in a new tab)Record for A Robust and Energy-Efficient Trajectory Planning Framework for High-Degree-of-Freedom Robots (opens in a new tab)Case study