Task-Specific Sensor Planning for Robotic Assembly Tasks

When performing multi-robot tasks, sensory feedback is crucial in reducing uncertainty for correct execution. Yet the utilization of sensors should be planned as an integral part of the task planning, taken into account several factors such as the tolerance of different inferred properties of the scene and interaction with different agents. In this paper we handle this complex problem in a principled, yet efficient way. We use surrogate predictors based on open-loop simulation to estimate and bound the probability of success for specific tasks. We reason about such task-specific uncertainty approximants and their effectiveness. We show how they can be incorporated into a multi-robot planner, and demonstrate results with a team of robots performing assembly tasks.

Authors

• Guy Rosman
• Changhyun Choi
• Mehmet Remzi Dogar
• John W. Fisher III
• Daniela Rus

Keywords

• Robot sensing systems
• Task analysis
• Uncertainty
• Planning
• Robotic assembly
• Estimation
• Robotic Tasks
• Sensory Feedback
• Swarm Robotics
• Collision
• Task Force
• Morphine
• Uncertainty Estimation
• Sensor Locations
• Manipulation Tasks
• Pose Estimation
• Task Success
• Conditional Entropy
• Simultaneous Localization And Mapping
• Real Robot
• Assembly Of Parts
• Iterative Closest Point
• Constraint Satisfaction Problem
• Object Pose
• Multiple Robots
• Surrogate Function
• Assembly Operations
• Mobile Manipulator
• 3D Sensor
• Sensor Placement
• Set Of Observations
• Control Signal
• Approximate Entropy
• Optimal Plan
• Depth Camera
• Degrees Of Freedom