Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems (1612.01650v3)

Abstract: Closed kinematic chains are created whenever multiple robot arms concurrently manipulate a single object. The closed-chain constraint, when coupled with robot joint limits, dramatically changes the connectivity of the configuration space. We propose a regrasping move, termed "IK-switch", which allows efficiently bridging components of the configuration space that are otherwise mutually disconnected. This move, combined with several other developments, such as a method to stabilize the manipulated object using the environment, a new tree structure, and a compliant control scheme, enables us to address complex closed-chain manipulation tasks, such as flipping a chair frame, which is otherwise impossible to realize using existing multi-arm planning methods.

• The paper introduces a novel planning framework featuring an 'IK-switch' move, enhanced tree data structures, and compliant control to address the challenges of closed-chain manipulation by multi-arm robots.
• Simulations and experiments demonstrate that the proposed methods significantly reduce planning times, enabling the successful completion of complex manipulation tasks like flipping objects.
• This research provides practical methods for improving industrial multi-arm robotic systems handling large objects by navigating complex configuration spaces and accommodating real-world uncertainties.

Overview of "Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems"

The paper by Zhou, Lertkultanon, and Pham addresses the challenges in motion and path planning for multi-arm robotic systems tasked with manipulating large objects while maintaining closed-chain kinematic constraints. A closed-chain occurs when multiple robotic arms concurrently handle a single object, significantly affecting the connectivity of the configuration space due to joint limits.

A novel concept introduced in this paper is the "IK-switch" move, which involves regrasping that facilitates the bridging of otherwise disconnected components within the configuration space. The paper posits that this maneuver, alongside environmental stabilization methods, tree data structures, and compliant control schemes, enables the execution of complex manipulation tasks deemed infeasible with existing approaches.

Key Contributions

1. IK-Switch Move: This regrasping technique allows robotic arms to remain bound by the same object grasp class while moving between different inverse kinematic (IK) solutions. It's significant for transitioning between disconnected configuration components in closed-chain systems.
2. Environment Utilization for Stabilization: By leveraging the physical environment, the paper proposes methods to stabilize objects during IK-switches without losing contact stability.
3. Enhanced Tree Data Structure: The authors introduce a tree structure adept at supporting regrasping moves, enhancing planning efficiency by retaining space information even when failure points are encountered.
4. Compliant Control Scheme: To deal with real-world uncertainties during execution, a compliance-driven control approach is proposed to ensure smooth motion under model discrepancies.

Findings and Implications

The research provides simulations and experimental results demonstrating the successful completion of complex manipulation tasks, such as flipping a chair frame, using the proposed methods. Planning times are notably reduced (e.g., less than 20 seconds for certain tasks), showing the practical effectiveness of the planning framework.

From the experiments, especially those involving real robotic arms, the compliant control scheme illustrates crucial successes in closed-chain trajectory execution, highlighting the importance of accommodating real-world uncertainties and robotic imprecision.

Theoretical and Practical Implications

Theoretically, the paper contributes to understanding the structural connectivity changes in multi-arm systems due to closed-chain constraints. Practically, it provides a robotic path planner that could improve industrial robotic systems handling large payloads or requiring precise manipulations.

Future Directions

The paper notes certain limitations and suggests avenues for future research, such as incorporating multiple grasping poses to enhance the planner's flexibility and complexity within non-planar workspaces. Future work may also explore complete planning approaches guaranteeing higher levels of completeness and optimality in complex robotic environments.

In summary, the paper provides valuable methods for overcoming significant hurdles in robotic manipulation planning within closed-chain configurations. These contributions are crucial for advancing robotic automation capabilities in various fields including manufacturing, logistics, and autonomous systems.