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We develop a learning-based algorithm for the distributed formation control of networked multi-agent systems governed by unknown, nonlinear dynamics. Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees. The proposed algorithm avoids these drawbacks by integrating neural network-based learning with adaptive control in a two-step procedure. In the first step of the algorithm, each agent learns a controller, represented as a neural network, using training data that correspond to a collection of formation tasks and agent parameters. These parameters and tasks are derived by varying the nominal agent parameters and a user-defined formation task to be achieved, respectively. In the second step of the algorithm, each agent incorporates the trained neural network into an online and adaptive control policy in such a way that the behavior of the multi-agent closed-loop system satisfies the user-defined formation task. Both the learning phase and the adaptive control policy are distributed, in the sense that each agent computes its own actions using only local information from its neighboring agents. The proposed algorithm does not use any a priori information on the agents' unknown dynamic terms or any approximation schemes. We provide formal theoretical guarantees on the achievement of the formation task.

Distributed control on a multi-agent format in co-simulation environment has been conceived in a client/server architecture for controlling a series of devices connected to a Direct Current (DC) bus. The implemented system aims for providing the communication infrastructure required for connecting the whole co-simulation environment. Power converters interact via a communication infrastructure orchestrated by a multi-agent system whose algorithm has been built for the proposed scenario. A virtual small village is supplied by a DC power system endowed by some photovoltaic arrays and energy storage by a battery bank. The use of Python, socket Transmission Control Protocol/Internet Protocol (TCP/IP) and Power Simulator (PSIM) with appropriate adaptation is meant to build the system in a lighter computational environment. The interaction among agents helped the co-simulation with a distributed control to maintain the DC bus stable in 180 Vdc and battery voltage oscillating within the state of charge (SoC) range, 99% and 97%, of 144 Vdc fed by a photovoltaic array under the coordination of the multi-agent system.

We study the problem of reinforcement learning for a task encoded by a reward machine. The task is defined over a set of properties in the environment, called atomic propositions, and represented by Boolean variables. One unrealistic assumption commonly used in the literature is that the truth values of these propositions are accurately known. In real situations, however, these truth values are uncertain since they come from sensors that suffer from imperfections. At the same time, reward machines can be difficult to model explicitly, especially when they encode complicated tasks. We develop a reinforcement -learning algorithm that infers a reward machine that encodes the underlying task while learning how to execute it, despite the uncertainties of the propositions' truth values. In order to address such uncertainties, the algorithm maintains a probabilistic estimate about the truth value of the atomic propositions; it updates this estimate according to new sensory measurements that arrive from exploration of the environment. Additionally, the algorithm maintains a hypothesis reward machine, which acts as an estimate of the reward machine that encodes the task to be learned. As the agent explores the environment, the algorithm updates the hypothesis reward machine according to the obtained rewards and the estimate of the atomic propositions' truth value. Finally, the algorithm uses a Q -learning procedure for the states of the hypothesis reward machine to determine an optimal policy that accomplishes the task. We prove that the algorithm successfully infers the reward machine and asymptotically learns a policy that accomplishes the respective task.

We develop an algorithm to control an underactuated unmanned surface vehicle (USV) using kinodynamic motion planning with funnel control (KDF). KDF has two key components: motion planning used to generate trajectories with respect to kinodynamic constraints, and funnel control, also referred to as prescribed performance control (PPC), which enables trajectory tracking in the presence of uncertain dynamics and disturbances. We extend PPC to address the challenges posed by underactuation and control input saturation present on the USV. The proposed scheme guarantees stability under user-defined prescribed performance functions where model parameters and exogenous disturbances are unknown. Furthermore, we present an optimization problem to obtain smooth, collision-free trajectories while respecting kinodynamic constraints. We deploy the algorithm on a USV and verify its efficiency in real-world open-water experiments.

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 study focuses on addressing the problem of motion planning within workspaces cluttered with obstacles while considering temporal and input constraints. These specifications can encapsulate intricate high-level objectives involving both temporal and spatial constraints. The existing literature lacks the ability to fulfill time specifications while simultaneously managing input-saturation constraints. The proposed approach introduces a hybrid three-component control algorithm designed to learn the safe execution of a high-level specification expressed as a timed temporal logic formula across predefined regions of interest in the workspace. The first component encompasses a motion controller enabling secure navigation within the minimum allowable time interval dictated by input constraints, facilitating the abstraction of the robot's motion as a timed transition system between regions of interest. The second component utilizes formal verification and convex optimization techniques to derive an optimal high-level timed plan over the mentioned transition system, ensuring adherence to the agent's specification. However, the necessary navigation times and associated costs among regions are initially unknown. Consequently, the algorithm's third component iteratively adjusts the transition system and computes new plans as the agent navigates, acquiring updated information about required time intervals and associated navigation costs. The effectiveness of the proposed scheme is demonstrated through both simulation and experimental studies.

A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and cause collisions, especially in obstacle-rich environments. This letter presents a disturbance-aware motion planning and control framework for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The motion planner consists of a predictive control scheme and an online-adapted learned disturbance model. The tracking controller, developed using contraction control methods, ensures safety bounds on the quadrotor's behavior near obstacles with respect to the motion plan. The algorithm is tested in simulations with a quadrotor facing strong crosswind and ground-induced disturbances.

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework.

Task and motion planning (TAMP) can enhance intelligent multi-robot coordination. TAMP becomes significantly more complicated in obstacle-cluttered environments and in the presence of robot dynamic uncertainties. We propose a control framework that solves the motion-planning problem for multi-robot teams with uncertain dynamics, addressing a key component of the TAMP pipeline. The principal part of the proposed algorithm constitutes a decentralized feedback control policy for tracking of reference paths taken by the robots while avoiding collision and adapting in real time to the underlying dynamic uncertainties. The proposed framework further leverages sampling-based motion planners to free the robots from local-minimum configurations. Extensive experimental results in complex, realistic environments illustrate the superior efficiency of the proposed approach, in terms of planning time and number of encountered local minima, with respect to state-of-the-art baseline methods.

We present a distributed, event-triggered, and adaptive control algorithm for cooperative object manipulation with rolling contacts and unknown dynamic parameters. Whereas conventional cooperative manipulation methods require rigid contact points, our approach exploits rolling effects of passive end-effectors and does not require force/torque sensing. The removal of rigidity allows for more modular grasping, increased application to more object types, and online adjustment of the grasp. The proposed control algorithm exhibits the following properties: 1) it is distributed, in the sense that the robotic agents calculate their own control signal, under an event-triggered communication scheme. Such a scheme reduces the interagent communication requirements with respect to continuous communication schemes; 2) it uses an online adaptation mechanism to accommodate for unknown dynamic parameters of the object and the agents and 3) it adapts existing internal force controllers to guarantee no slip throughout the manipulation task despite the event-triggered nature of the communication scheme. Hardware implementation validates the effectiveness of the proposed approach.