Quick Overview

What is a Probabilistic Map?

Our vision system creates a real-time map of the playing field, including the locations of notes and robots, but with an added layer of probability. This means that instead of just providing a single location for each object, the map assigns a probability score to each location. This score indicates how likely it is that an object is there.

Why is it Important?

• Robustness: Probabilistic maps help handle noisy and uncertain sensor data, making the system more resilient to errors.

• Informed Decision Making: By considering the probability of object locations, the robot can make more informed decisions about its actions, such as path planning and obstacle avoidance.

• Adaptive Behavior: The probabilistic map allows the robot to adapt to changing conditions, such as unexpected movements of other robots or sudden changes in the environment.

• Strategic Planning: By understanding the likely positions of other robots, XBOT can plan its moves strategically to gain an advantage.

How Does It Work?

Our probabilistic mapping system works by continuously updating a map of the field, assigning probabilities to different locations for the presence of objects like notes and robots.

1. Sensor Data Acquisition:

   • Our XBOT's sensors, such as cameras and LiDAR, continuously collect data about the surrounding environment.

2. Object Detection and Localization:

   • Advanced algorithms process the sensor data to detect and localize objects in the field.

3. Probability Assignment:

   • Each detected object is assigned a probability based on the confidence of the detection.

   • The probability is initially high for a newly detected object but gradually decreases over time unless it's continuously detected.

4. Map Update:

   • The probabilistic map is updated with the new object detections and probabilities.

   • The probabilities of existing objects are adjusted based on their recent detections.

5. Decision Making:

   • The robot's decision-making system uses the probabilistic map to plan its actions. For example, it might prioritize approaching objects with high probability and avoid areas with high uncertainty.