Dual arm robots improve picking speed and safety with smart task sharing
Competitive Task Allocation with Temporal Proposal Windows and Improved Artificial Potential Fields for Dual-Arm Industrial Manipulation
Robotics
Summary
Industrial robots often struggle to work together efficiently when picking and placing objects, sometimes bumping into each other or wasting time. This paper describes a system where two robot arms use cameras to look for objects and decide in real time which arm should pick each one based on distance and timing. The system uses a mathematical way to guide the arms so they avoid collisions, move smoothly, and stay efficient. Tests showed the robots could work faster and safer than older methods, making the approach practical for factory tasks like assembly and sorting.
What this means in practice
- •For industrial automation engineers: Enable dual-arm robots to share picking tasks dynamically for faster and safer assembly line operations with varying object layouts.
- •For warehouse robotics teams: Improve coordination of two robot arms in sorting tasks to reduce collisions and optimize workspace coverage under changing conditions.
Authors
Surya Prakash S. K, Bhuvan Narula, Jagannath Prasad Sahoo, Jayakumar VK, Praful Hambarde, Amit Shukla
Abstract
Modern industrial automation demands autonomous collaborative systems capable of coordinated manipulation in workspaces with heterogeneous object arrangements. Existing systems rely on predetermined trajectories and rigid allocation strategies, causing suboptimal workspace utilization and collision risks. This paper presents a distance-based competitive task allocation framework for dual-arm systems, integrating real-time vision perception with collision-free motion planning. A temporal window mechanism enables each arm to evaluate object detections over five consecutive vision frames before committing to optimal distance-based selection, featuring dynamic reallocation and spatial conflict resolution at 0.01m threshold accuracy. An improved Artificial Potential Field (iAPF) framework provides coordinated dual-arm movements through exponential goal attraction, inter-arm repulsion, home-seeking, and velocity damping forces. Simulation results validate superiority over market-based, round-robin, and fixed allocation strategies in distance efficiency, collision risk, and workspace coverage. Hardware experiments using two 6-DOF Kinova Gen3 Lite manipulators demonstrate approximately 47% average velocity improvement over fixed allocation in homogeneous scenarios with zero collisions, while mixed object scenarios confirm universal handling with approximately 11% time overhead offset by superior safety margins. The framework integrates YOLOv8 OBB detection at 30 FPS with 10 Hz control loops, demonstrating practical deployment capability for industrial assembly and sorting operations.