Adaptive Sparsity Optimization for Dynamic Environment Navigation

Jin et al. (2024) introduced sparsity to minimize excessive joint movements in redundant manipulators, but this approach lacks context-awareness. This research extends their work by creating an adaptive sparsity controller that modulates movement sparsity in real-time based on obstacle density and task urgency. Unlike static sparsity optimization, the proposed system uses stochastic template-based RRT* (Yang & Shimosaka, 2025) to generate motion primitives with variable sparsity levels, guided by a neural network trained on environmental complexity metrics. For example, in tight spaces (like Tajbakhsh et al.'s multi-robot scenarios), sparsity increases to reduce collision risks, while in open areas, smoother motions prioritize efficiency. This bridges the gap between energy-aware control (Jin et al.) and dynamic adaptability (Cho & Jung, 2024).

References:

1. Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning. Ardalan Tajbakhsh, L. Biegler, Aaron M. Johnson (2023). IEEE International Conference on Robotics and Automation.
2. Collective Neural Dynamics for Sparse Motion Planning of Redundant Manipulators Without Hessian Matrix Inversion. Long Jin, Jinchuan Zhao, Liangming Chen, Shuai Li (2024). IEEE Transactions on Neural Networks and Learning Systems.
3. Reinforcement Learning-Based Motion Planning for Robotic Manipulators in Smart Industry. Junhyung Cho, Soyi Jung (2024). Information and Communication Technology Convergence.
4. Efficient and Asymptotically Optimal Vehicle Motion Planning With Stochastic Template-Based RRT*. Shaoyu Yang, Masamichi Shimosaka (2025). IEEE Access.