Controlled microrobots in fluidic environments hold promise for precise drug delivery and cell manipulation, opening new ways for personalized healthcare. However, coordinating magnetic microrobot swarms presents significant challenges due to the complexity of the associated actuation mechanisms. While existing methods to achieve motion differentiation in collections of microrobots rely on design variations among them, the work reported here applies to homogeneous collectives and enables them to be steered as a whole or in fragments, by means of a common externally generated force field. This paper contributes to an emerging set of methods that enable swarm control through manipulation of these force fields. This paper in particular exploits the nature of force field equilibria in a quadrupole workspace configuration as a means of steering the swarm while maintaining its cohesion. The approach also enables splitting the swarm in two subgroups in order to direct each simultaneously to a different location.

Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits. As the importance of oysters grows, so does the relevance of autonomous systems for their detection and monitoring. However, current monitoring strategies often rely on destructive methods. While manual identification of oysters from video footage is non-destructive, it is time-consuming, requires expert input, and is further complicated by the challenges of the underwater environment. To address these challenges, we propose a novel pipeline using stable diffusion to augment a collected real dataset with photorealistic synthetic data. This method enhances the dataset used to train a YOLOv10-based vision model. The model is then deployed and tested on an edge platform; Aqua2, an Autonomous Underwater Vehicle (AUV), achieving a state-of-the-art 0.657 mAP@50 for oyster detection.

Infant-robot interaction has been increasingly gaining attention, yet, there are limited studies on the development of robot-assisted environments that promote perceptual-motor development in infants. This paper assesses the feasibility of operating a spherical mobile robot, Sphero, to engage infants in perceptual-motor exploration of an open area. Two case scenarios were considered. In the first case, Sphero was the only robot providing stimuli in the environment. In the second case, two additional robots provided stimuli along with Sphero. Pilot data from two infants were analyzed to extract information on their visual attention to and physical interaction with Sphero, as well as their motor actions. Overall, infants (i) expressed a preference to Sphero regardless of stimulation levels, and (ii) moved out of stationary postures in an effort to chase and approach Sphero. These preliminary findings provide support for the future implementation of Sphero in robot-assisted learning environments to promote perceptual-motor development in infants.

Marine autonomous vehicles deployed to conduct marine geophysical surveys are becoming an increasingly used asset in the commercial, academic, and defense industries. However, the ability to collect high-quality data from applicable sensors is directly related to the robustness of vehicle motion caused by environmental disturbances. In this paper we designed and integrated a new path following controller on an autonomous surface vehicle (ASV) that minimizes the linear and angular accelerations on the sensor's local frame. Simulation and experimental results verify reduction of vehicle motion, improvement in path following, and improvement in preliminary sonar data quality compared to that of the existing proportional-yaw path following controller.

Accidental or deliberate disruption of the coordination function in a multi-agent system has been discussed and referred to in the social sciences literature as leader decapitation; this paper outlines a methodology for making multi-agent networks resilient to this type of failure, enabling a timely restoration of operation normalcy by leveraging machine learning techniques. The approach involves endowing the agents with a cascade of independent learning modules that enable them to discover over time their role in the overall system coordinating strategy, so that they are able to autonomously implement it when central coordination seizes to function. Through these machine learning algorithms, the agents incrementally identify the overall system's task specification and simultaneously optimize their strategy to serve the common goal.

Providing infants with opportunities to engage in a variety of motor actions early in life impacts their later development. Our recent work utilizes socially assistive robots (SARs) and body weight support (BWS) technology to provide infants such opportunities. This paper examines the motor actions demonstrated by an infant with Down syndrome during their spontaneous interactions with the SARs and while using BWS assistance. The infant participated in eight 1-hour sessions over the course of four weeks. By using methodological tools from social networks, we identify and describe the complex nature of the infant’s motor actions displayed during interaction with both types of technology over time. The changes in these networks informs the development of robot-assisted learning environments that can be applied at this critical life period.

Activity recognition in children and infants is important in applications such as safety monitoring, behavior assessment, and child-robot interaction, among others. However, it differs from activity recognition in adults not only because body poses and proportions are different, but also because of the way in which actions are performed. This paper addresses the problem of infant action classification in challenging conditions. The actions are performed in a pediatric rehabilitation environment in which not only infants but also robots and adults are present, with the infant being one of the smallest actors in the scene. We propose a multiview action classification system based on Faster R-CNN and LSTM networks, which fuses information from different views by using learnable fusion coefficients derived from detection confidence scores. The proposed system is view-independent, learns features that are close to view-invariant, and can handle new or missing views at test time. Our approach outperforms the state-of-the-art baseline model for a small dataset (2 subjects, 10-24 months old) by 11.4% in terms of average classification accuracy in four classes (crawl, sit, stand and walk). Moreover, experiments in an extended dataset (6 subjects, 8-24 months old) show that the proposed fusion strategy outperforms all the alternative fusion methods studied.

