This paper presents a novel control method using Haptic Shared Autonomy (HSA) for two-wheeled drones that can drive on surfaces and fly in the air. These drones can switch between flight and contact-based locomotion, enabling versatile operation in complex environments, but tend to become unstable when transitioning from flight to wall-running mode due to wall-collision impacts. To address this, we formulate a human-in-the-loop framework in which the operator inputs commands and receives haptic feedback via a haptic device. We design Control Barrier Functions (CBFs) and impose an energy-based ℒ2gain constraint to guarantee soft landing. By solving a Sequential Control Force (SCF) problem, we compute the drone’s control inputs and haptic feedback forces, automatically adjusting the approach speed. Simulations demonstrate that the proposed method achieves effective soft wall landings while preserving the human operator’s intent.

In this paper, we present a unified framework to ensure the stability, safety, and passivity of a multi-robot teleoperation system in a holistic fashion. The proposed approach consists of encoding these three properties as constraints in an optimization-based controller using control Lypaunov and (integral) control barrier functions. The result is a stability-safety-passivity (SSP) filter implemented as a convex optimization control policy, which can be efficiently evaluated in an online fashion. The developed filter minimally modifies the teleoperation input in order to ensure that the robotic system remains stable, safe, and passive. The effectiveness of the developed approach is showcased using a team of mobile robots in a human-multi-robot teleoperation scenario.

Systematically designing local interaction rules to achieve collective behaviors in robot swarms is a challenging endeavor, especially in micro robots, where size restrictions imply severe sensing, communication, and computation limitations. In such robot swarms, performing useful functions is often preconditioned on the formation of high-density aggregations which can facilitate collective signaling and information sharing. In this article, we present a systematic approach to control aggregation behaviors by leveraging the physical interactions in a swarm of 300 3-mm vibration-driven micro bristle robots that we designed and fabricated. We demonstrate the ability to control the degree of aggregation by varying the motility characteristics of the robots through global vibration frequency and amplitude inputs, after comprehensive characterization, modeling, and simulation of the locomotion dynamics and robot interactions. To quantify the degree of aggregation, we also introduce a new metric, the motility-induced phase separation index index, which unlike many existing methods does not require a scenario-specific tuning of parameters. Our investigations reveal how physics-driven interaction mechanisms can be exploited to achieve desired behaviors in minimally equipped robot swarms and highlight the specific ways in which hardware and software developments aid in the achievement of collision-induced aggregations.

This paper considers a multi-robot team tasked with monitoring an environmental field of interest over long time horizons. The approach is based on a control-theoretic measure of the information collected by the robots, namely a norm of the constructability Gramian. This measure is leveraged in order to learn a distributed multi-robot control policy using the reinforcement learning paradigm. The learned policy is then combined with energy constraints using the constraint-driven control framework in order to achieve persistent environmental monitoring. The proposed approach is tested in a simulated multi-robot persistent environmental monitoring scenario where a team of robots with limited availability of energy is to be controlled in a coordinated fashion in order to estimate the concentration of a gas diffusing in the environment.

Applications that require multirobot systems to operate independently for extended periods of time in unknown or unstructured environments face a broad set of challenges, such as hardware degradation, changing weather patterns, or unfamiliar terrain. To operate effectively under these changing conditions, algorithms developed for long-term autonomy applications require a stronger focus on robustness. Consequently, this work considers the ability to satisfy the operation-critical constraints of a disturbed system in a modular fashion, which means compatibility with different system objectives and disturbance representations. Toward this end, this article introduces a controller-synthesis approach to constraint satisfaction for disturbed control-affine dynamical systems by utilizing control barrier functions (CBFs). The aforementioned framework is constructed by modeling the disturbance as a union of convex hulls and leveraging previous work on CBFs for differential inclusions. This method of disturbance modeling grants compatibility with different disturbance-estimation methods. For example, this work demonstrates how a disturbance learned via a Gaussian process may be utilized in the proposed framework. These estimated disturbances are incorporated into the proposed controller-synthesis framework which is then tested on a fleet of robots in different scenarios.

In the context of heterogeneous multirobot teams deployed for executing multiple tasks, this article develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Toward this end, the task prioritization and execution—through which the allocation of tasks to robots is effectively realized—are encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations, where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.

Multi-robot task allocation is a ubiquitous problem in robotics due to its applicability in a variety of scenarios. Adaptive task-allocation algorithms account for unknown disturbances and unpredicted phenomena in the environment where robots are deployed to execute tasks. However, this adaptivity typically comes at the cost of requiring precise knowledge of robot models in order to evaluate the allocation effectiveness and to adjust the task assignment online. As such, environmental disturbances can significantly degrade the accuracy of the models which in turn negatively affects the quality of the task allocation. In this paper, we leverage Gaussian processes, differential inclusions, and robust control barrier functions to learn environmental disturbances in order to guarantee robust task execution. We show the implementation and the effectiveness of the proposed framework on a real multi-robot system.

For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions. This paper presents an adaptive task allocation and task execution framework which allows individual robots to prioritize among tasks while explicitly taking into account their efficacy at performing the tasks---the parameters of which might be unknown before deployment and/or might vary over time. Such a \emph{specialization} parameter---encoding the effectiveness of a given robot towards a task---is updated on-the-fly, allowing our algorithm to reassign tasks among robots with the aim of executing them. The developed framework requires no explicit model of the changing environment or of the unknown robot capabilities---it only takes into account the progress made by the robots at completing the tasks. Simulations and experiments demonstrate the efficacy of the proposed approach during variations in environmental conditions and when robot capabilities are unknown before deployment.

