In this article, we consider the periodic event-triggered consensus problem for single-integrator multiagent systems. In existing approaches, consensus is typically achieved by trigger schemes enforcing a monotone decrease of a Lyapunov function. Such trigger schemes may, however, result in more transmissions than are actually required to ensure consensus or meet certain performance specifications. This is because a monotone decrease may be a rather conservative condition for a given Lyapunov function in an event-triggered setting. To overcome the conservativity, we propose a novel window-based trigger scheme, which allows us to leverage existing trigger schemes from the literature to derive less conservative ones. This is achieved by taking the past system behavior into account and allowing a temporary increase of the Lyapunov function as long as a decreasing tendency is still guaranteed. For that, information from previous time steps within certain time windows is considered. We provide an explicit bound on the evolution of the Lyapunov function that is the same for the window-based scheme and the (monotone) original trigger schemes. To illustrate the benefits of the window-based scheme, we validate its efficacy through a comprehensive simulation study and demonstrate that the scheme typically reduces the average update rate of the underlying communication structure in comparison to the original trigger schemes.

This paper investigates the planning and control problems for multi-robot systems under linear temporal logic (LTL) specifications. In contrast to most of existing literature, 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 conductedto demonstrate the effectiveness and the applicability of the proposed strategies.

The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human-robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human-robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work.

This article investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace, each of which is assigned a linear temporal logic specification. Based on the realistic assumptions that each robot is subject to both state and input constraints and can have only local view and local information, a fully distributed multirobot motion coordination strategy is proposed. For each robot, the motion coordination strategy consists of three layers. An offline layer precomputes the braking area for each region in the workspace, the controlled transition system, and a so-called potential function. An initialization layer outputs an initially safely satisfying trajectory. An online coordination layer resolves conflicts when one occurs. The online coordination layer is further decomposed into three steps. First, a conflict detection algorithm is implemented, which detects conflicts with neighboring robots. Whenever conflicts are detected, a rule is designed to assign dynamically a planning order to each pair of neighboring robots. Finally, a sampling-based algorithm is designed to generate local collision-free trajectories for the robot, which at the same time guarantees the feasibility of the specification. Safety is proven to be guaranteed for all robots at any time. The effectiveness and the computational tractability of the resulting solution is verified numerically by two case studies.

Discrete abstractions have become a standard approach to assist control synthesis under complex specifications. Most techniques for the construction of a discrete abstraction for a continuous-time system require time-space discretization of the concrete system, which constitutes property satisfaction for the continuous-time system non-trivial. In this work, we aim at relaxing this requirement by introducing a control interface. Firstly, we connect the continuous-time uncertain concrete system with its discrete deterministic state-space abstraction with a control interface. Then, a novel stability notion called eta-approximately controlled globally practically stable, and a new simulation relation called robust approximate simulation relation are proposed. It is shown that the uncertain concrete system, under the condition that there exists an admissible control interface such that the augmented system (composed of the concrete system and its abstraction) can be made eta-approximately controlled globally practically stable, robustly approximately simulates its discrete abstraction. The effectiveness of the proposed results is illustrated by two simulation examples.

This paper addresses the problem of time-constrained leader-follower multi-agent task scheduling and control synthesis. The leader-follower multi-agent system is subject to a set of dynamically activated tasks, each of which is associated with a relative deadline and can be completed at different Quality-of-Satisfaction levels. By taking into account the reward and cost of satisfying these tasks, a novel scheduling problem is formulated and a dynamic scheduling strategy is proposed. Based on the dynamic plan, distributed control laws are designed accordingly for the leader and follower agents. Under the condition that the information of the target regions are available only to the leader agents, it is shown that the proposed control laws guarantee the satisfaction of each task at its desired Quality-of-Satisfaction level. A simulation example is given to verify the theoretical results.

In this paper, we propose a ROS software package for planning and control of robotic systems with a human-in-the-Ioop focus. The software uses temporal logic specifications, specifically Linear Temporal Logic, for a language-based method to develop correct-by-design high level robot plans. The approach is structured to allow a human to adjust the high-level plan online. A human may also take control of the robot (in a low-level control fashion), but the software prevents the human from implementing dangerous behaviour that would violate the high-level task specification. Finally, the planner is able to learn human-preferred high-level tasks by tracking human low-level control inputs in an inverse learning framework. The proposed approach is demonstrated in a warehouse setting with multiple robot agents to showcase the efficacy of the proposed solution.

This paper studies the hierarchical control of uncertain discrete-time nonlinear systems under input constraints. First, the notion of robust approximate simulation relation is defined. We show that by properly designing a control interface, the robust approximate simulation relation can be constructed from a low-complexity, deterministic (abstract) system to the original system. Then, we apply the hierarchical control approach to the robust control synthesis under signal temporal logic specifications. The results show that this approach reduces the computational complexity of the control synthesis, and is in some cases applicable to a larger set of initial states. The effectiveness of the proposed approach is verified by a simulation example.

Multi-agent systems (MAS) offer a tremendous potential to improve the quality of modern society life. For instance, robot networks have been widely used for providing services such as search and rescue missions, surveillance and data collection, healthcare and entertainment.  One challenge for MAS is the design of control and coordination strategies that enable each agent to perform operations safely and efficiently while achieving group or individual motion objectives. This dissertation addresses this challenge by developing different task-oriented control and coordination strategies for MAS.

The first part of the thesis is devoted to the control of MAS under cooperative tasks. Firstly, we investigate the distributed control of multi-agent consensus. Event-triggered control strategies are developed to reduce communication burden. It is proven that consensus can be achieved with the proposed strategies. Next, we tackle the problem of leader-follower multi-agent target tracking, where the group of agents is assigned a set of dynamically activated tasks, each of which has to be completed within a deadline. A dynamic scheduling strategy is proposed and distributed control laws are designed respectively for the leader and follower agents. It is shown that the proposed control laws guarantee the satisfaction of each task.

In the second part of the thesis, we develop control and coordination schemes for single- or multi-agent systems under temporal logic specifications. Firstly, the symbolic control of continuous-time uncertain nonlinear systems is studied. A new stability notion called approximate controlled globally practically stable is introduced. Building on this notion, we provide for the first time a behavioral relationship between the original continuous-time system and its discrete state-space symbolic model. After that, we consider the robust satisfiability check and online control synthesis of uncertain systems under signal temporal logic specifications. A sufficient condition is obtained for the robust satisfiability check of the uncertain systems. An online control synthesis algorithm is designed, which is shown to be sound for uncertain systems and both sound and complete for deterministic systems. Finally, the motion coordination of MAS is investigated, where each agent is assigned a linear temporal logic specification. Based on the realistic assumptions that each agent is subject to both state and input constraints and can have only local view and local information, a provably safe and fully distributed multi-agent motion coordination strategy is proposed.

This paper investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace. Based on the realistic assumptions that each robot is subject to both velocity and input constraints and can have only local view and local information, a fully distributed multi-robot motion coordination strategy is proposed. Building on top of a cell decomposition, a conflict detection algorithm is presented first. Then, a rule is proposed to assign dynamically a planning order to each pair of neighboring robots, which is deadlock-free. Finally, a two-step motion planning process that combines fixed-path planning and trajectory planning is designed. The effectiveness of the resulting solution is verified by a simulation example.