In this work, we present a manipulation framework to reliably complete pick and place tasks using mobile bases with robotic arms aiming to enable a multi-robot system to build complex structures using different construction blocks. For picking, an eye-in-hand architecture is used and a visual servoing controller is implemented to ensure successful object tracking. The visual servoing controller is filtered by Control Barrier Functions which ensure fiducial markers in the manipulated object remain visible during approach. For placing, an extra robot with an eye-to-hand architecture is used to ensure the blocks are placed precisely, which is crucial for structural stability. Provisions are also made to allow a human in the control loop to add flexibility and fault correction capabilities to the system. An analysis of the robustness of the system with respect to errors in the pose of the camera is performed, and changes to the barrier functions are proposed to deal with them. Lastly, experiments to validate the developed framework were performed in 6-DoF mobile manipulators.

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through field experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.

In this paper, we introduce a ROS software package designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real-time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

This paper presents a systematic method for synthesizing a Control Barrier Function (CBF) that encodes predictive information into a CBF. Unlike other methods, the synthesized CBF can account for changes and time-variations in the constraints even when constructed for time-invariant constraints. This avoids recomputating the CBF when the constraint specifications change. The method provides an explicit characterization of the extended class K function alpha that determines the dynamic properties of the CBF, and alpha can even be explicitly chosen as a design parameter in the controller synthesis. The resulting CBF further accounts for input constraints, and its values can be determined at any point without having to compute the CBF over the entire domain. The synthesis method is based on a finite horizon optimal control problem inspired by Hamilton-Jacobi reachability analysis and does not rely on a nominal control law. The synthesized CBF is time-invariant if the constraints are. The method poses mild assumptions on the controllability of the dynamic system and assumes the knowledge of at least a subset of some control invariant set. The paper provides a detailed analysis of the properties of the synthesized CBF, including its application to time-varying constraints. A simulation study applies the proposed approach to various dynamic systems in the presence of time-varying constraints. The paper is accompanied by an online available parallelized implementation of the proposed synthesis method.

In this work, a modular simulation framework for vehicle platooning has been developed. Designed with an extensible, object-oriented architecture and leveraging established design patterns, the framework enables efficient evaluation and comparison of various vehicle control algorithms and dynamic models. Its modular structure allows seamless plug-and-play integration of different vehicle dynamics, lateral and longitudinal controllers, and road segment types. The accompanying video demonstrates a representative traffic scenario, which can be constructed within the simulator in a modular fashion.

In this work, we present a coordination strategy tailored for scenarios involving multiple agents and tasks. We devise a range of tasks using signal temporal logic (STL), each earmarked for specific agents. These tasks are then imposed through control barrier function (CBF) constraints to ensure completion. To extend existing methodologies, our framework adeptly manages interactions among multiple agents. This extension is facilitated by leveraging nonlinear model predictive control (NMPC) to compute trajectories that avoid collisions. An integral aspect of our approach is the integration of a human-in-the-loop (HIL) model. This model enables real-time integration of human directives into the coordination process. A novel task allocation protocol is embedded within the framework to guide this process. We substantiate our methodology through a series of experiments, which corroborate the viability and relevance of our algorithms.

In this work, we develop a control algorithm for mobile manipulators manipulating an object within a leader- follower framework. Unlike existing literature, we avoid the knowledge of the object’s dynamics, and only the leader is aware of the tasks to be executed by the object. The followers are primarily tasked to lift the object and maintain a desired posture while the leader manipulates the object despite its unknown dynamic parameters. We employ a stiffness-based controller for the followers, allowing set-point stabilisation with permissible flexibility and a high-gain prescribed performance controller for the leader to facilitate manipulation from the object’s equilibrium state. We present simulation results with two followers and one leader KUKA youbots to validate our proposed framework.

This article presents MAPS2: a distributed algorithm that allows multi-robot systems to deliver coupled tasks expressed as Signal Temporal Logic (STL) constraints. Classical control theoretical tools addressing STL constraints either adopt a limited fragment of the STL formula or require approximations of min/max operators. Meanwhile, works maximising robustness through optimisation-based methods often suffer from local minima, thus relaxing any completeness arguments due to the NP-hard nature of the problem. Endowed with probabilistic guarantees, MAPS2 provides an autonomous algorithm that iteratively improves the robots’ trajectories. The algorithm selectively imposes spatial constraints by taking advantage of the temporal properties of the STL. The algorithm is distributed in the sense that each robot calculates its trajectory by communicating only with its immediate neighbours as defined via a communication graph. We illustrate the efficiency of MAPS2 by conducting extensive simulation and experimental studies, verifying the generation of STL satisfying trajectories.

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.