Motion interference detection in mobile robots

As mobile robots become better equipped to autonomously navigate in human-populated environments, they need to become able to recognize internal and external factors that may interfere with successful motion execution. Even when these robots are equipped with appropriate obstacle avoidance algorithms, collisions and other forms of motion interference might be inevitable: there may be obstacles in the environment that are invisible to the robot's sensors, or there may be people who could interfere with the robot's motion. We present a Hidden Markov Model-based model for detecting such events in mobile robots that do not include special sensors for specific motion interference. We identify the robot observable sensory data and model the states of the robot. Our algorithm is motivated and implemented on an omnidirectional mobile service robot equipped with a depth-camera. Our experiments show that our algorithm can detect over 90% of motion interference events while avoiding false positive detections.

Authors

• Juan Pablo Mendoza
• Manuela Veloso
• Reid G. Simmons

Keywords

• Hidden Markov models
• Mobile robots
• Acceleration
• Interference
• Collision avoidance
• Monitoring
• Mobile Robot
• Motion Interference
• Navigation
• Collision
• Observational Data
• Positive Detection
• Obstacle Avoidance
• False Positive Detection
• Service Robots
• Form Of Interference
• Hidden Markov Model
• Transition Probabilities
• Constant Speed
• Precision And Recall
• Detection Model
• Noise In Data
• Explicit Model
• Precision Rate
• Velocity Difference
• Recall Rate
• Velocity Commands
• Velocity Of The Robot
• Control Run
• Forward Velocity
• Series Of Observations
• Laser Ranging
• Robot Behavior
• Types Of Interference
• True Positive Detection
• Unconstrained Environment