This work proposes a novel 2-D leader-follower multi-agent formation control scheme with (almost) global convergence to the desired shape, which is implementable in agents’ arbitrarily oriented local coordinate frames using only low-cost onboard vision sensors. The agents’ interaction (sensing) graph is assumed to be triangulated and directed. Prescribed performance control (PPC) is employed to devise a decentralized control law. The proposed formation control scheme can handle formation maneuvering, scaling, and orientation specifications using only two leader agents. In this respect, the proposed formation control scheme paves the way for formation maneuvering under STL specifications only available to the leader agents.

This work proposes a funnel control method under time-varying hard and soft output constraints. First, an online funnel planning scheme is designed that generates a constraint consistent funnel, which always respects hard (safety) constraints, and soft (performance) constraints are met only when they are not conflicting with the hard constraints. Next, the prescribed performance control method is employed for designing a robust low-complexity funnel-based controller for uncertain nonlinear Euler-Lagrangian systems such that the outputs always remain within the planned constraint consistent funnels. Finally, the results are verified with a simulation example of a mobile robot tracking a moving object while staying in a box-constrained safe space. This work provides a preliminary result for handling conflicting spatiotemporal constraints (in terms of funnels) for a single-agent system. Extensions to the multi-agent scenario will be investigated in the future. The video provides the simulation example under different tunnings for the proposed online funnel planning scheme.

We address the problem of cooperative manipulation of an object whose tasks are specified by a Signal Temporal Logic (STL) formula. We employ the Prescribed Performance Control (PPC) methodology to guarantee predefined transient and steady-state performance on the object trajectory in order to satisfy the STL formula. We also experimentally verify the results on two manipulator arms, cooperatively working to manipulate an object based on a STL formula.

This work addresses proposes a control pipeline that enables multiple mobile manipulators collaboratively transport an object that is potentially higher than a single robot's payload limit. The robot arm controllers are designed to replicate a spring damper position maintenance for the object while the mobile bases use a model predictive controller (MPC) to maintain a formation around the object. Small displacements of the object, either as an input by the arms or by a human operator, is then used to guide the formation to the target destination.

This work investigates the planning and control for multi-robot systems under linear temporal logic (LTL) specifications. In contrast to most of existing work, which presumes a static and known environment, our study focuses on dynamic environments that can have unknown moving obstacles like humans walking through. Depending on whether local communication is allowed between robots, we consider two different online re-planning approaches. When local communication is allowed, we propose a local trajectory generation algorithm for each robot to resolve conflicts that are detected on-line. In the other case, i.e., no communication is allowed, we develop a model predictive controller to reactively avoid potential collisions. In both cases, task satisfaction is guaranteed whenever it is feasible. In addition, we consider the human-in-the-loop scenario where humans may additionally take control of one or multiple robots. We design a mixed initiative controller for each robot to prevent unsafe human behaviors while guarantee the LTL satisfaction. Using our previous developed ROS software package, several experiments are conducted to demonstrate the effectiveness and the applicability of the proposed strategies.

The work proposes a decentralized vehicle coordination algorithm that does not rely on switching between controllers. Thereby, vehicle interact with each other by indicating the plans through their manoeuvers implicitly. The algorithm is a low level-controller that allows for forming, merging, maintaining and splitting platoons and supports plug-and-play.

This work investigates the usage of the prescribed performance control (PPC) in leade- follower platooning under STL specifications to reveal the challenges, advantages, and limitations of the PPC in this specific application. All follower agents apply PPC with appointed-time performance bounds to regulate inter-vehicle distance and ensure collision avoidance and connectivity maintenance. The leader agent has direct access to platoon-level STL specifications as well as the user-defined appointed-time of the followers’ performance bounds. The video shows an example of 1-D platoons where the leader agent drives the whole platoon to specific locations (determined by the position of the vehicles on the other lane) at certain times. Such behavior is useful, for example, in merging other vehicles into the platoon (the merging scenario is not considered in the simulation video).

This work addresses the problem of cooperative control of leader-follower multi-agent systems under local signal temporal logic (STL) specifications in a distributed fashion, where the overall system is composed of several leader-follower subsystems with coupled dynamics. In this work, only the leaders know the related STL specifications and are designed to drive the followers in a way such that the STL specifications are globally satisfied. Under the local feasibility assumption, we propose a funnel-based control approach for each leader-follower subsystem such that the local STL specifications are achieved, which further implies the global satisfaction of all STL specifications. In order to enforce the satisfaction of the STL formulas, the funnel parameters are appropriately designed to prescribe certain transient behavior that constrains the closed-loop trajectories.