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https://github.com/nrjsbudhe/path-planning-using-graph-based-planning-algorithms
• Implemented path planning in binary occupancy grid maps using A* Search Algorithm • Computed a Probabilistic Road Map using random sampling and computed the shortest path between two nodes
https://github.com/nrjsbudhe/path-planning-using-graph-based-planning-algorithms
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• Implemented path planning in binary occupancy grid maps using A* Search Algorithm • Computed a Probabilistic Road Map using random sampling and computed the shortest path between two nodes
- Host: GitHub
- URL: https://github.com/nrjsbudhe/path-planning-using-graph-based-planning-algorithms
- Owner: nrjsbudhe
- Created: 2024-01-31T16:48:46.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-03-19T16:58:39.000Z (10 months ago)
- Last Synced: 2024-03-19T18:07:29.703Z (10 months ago)
- Language: Python
- Size: 2.11 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Route Planning in Occupancy Grid Maps
## Overview
This project explores route planning in robotic environments represented by occupancy grid maps, utilizing two prominent graph-based planning algorithms: **A\* Search** and **Probabilistic Road Maps (PRM)**. The project is structured in three main parts:
### Occupancy Grid Map
1. **Implementation of A\* Search:**
- A general version of the A\* search algorithm is implemented, which is abstracted from the specific graph representation. This allows it to be applied to both occupancy grids (considered as 8-connected graphs) and standard graphs, such as those used in probabilistic roadmaps.
- The algorithm is designed to find the shortest path from a start node to a goal node within a graph.
2. **Route Planning in Occupancy Grids with A\* Search:**
- This section applies the A\* search algorithm to an occupancy grid map, treated as an 8-connected graph. In this representation, each vertex corresponds to a cell in the grid, identified by its row and column coordinates.
- The algorithm navigates through the grid, finding the optimal path from a start cell to a goal cell while avoiding obstacles.
3. **Route Planning with Probabilistic Roadmaps:**
- Voxelized grids, like occupancy maps, are efficient for modeling robot configuration spaces, but they become impractical for high-dimensional spaces due to their exponential memory requirements. In such cases, sampling-based planners, like PRMs, offer a more feasible alternative.
- This part of the project implements a sampling-based planner to construct a probabilistic roadmap (PRM) within the occupancy grid. The PRM is incrementally built by sampling new vertices from the configuration space and connecting them to nearby vertices using a local planner.
- Ultimately A\* Algorithm is implemented on the constructed PRM to find a path from start to goal.
## Algorithms
### A\* Search
- **A\* Search** is a graph traversal and path search algorithm, which finds the shortest path between a start and goal node. It uses a heuristic to guide its search, combining the cost to reach a node and an estimate of the cost from that node to the goal.
### Probabilistic Roadmaps (PRM)
- **PRM** is a sampling-based path planning algorithm that approximates the configuration space by constructing a graph. Vertices in this graph are sampled points in the configuration space, and edges represent feasible paths between these points, determined by a local planner.
## Dependencies
- Python 3.x
- Libraries: NumPy, Matplotlib, heapq, networkx (for graph handling)
## Generated Paths
### A\* Star Search
### PRM Based A\* Implementation