This paper presents a synchronization controller for mobile sensors that are minimally actuated and can only communicate with each other over a very short range. This work is motivated by ocean monitoring applications where large-scale sensor networks consisting of drifters with minimal actuation capabilities, i.e., active drifters, are employed. We assume drifters are tasked to monitor regions consisting of gyre flows where their trajectories are periodic. As drifters in neighboring regions move into each other's proximity, it presents an opportunity for data exchange and synchronization to ensure future rendezvous. We present a nonlinear synchronization control strategy to ensure that drifters will periodically rendezvous and maximize the time they are in their rendezvous regions. Numerical simulations and small-scale experiments validate the efficacy of the control strategy and hint at extensions to large-scale mobile sensor networks.

The paper presents a receding horizon planning strategy for a quadrotor-type mav to navigate through an unknown cluttered environment at high speed. Utilizing a lightweight on-board short-range sensor that generates point-clouds within a narrow Field of View (FOV), the reported approach generates safe and dynamically feasible trajectories within the fov of the sensor, which the mav uses to navigate without relying on any global planner or prior information about the environment. The effectiveness of this planner-controller combination is demonstrated in both indoor and outdoor tests featuring speeds of up to of 3.5 m/s. With minor adjustments, the local motion planner can be utilized for interception and tracking of a moving target; evidence to this effect are provided in the form of numerical (Gazebo) simulations. Given the absence of any global information about the robot's workspace, the extent to which the local planner can provide convergence guarantees is limited; when complemented by a global planner and/or target tracker, the reported lower-level, sensor-driven reactive motion control strategy completes the autonomous mav navigation stack, enabling navigation in dynamic, uncertain, and partially-known environments with guaranteed convergence to any static or dynamic target.

This paper suggests reflections can be practically useful if they are included in planning for collision capable robot platforms. By modifying a proven strategy for navigation with reflections we maintain global convergence results and reach the goal in less time. An algorithm for identifying reflection surfaces for a given cell decomposition is reported. Baseline and reflected scenarios are compared for two different cell decompositions. Omnipuck, a reflection capable omnidirectional robot meant to store and release impact energy, is used to obtain experimental results and draw conclusions for future work.

This paper formally constructs navigation functions with time-varying destinations on star worlds. The construction is based on appropriate diffeomorphic transformations and extends an earlier sphere-world formulation. A new obstacle modeling method is also introduced, reducing analytical complexity and offering unified expressions of common classes of n-dimensional obstacles. The method allows for dynamic target tracking and is validated through simulations and experiments.

This paper reports on a model-based control strategy for miniature legged robots tasked with navigation in cluttered environments. Our approach uses a new model for crawling locomotion to derive closed-form expressions of state propagation. The latter enable the development of a feedback control navigation strategy. The strategy consists of a waypoint tracking controller that steers the system along desired paths and an outer control loop that updates the reference path to account for uncertainty. This strategy allows noise-resilient navigation for miniature legged robots and is experimentally validated on an eight-legged robot that navigates in obstacle-cluttered environments.

This paper addresses the problem of optimal control of a unicycle-type robot perturbed with stochastic noise in an environment with sparsely populated obstacles. The objective is that the robot pose converges to a neighborhood of a desired position and orientation. A feedback control law is constructed such that it is compatible with the differential constraints of the unicycle. The construction is based on numerical solution of the Hamilton-Jacobi-Bellman (HJB) partial differential equation (PDE) associated with a stochastic optimal control problem. The control law is optimal in terms of control effort and comes with probabilistic guarantees of convergence to the goal set.

The paper reports on the design and preliminary experimental testing of a novel 3D-printed miniature legged robot. It is called Passively Sprawling Robot (PSR), and it features a mechanism that achieves passive adjustment of the sprawl angle of the robot's legs. Passive sprawling in this robot exhibits compliance by design, yet it can be controlled indirectly by regulating the yaw rate. Spring-loaded assemblies on the left and right side of the robot function independently, allowing the vehicle to overcome asymmetrical obstacles with improved lateral stability, and withstand falls from moderate heights without sustaining structural damage. The regulation of the sprawling angle by means of varying yaw rates, as well as the improved motion characteristics have been experimentally observed and verified, and open-loop motion accuracy along straight and constant curvature paths was tested on a number of repeated trials.

This paper approaches from an optimal control perspective the problem of fixed-time detection of mobile radioactive sources in transit by means of a collection of mobile sensors. Under simplifying assumptions on the geometry of the source, sensors, and surrounding environment, and with knowledge of the source's trajectory, it is shown that the optimal control problem admits an intuitive, analytic closed-form solution. This is facilitated by the availability of analytic expressions for bounds on the probabilities of detection and false alarm during a Neyman-Pearson detection test. The intuition derived from this analytic solution leads to a motion control law that steers suboptimally the sensors to an arbitrarily small neighborhood of the suspected source, while navigating among stationary obstacles in their environment, making a step closer to a practical physical implementation.

We present our findings on the first application of motion planning methodologies to the recently introduced Sprawl Tuned Autonomous Robot (STAR). The reported results provide a first glimpse on the capabilities of this novel, 3D-printed robot in performing autonomously non-trivial motion planning tasks in environments populated with obstacles. We employ methods from sampling-based motion planning under nonholonomic constraints, and implement in open loop the generated path on the physical robot for various environments of increasing complexity.

