mlsanityData to 14 Oct 2026

Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments

arXiv 2412.02176 ↗First posted 3 Dec 2024cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper introduces SmartBSP, an advanced self-supervised learning framework for real-time path planning and obstacle avoidance in autonomous robotics navigating through complex environments. The proposed system integrates Proximal Policy Optimization (PPO) with Convolutional Neural Networks (CNN) and Actor-Critic architecture to process limited LIDAR inputs and compute spatial decision-making probabilities. The robot's perceptual field is discretized into a grid format, which the CNN analyzes to produce a spatial probability distribution. During the training process a nuanced cost function is minimized that accounts for path curvature, endpoint proximity, and obstacle avoidance. Simulations results in different scenarios validate the algorithm's resilience and adaptability across diverse operational scenarios. Subsequently, Real-time experiments, employing the Robot Operating System (ROS), were carried out to assess the efficacy of the proposed algorithm.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 28 references that are in the map. First posted on arXiv 3 Dec 2024.

Builds on

4 of 28 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Value Iteration Networkscited by 160
  3. 2017PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planningcited by 69
  4. 2019RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policiescited by 20

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  3. 2026Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors
  4. 2025Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion
  5. 2023Speeding Up Optimization-based Motion Planning through Deep Learning
  6. 2026StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance
  7. 2024Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control
  8. 2024Learning Probabilistic Obstacle Spaces from Data-driven Uncertainty using Neural Networks