Conscious Frontier Analysis Model: Learning-Based Path Planning for PointGoal Navigation

The objective of PointGoal navigation is to guide mobile robot to their destination in the shortest distance possible without a prioir map. However, due to the environment uncertainty, finding a solution is complicated. To address this issue, we propose a conscious frontier analysis model based on a partially observable Markov decision process. Our model uses synthetic maps for training and selects frontiers (points between known and unknown areas). By leveraging a reinforcement learning framework for path planning, the model efficiently navigates without prior mapping, enhancing exploration and pathfinding capabilities to reach the PointGoal in unknown environments. We utilize a modified belief-based version of the Bellman equation to assess the cost of failing to reach the goal, enabling the selection of frontiers with the least associated cost. Additionally, we train a modified ResNet18 model to identify the cost and valuable properties of each frontier. We evaluated our model’s performance using the large-scale RGB-D indoor dataset Matterport3D on the Habitat simulator, achieving a 90.7% successful completion of the path in the environment, outperforming traditional frontier-based model by 27.9%, active neural SLAM by 14.3%, and learning augmented model by 2.8%. In certain paths, our model outperformed the Habitat simulator’s state-of-the-art baseline normalized path length by 3.6%. Also, we reckon the efficacy of the model on the real robot, and it proved to be effective. This model supports 3D Lidar, 2D Lidar, and RGB-D perception, enabling exploration in complex environments before pinpointing the goal frontier, while synthetic maps reduce sampling biases in new environments.