Multi-robot grasp planning for sequential assembly operations

This paper addresses the problem of finding robot configurations to grasp assembly parts during a sequence of collaborative assembly operations. We formulate the search for such configurations as a constraint satisfaction problem (CSP). Collision constraints in an operation and transfer constraints between operations determine the sets of feasible robot configurations. We show that solving the connected constraint graph with off-the-shelf CSP algorithms can quickly become infeasible even for a few sequential assembly operations. We present an algorithm which, through the assumption of feasible regrasps, divides the CSP into independent smaller problems that can be solved exponentially faster. The algorithm then uses local search techniques to improve this solution by removing a gradually increasing number of regrasps from the plan. The algorithm enables the user to stop the planner anytime and use the current best plan if the cost of removing regrasps from the plan exceeds the cost of executing those regrasps. We present simulation experiments to compare our algorithm's performance to a naive algorithm which directly solves the connected constraint graph. We also present a real robot system which uses the output of our planner to grasp and bring parts together in assembly configurations.

Authors

• Mehmet Remzi Dogar
• Andrew Spielberg
• Stuart Baker
• Daniela Rus

Keywords

• Assembly
• Robots
• Collision avoidance
• Planning
• Grasping
• Complexity theory
• Grippers
• Assembly Operations
• Local Search
• Real Robot
• Assembly Of Parts
• Constraint Satisfaction Problem
• Robot Configuration
• Naive Algorithm
• Local Neighborhood
• Current Solution
• Types Of Constraints
• Subsequent Operations
• Swarm Robotics
• Robots In Environments
• Variety Of Constraints