Search for: All records

Abstract This paper aims to develop a distributed layered control framework for the navigation, planning, and control of multi-agent quadrupedal robots subject to environments with uncertain obstacles and various disturbances. At the highest layer of the proposed layered control, a reference path for all agents is calculated, considering artificial potential fields (APF) under a priori known obstacles. Second, in the middle layer, we employ a distributed nonlinear model predictive control (NMPC) scheme with a one-step delay communication protocol (OSDCP) subject to reduced-order and linear inverted pendulum (LIP) models of agents to ensure the feasibility of the gaits and collision avoidance, addressing the degree of uncertainty in real-time. Finally, low-level nonlinear whole-body controllers (WBCs) impose the full-order locomotion models of agents to track the optimal and reduced-order trajectories. The proposed controller is validated for effectiveness and robustness on up to four A1 quadrupedal robots in simulations and two robots in the experiments.1 Simulations and experimental validations demonstrate that the proposed approach can effectively address the real-time planning and control problem. In particular, multiple A1 robots are shown to navigate various environments, maintaining collision-free distances while being subject to unknown external disturbances such as pushes and rough terrain. 

This paper aims to develop distributed feedback control algorithms that allow cooperative locomotion of quadrupedal robots which are coupled to each other by holonomic constraints. These constraints can arise from collaborative manipulation of objects during locomotion. In addressing this problem, the complex hybrid dynamical models that describe collaborative legged locomotion are studied. The complex periodic orbits (i.e., gaits) of these sophisticated and high-dimensional hybrid systems are investigated. We consider a set of virtual constraints that stabilizes locomotion of a single agent. The paper then generates modified and local virtual constraints for each agent that allow stable collaborative locomotion. Optimal distributed feedback controllers, based on nonlinear control and quadratic programming, are developed to impose the local virtual constraints. To demonstrate the power of the analytical foundation, an extensive numerical simulation for cooperative locomotion of two quadrupedal robots with robotic manipulators is presented. The numerical complex hybrid model has 64 continuous-time domains, 192 discrete-time transitions, 96 state variables, and 36 control inputs.