Trajectory Optimization for Collision-Aware Redundant Robotic Multi-Axis Additive Manufacturing by Constrained Gradient Projection
2026-06-29 • Robotics
RoboticsComputational GeometryGraphics
AI summaryⓘ
The authors developed a new method to plan the movements of robotic arms for 3D printing complex shapes without needing extra supports. They created a way to carefully track the printer nozzle's position and avoid collisions while keeping the path smooth and accurate. Their tests showed improved speed, reduced sudden joint movements, and accurate printing results with fewer defects. This method was demonstrated on an 8-joint robot and confirmed through real printing experiments.
Redundant robotic manipulatorAdditive manufacturingTrajectory optimizationCollision avoidanceJacobian matrixSigned distance function (SDF)Self-motion manifoldJoint jerkSupport-free fabricationSQP (Sequential Quadratic Programming)
Authors
Zhikai Shen, Jiasheng Qu, Chenyu Xu, Zhuo Huang, Chengkai Dai, Yongzhe Li, Guoxin Fang
Abstract
Redundant robotic multi-axis additive manufacturing (MAAM) enables support-free and conformal fabrication, but trajectory optimization for long-horizon paths remains challenging under strict deposition-position constraints and time-varying collision constraints. This work proposes a computational framework for collision-aware trajectory optimization in redundant robotic MAAM. We first formulate nozzle-workpiece relative kinematics using a relative Jacobian, and develop a differentiable SDF-based collision model that captures fabrication-induced geometry evolution and provides optimization gradients. The deposition position is then enforced as a hard waypoint-wise equality constraint through iterative projection onto the self-motion manifold, with the loss gradient restricted to the corresponding tangent space. Experiments on an 8-DOF robotic MAAM platform with diverse long-horizon support-free and conformal toolpaths show that our method maintains a mean nozzle-position error below 10μm, reduces maximum joint jerk by up to $77.6\%$, and eliminates all sampled collision and orientation violations. Compared with the SQP-based baseline, it achieves up to a 10.2x speedup and improved convergence. Physical fabrication experiments further verify that the resulting smooth, collision-free trajectories enable successful printing of complex geometries with fewer visible deposition artifacts.