Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies

Abstract

In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in SE(3). The robots do not know the mass or friction properties of the object, or where they are attached to the object. They can, however, access a common state measurement, either from one robot broadcasting its measurements to the team, or by all robots communicating and averaging their state measurements to estimate the state of their centroid. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, as well as hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodeled effects, such as discretization errors and complex frictional interactions.

A Novel Four-DOF Lagrangian Approach to Attitude Tracking for Rigid Spacecraft

“…This allows to conclude only local asymptotic stability of the closed loop due to restrictions to a subspace of the configuration in the controller design. In a recent work [53] a 9-DOF Lagrangian dynamics in SO(3) was used to address attitude manipulation in collaborative robots.…”

Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots

“…They are applied to manipulation tasks for robotic arms [9], [10], aerial robots [11], [12] and mobile robots [13], [14]. Rigid interactions can be found commonly in robotic arm manipulation [9], [10], [15] or through rigid linkages [14], [16], [17], which makes maneuverability in narrow spaces challenging. Soft interactions on the other hand introduce agility for manipulation tasks with a soft gripper [18] or deformable objects [19], but the system dynamics are complex.…”

ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation

“…We propose to convexify the centralized action-value function in MARL and efficiently solve the inner minimization problem in Equation (3). Specifically, we assume that Q µ i is parameterized by fully connected networks with L layers with an input x 0 = (o, a i , a −i ).…”

“…Compared to a single robot approach, a multirobot system provides several unique benefits, including 1) improved efficiency since a sophisticated problem can be decomposed into simpler sub-problems, distributed across robots, and then solved simultaneously and 2) improved mission success rate because a single robot failure can be addressed by another teammate [1]. These advantages have resulted in emerging multirobot applications, such as formation control [2], cooperative manipulation [3], and human-swarm interaction [4].…”