Evaluation of Deep Learning Methods for Robot Motion Planning

Efficient trajectory planning is critical for enhancing robot performance, lifespan, and capabilities. This paper provides a comparative analysis of three fundamental trajectory planning techniques - polynomial interpolation, trapezoidal velocity curve, and seven-segment S-curve methods. The goal is evaluating the optimality of each approach for point-to-point robot motion planning under specific constraints. The trajectories are assessed based on efficiency in reaching target positions and orientations while optimizing time and ensuring continuity of position, velocity, and acceleration. Furthermore, the study explores the integration of deep learning within the motion planning pipeline. Deep neural networks can model complex environments and infer smooth, optimized trajectories encapsulating years of experience. Different network architectures and training methodologies are implemented and evaluated on a physical robotic arm. Results indicate deep learning-powered trajectories achieve substantially higher planning efficiency and navigation success compared to conventional methods. Ablation studies provide valuable insights into model design choices and training strategies. This research delivers an in-depth assessment of both classical and learning-based approaches to robot trajectory planning. Through comparative analysis using real-world experiments, optimal strategies are identified to balance planning speed, path quality, and control efficiency. The framework provides a robust methodology for evaluating different motion planning techniques. Integrating deep learning paves the way for next-generation intelligent robot systems that leverage experience and environmental data for enhanced autonomy.