This AI Research Presents Neural A*: A Novel Data-Driven Search Method for Path Planning Problems

Path planning identifies a cost-effective and valid path from an initial point to a target point within an environmental map. Search-based planning methods, which include the well-known A* search, are widely employed in addressing path-planning challenges. These techniques have found application in various domains, including autonomous vehicle navigation and robot arm manipulation.

Recent studies have highlighted the significant benefits of data-driven path planning in two specific scenarios. 

• The first scenario involves the more efficient discovery of near-optimal paths in point-to-point shortest-path search problems compared to traditional heuristic planners. 
• The second scenario pertains to enabling path planning using raw image inputs. This task is challenging for classical planners unless there is access to semantic pixel-wise labeling of the environment.

In this research, the authors have redefined the conventional A* search algorithm differently and combined it with a convolutional encoder to create a fully trainable end-to-end neural network planner. This approach, known as Neural A*, addresses path planning problems by transforming a given problem instance into a guidance map and subsequently conducting a differentiable A* search based on that map. 

The above image demonstrates two Scenarios of Path Planning with Neural A*.

1. Point-to-point shortest path search: finding a near-optimal path (red) with fewer node explorations (green) for an input map.
2. Path planning on raw image inputs: accurately predicting a human trajectory (red) on a natural image.

Through the process of learning to align search outcomes with expert-provided ground truth paths, Neural A* can generate paths that accurately and efficiently adhere to the ground truth. 

This figure shows the schematic diagram of Neural A*:

(1) A path-planning problem instance is fed to the encoder to produce a guidance map. 

(2) The differentiable A* module performs a point-to-point shortest path search with the guidance map and outputs a search history and a resulting path. 

(3) A loss between the search history and the ground-truth path is back-propagated to train the encoder. 

Comprehensive experimentation results have shown that Neural A* surpasses state-of-the-art data-driven planners, achieving a favorable balance between search optimality and efficiency.  Furthermore, Neural A* has demonstrated the capability to predict realistic human trajectories by directly applying search-based planning to natural image inputs.

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