Particle Filter enhanced Path planningÂ
Understanding the Challenge
Path planning for robots in unfamiliar and changing environments has always been a challenging task. Traditional methods often require excessive computational resources and may not adapt well to dynamic surroundings. My goal was to develop an approach that not only provided efficient solutions but could also adapt in real-time, making it suitable for a wide range of robotic applications
The Particle Filter Approach
Key Highlights of Terrain Mapping:
Sensor Integration: We integrated various sensors such as LiDAR, cameras, and IMUs to gather data from the environment. These sensors provided valuable information for our particle filter-based mapping system.
Localization: Particle filters allowed us to estimate the robot's pose accurately, even in challenging conditions like uneven terrain or limited visibility.
Map Fusion: We fused data from multiple sensors to create a comprehensive and precise representation of the terrain's topography.
Enhanced Path Planning with the RRT Algorithm
With a reliable terrain map in hand, we moved on to the path-planning phase. Here, we adopted the Rapidly-exploring Random Trees (RRT) algorithm to find optimal paths through complex terrain. RRT is known for its efficiency in exploring large state spaces and quickly finding feasible paths.
Key Features of the RRT Algorithm Integration:
Dynamic Environment Handling: RRT excels in adapting to dynamic environments by continuously exploring and updating paths.
Collision Avoidance: We fine-tuned our RRT implementation to ensure that the generated paths were collision-free, prioritizing safety in navigation.
Real-time Performance: Our system was designed to deliver real-time path planning, making it suitable for time-critical applications.