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Efficient Collision Detection Framework for Enhancing Collision-Free Robot Motion

Xiankun Zhu, Yucheng Xin, Shoujie Li, Houde Liu, Chongkun Xia, Bin Liang

TL;DR

The paper tackles real-time, collision-free motion for articulated robots by introducing SDF-SC, a differentiable framework that fuses parallel per-link SDF networks with a self-collision boundary learned via support vector machines. By unifying external and self-collision distances into a single metric and preserving differentiability, the approach enables efficient trajectory optimization and reactive control in dynamic environments. Key contributions include a lightweight parallel SDF architecture, an integrated SVM-based self-collision component, and a reactive controller that demonstrates robustness and real-time performance on the Franka Emika Panda. The results show up to fivefold speedups over prior methods with millimeter-level accuracy, highlighting the framework's practical potential for safe, autonomous robot motion in complex settings.

Abstract

Fast and efficient collision detection is essential for motion generation in robotics. In this paper, we propose an efficient collision detection framework based on the Signed Distance Field (SDF) of robots, seamlessly integrated with a self-collision detection module. Firstly, we decompose the robot's SDF using forward kinematics and leverage multiple extremely lightweight networks in parallel to efficiently approximate the SDF. Moreover, we introduce support vector machines to integrate the self-collision detection module into the framework, which we refer to as the SDF-SC framework. Using statistical features, our approach unifies the representation of collision distance for both SDF and self-collision detection. During this process, we maintain and utilize the differentiable properties of the framework to optimize collision-free robot trajectories. Finally, we develop a reactive motion controller based on our framework, enabling real-time avoidance of multiple dynamic obstacles. While maintaining high accuracy, our framework achieves inference speeds up to five times faster than previous methods. Experimental results on the Franka robotic arm demonstrate the effectiveness of our approach.

Efficient Collision Detection Framework for Enhancing Collision-Free Robot Motion

TL;DR

The paper tackles real-time, collision-free motion for articulated robots by introducing SDF-SC, a differentiable framework that fuses parallel per-link SDF networks with a self-collision boundary learned via support vector machines. By unifying external and self-collision distances into a single metric and preserving differentiability, the approach enables efficient trajectory optimization and reactive control in dynamic environments. Key contributions include a lightweight parallel SDF architecture, an integrated SVM-based self-collision component, and a reactive controller that demonstrates robustness and real-time performance on the Franka Emika Panda. The results show up to fivefold speedups over prior methods with millimeter-level accuracy, highlighting the framework's practical potential for safe, autonomous robot motion in complex settings.

Abstract

Fast and efficient collision detection is essential for motion generation in robotics. In this paper, we propose an efficient collision detection framework based on the Signed Distance Field (SDF) of robots, seamlessly integrated with a self-collision detection module. Firstly, we decompose the robot's SDF using forward kinematics and leverage multiple extremely lightweight networks in parallel to efficiently approximate the SDF. Moreover, we introduce support vector machines to integrate the self-collision detection module into the framework, which we refer to as the SDF-SC framework. Using statistical features, our approach unifies the representation of collision distance for both SDF and self-collision detection. During this process, we maintain and utilize the differentiable properties of the framework to optimize collision-free robot trajectories. Finally, we develop a reactive motion controller based on our framework, enabling real-time avoidance of multiple dynamic obstacles. While maintaining high accuracy, our framework achieves inference speeds up to five times faster than previous methods. Experimental results on the Franka robotic arm demonstrate the effectiveness of our approach.
Paper Structure (12 sections, 16 equations, 7 figures, 4 tables)

This paper contains 12 sections, 16 equations, 7 figures, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. Framework Composition
  3. Parallel Signed Distance Field Networks
  4. Self-Collision Detection
  5. Dataset Construction
  6. Hyperparameter Search and Framework Evaluation
  7. Hyperparameter Search
  8. Framework Evaluation
  9. Montion Planning Experiments
  10. Trajectory Optimization with SDF-SC
  11. Reactive Control with SDF-SC
  12. CONCLUSIONS

Figures (7)

  • Figure 1: Overview of our work. (a) We combine our own lightweight SDF framework with self-collision detection to obtain a new collision detection framework SDF-SC. (b) Trajectory Optimization based on the SDF-SC. (c) We use SDF-SC to enable reactive control of the robot.
  • Figure 2: Overall algorithm pipeline for estimating collision distance. Collision distance refers to the safety margin between the robot and potential collisions.
  • Figure 3: (a) The score distribution of the self-collision detection model on the validation set. (b) The impact of different network architectures on fitting accuracy.
  • Figure 4: The reconstruction of the Franka Emika Panda's links is performed based on the distance isosurfaces $D(\boldsymbol{q}, \boldsymbol{p})=$0 cm (solid) and $D(\boldsymbol{q}, \boldsymbol{p})=$5 cm (transparent). Different colors represent different links.
  • Figure 5: Trajectory optimization in simple (left) and complex (right) scenarios, where the red dashed line represents the trajectory generated by RRT*, and the green dashed line represents the trajectory optimized using SDF-SS with RRT-connect.
  • ...and 2 more figures