Executing multiple tasks concurrently is important in many robotic applications. Moreover, the prioritization of tasks is essential in applications where safety-critical tasks need to precede application-related objectives, in order to protect both the robot from its surroundings and vice versa. Furthermore, the possibility of switching the priority of tasks during their execution gives the robotic system the flexibility of changing its objectives over time. In this paper, we present an optimization-based task execution and prioritization framework that lends itself to the case of time-varying priorities as well as variable number of tasks. We introduce the concept of extended set-based tasks, encode them using control barrier functions, and execute them by means of a constrained-optimization problem, which can be efficiently solved in an online fashion. Finally, we show the application of the proposed approach to the case of a redundant robotic manipulator.

In this paper, we consider a two-player racing game, where an autonomous ego vehicle has to be controlled to race against an opponent vehicle, which is either autonomous or human-driven. The approach to control the ego vehicle is based on a Sensitivity-ENhanced NAsh equilibrium seeking (SENNA) method, which uses an iterated best response algorithm in order to optimize for a trajectory in a two-car racing game. This method exploits the interactions between the ego and the opponent vehicle that take place through a collision avoidance constraint. This game-theoretic control method hinges on the ego vehicle having an accurate model and correct knowledge of the state of the opponent vehicle. However, when an accurate model for the opponent vehicle is not available, or the estimation of its state is corrupted by noise, the performance of the approach might be compromised. For this reason, we augment the SENNA algorithm by enforcing Permissive RObust SafeTy (PROST) conditions using control barrier functions. The objective is to successfully overtake or to remain in the front of the opponent vehicle, even when the information about the latter is not fully available. The successful synergy between SENNA and PROST-antithetical to the notable rivalry between the two namesake Formula 1 drivers-is demonstrated through extensive simulated experiments.

In this paper we consider a constrained coverage control problem for a team of mobile robots. The robots are asked to provide sensor coverage over a two-dimensional domain, while being constrained to only move on a curve. The unconstrained coverage problem can be effectively solved by defining a locational cost to be minimized by the robots, in a decentralized fashion, using gradient descent. However, a direct projection of the solution to the unconstrained problem onto the curve may result in a very poor spatial allocation of the team within the two-dimensional domain. Therefore, we propose a modification to the locational cost, which incorporates the constraints, and a convex relaxation that allows us to efficiently minimize a convex approximation of the cost using a decentralized strategy. The resulting algorithm is implemented on a team of mobile robots.

In robot swarms operating under highly restrictive sensing and communication constraints, individuals may need to use direct physical proximity to facilitate information exchange. However, in certain task-related scenarios, this requirement might conflict with the need for robots to spread out in the environment, e.g., for distributed sensing or surveillance applications. This paper demonstrates how a swarm of minimally-equipped robots can form high-density robot aggregates that coexist with lower robot densities in space. We envision a scenario where a swarm of vibration-driven robots-which sit atop bristles and achieve directed motion by vibrating them-move randomly in an environment while colliding with each other. Theoretical techniques from the study of far-from-equilibrium collectives and statistical mechanics clarify the mechanisms underlying the formation of these high and low density regions. Specifically, we capitalize on a transformation that connects the collective properties of a system of self-propelled particles with that of a well-studied molecular fluid system, thereby inheriting the rich theory of equilibrium thermodynamics. Real robot experiments as well as simulations illustrate how inter-robot collisions can precipitate the formation of non-uniform robot densities in a closed and bounded region.

In this paper we present a study of a specific class of vibration-driven robots: the brushbots. In a bottom-up fashion, we start by deriving dynamic models of the brushes and we discuss the conditions under which these models can be employed to describe the motion of brushbots. Then, we present two designs of brushbots: a fully-actuated platform and a differential-drive-like one. The former is employed to experimentally validate both the developed theoretical models and the devised motion control algorithms. Finally, a coordinated-control algorithm is implemented on a swarm of differential-drive-like brushbots in order to demonstrate the design simplicity and robustness that can be achieved by employing a vibration-based locomotion strategy.

This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates that constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under stationarity assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex, and nonstationary.

In this paper we consider the coverage control problem for a team of wire-traversing robots. The two-dimensional motion of robots moving in a planar environment has to be projected to one-dimensional manifolds representing the wires. Starting from Lloyd's descent algorithm for coverage control, a solution that generates continuous motion of the robots on the wires is proposed. This is realized by means of a Continuous Onto Wires (COW) map: the robots' workspace is mapped onto the wires on which the motion of the robots is constrained to be. A final projection step is introduced to ensure that the configuration of the robots on the wires is a local minimizer of the constrained locational cost. An algorithm for the continuous constrained coverage control problem is proposed and it is tested both in simulation and on a team of mobile robots.

In this paper we present an interactive system to enhance bilateral teleoperation through online virtual fixtures generation and task switching. This is achieved using a stereo camera system which provides accurate information of the surrounding environment of the robot and of the tasks that have to be performed in it. The use of the proposed approach aims at improving the performances of bilateral teleoperation systems by reducing the human operator workload and increasing both the implementation and the execution efficiency. In fact, using our method virtual guidances do not need to be programmed a priori but they can be instead automatically generated and updated making the system suitable for unstructured environments. We strengthen the proposed method using passivity control in order to safely switch between different tasks while teleoperating under active constraints. A series of experiments emulating real industrial scenarios are used to show that the switch between multiple tasks can be passively and safely achieved and handled by the system.

A classification system for the segmentation of parking maneuvers and its validation using a small-scale autonomous vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle-dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that can be implemented on a microcontroller with limited computational resources, a two-stage design process is applied. In a first step an ensemble classifier, the Random Forest (RF) algorithm, is constructed and based on the RF-kernel a General Radial Basis Function (GRBF) classifier is generated. The GRBF-classifier is integrated into the small-scale autonomous vehicle leading to an excellent performance in both parallel- and cross-parking maneuvers.