Multi-View Projection-Based Self-Interference Detection and Interfering Path Point Optimization for Manipulators

Abstract

When manipulators perform non-repetitive tasks in dynamic environments, the generated trajectories are often highly nonlinear and difficult to verify in advance, which increases the risk of self-interference during execution. Existing studies mainly rely on detecting abrupt changes in physical signals after interference occurs, which may cause structural impact and damage. Geometric modeling approaches based on simplified bounding structures can provide predictive detection, but their conservative representations often introduce redundant envelope space and reduce motion flexibility. To address these limitations, this paper proposes a manipulator self-interference detection and path optimization method based on multi-view projection. First, an equivalent-volume representation of the manipulator is constructed by projecting the three-dimensional structure onto feature-sensitive planes and extending contour edge points along normal directions to form a compact three-layer key-point set. Then, the separability of projected key-point sets on interference-discriminative projection planes is evaluated through a geometric discrimination function to determine potential self-interference at path points. For the path points identified as interfering, a local iterative adjustment strategy based on the separating line is further applied to modify the path while preserving the original path geometry as much as possible. Simulation and experimental results demonstrate that the proposed method effectively improves self-interference detection reliability and path optimization efficiency, showing strong potential for practical industrial applications.