Distributed Contact-Implicit Trajectory Optimization for Collaborative Manipulation

Shorinwa, Ola; Schwager, Mac

doi:10.1109/mrs50823.2021.9620665

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…However, a separable form of the problem can always be obtained by introducing local copies of the optimization variables that are coupled in (14). The functions g and h can also encode complementarity constraints for manipulation and locomotion problems that involve making and breaking rigid body contact [22]. In the extreme case, where the optimization variables are coupled in the objective function and equality and inequality constraints in ( 14), a suitable reformulation takes the form…”

Section: Collaborative Planning Control and Manipulationmentioning

confidence: 99%

Distributed Optimization Methods for Multi-Robot Systems: Part I - A Tutorial

Shorinwa¹,

Halsted²,

Yu³

et al. 2023

Preprint

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Distributed optimization provides a framework for deriving distributed algorithms for a variety of multi-robot problems. This tutorial constitutes the first part of a twopart series on distributed optimization applied to multi-robot problems, which seeks to advance the application of distributed optimization in robotics. In this tutorial, we demonstrate that many canonical multi-robot problems can be cast within the distributed optimization framework, such as multi-robot simultaneous localization and planning (SLAM), multi-robot target tracking, and multi-robot task assignment problems. We identify three broad categories of distributed optimization algorithms: distributed first-order methods, distributed sequential convex programming, and the alternating direction method of multipliers (ADMM). We describe the basic structure of each category and provide representative algorithms within each category. We then work through a simulation case study of multiple drones collaboratively tracking a ground vehicle. We compare solutions to this problem using a number of different distributed optimization algorithms. In addition, we implement a distributed optimization algorithm in hardware on a network of Rasberry Pis communicating with XBee modules to illustrate robustness to the challenges of real-world communication networks.

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Section: Collaborative Planning Control and Manipulationmentioning

confidence: 99%

Distributed Optimization Methods for Multi-Robot Systems: Part I - A Tutorial

Shorinwa¹,

Halsted²,

Yu³

et al. 2023

Preprint

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“…Shirai et al [46] proposed a distributed optimization framework based on ADMM to solve contact dynamics. Shorinwa et al [47] used the ADMM method to solve a contact-implicit trajectory optimization problem in a multi-agent system. Wijayarathne et al [48] employed ADMM to generate real-time optimal control in soft contact problems.…”

Section: B Constrained Optimal Controlmentioning

confidence: 99%

Imitation of Manipulation Skills Using Multiple Geometries

Gao

Zhao

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Humans use tools to complete impact-aware tasks such as hammering a nail or playing tennis. The postures adopted to use these tools can significantly influence the performance of these tasks, where the force or velocity of the hand holding a tool plays a crucial role. The underlying motion planning challenge consists of grabbing the tool in preparation for the use of this tool with an optimal body posture. Directional manipulability describes the dexterity of force and velocity in a joint configuration along a specific direction. In order to take directional manipulability and tool affordances into account, we apply an optimal control method combining iterative linear quadratic regulator (iLQR) with the alternating direction method of multipliers (ADMM). Our approach considers the notion of tool affordances to solve motion planning problems, by introducing a cost based on directional velocity manipulability. The proposed approach is applied to impact tasks in simulation and on a real 7-axis robot, specifically in a nail-hammering task with the assistance of a pilot hole. Our comparison study demonstrates the importance of maximizing directional manipulability in impact-aware tasks.

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