This paper presents an approach to emulate the Poisson process observed by a sensor when subject to low levels of radiation, motivated by the problem of detecting a weak source in the presence of background radiation. We construct a physical emulation of this process to serve as a means of experimentation for various detection models involving mobile sensor networks. A pulsing laser emulates the nuclear emission, and a rotating mirror deflects the pulses in a random direction. The degree to which the proposed emulation process matches actual radiation measurement results is assessed experimentally, and the utility of the device is demonstrated and compared against conventional methods in a simple detection scenario.

The paper suggests a new method for statistically validating, and selecting the parameters of a model for a miniature eight-legged robot. It is based on a novel adaptation of concepts and techniques originally developed in the context of robust control design using randomized algorithms. The proposed approach is data driven and offers probabilistic guarantees of model fidelity and descriptive capacity, checking models against experimental data. In principle, this method applies to a large class of physical processes, the available models of which may be in a variety of forms including sets of differential equations.

In this paper we consider a nonholonomic system in the form of a unicycle and steer it to the origin so that both position and orientation converge to zero while avoiding obstacles. We introduce an artificial reference field, propose a discontinuous control policy consisting of a receding horizon strategy and implement the resulting field-based controller in a way that theoretically guarantees for collision avoidance; convergence of both position and orientation can also be established. The analysis integrates an invariance principle for differential inclusions with model predictive control. In this approach there is no need for the terminal cost in receding horizon optimization to be a positive definite function.

This paper presents a receding horizon control design for a robot subject to stochastic uncertainty, moving in a constrained environment. Instead of minimizing the expectation of a cost functional while ensuring satisfaction of probabilistic state constraints, we propose a two-stage solution where the path that minimizes the cost functional is planned deterministically, and a local stochastic optimal controller with exit constraints ensures satisfaction of probabilistic state constraints while following the planned path. This control design strategy ensures boundedness of errors around the reference path and collision-free convergence to the goal with probability one under the assumption of unbounded inputs. We show that explicit expressions for the control law are possible for certain cases. We provide simulation results for a point robot moving in a constrained two-dimensional environment under Brownian noise. The method can be extended to systems with bounded inputs, if a small nonzero probability of failure can be accepted.

We propose a new model to describe the horizontal motion of an eight-legged bio-inspired miniature robot. The model does not include compliance, and can capture the kinematics of the observed locomotion behavior that corresponds to an alternating tetrapod gait. We exploit symmetries and synergies to reduce the eight-legged robot to a mechanism that is composed by two switching four-bar linkages, each representing the collective effect of a tetrapod in contact with the ground. Notwithstanding its apparent simplicity, the resulting model reproduces on average the motion of the robot. In addition, by properly tuning a family of physically-relevant parameters - including touchdown and sweep angles - different motion primitives corresponding to circular and forward motions can be realized. This model represents a first step toward developing reduced-order kinematic representations of legged robots that can be used for motion planning and feedback control purposes.

This paper presents a methodology to design a control law that can provably steer a group of mobile agents to a free-floating formation of specific shape, while avoiding collisions. We indicate why existing multiagent solutions have a theoretical limitation due to one of the working assumptions of the single-agent potential function approach being violated. A new nonsmooth type of multiagent potential functions is, thus, developed. It is shown how this extension of the navigation function concept to multiagent systems ensures the nondegeneracy of the critical points of the potential function.

The analysis in this paper applies to robots with dynamics described by a stochastic differential equation, which need to navigate in constrained environments. The approach offers a method to calculate the probability that a feedback control policy designed for the drift component of the dynamics, will succeed in allowing the robot to avoid collisions and converge to its navigation goal in the presence of stochastic (white) noise. The problem is formulated as an exit problem and known techniques in the field of stochastic processes are brought to bear to determine the probabilities that the stochastic process describing the motion of the robot will ¿exit¿ the workspace through a particular part of the boundary. We motivate the use of this analysis using a controller constructed using negative gradient of a navigation function and give the analytic solution for the case of a constrained but obstacle-free workspace.

This paper introduces a framework that guides the design of stabilizing feedback control laws for systems with Pfaffian constraints. A new class of N-dimensional vector fields, the dipole-like vector fields is proposed, inspired by the form of the flow lines of the electric point dipole. A general connection between the dipole-like field and the Pfaffian constraints of catastatic nonholonomic systems is exploited, to establish systematic guidelines on the design of stabilizing control laws. The methodology is applied to the stabilization of the unicycle and of the nonholonomic double integrator. Based on these guidelines, switching control laws are constructed. The efficacy of the methodology is demonstrated through simulation results.

The paper suggests a new approach to navigation of mobile robots, based on nonlinear model predictive control and using a navigation function as a control Lyapunov function. In this approach, the nonlinear optimal control problem is treated using randomized algorithms. The advantage of the proposed combination of navigation functions for robot motion planning with randomized algorithms within an MPC framework, is that the control design offers stability by design, is platform independent, and allows the designer to trade-off performance for (computation) speed, according to the application requirements.

We develop a switched cooperative control scheme, to coordinate groups of ground and aerial vehicles for the purpose of locating a moving target in a given area. We do so by stabilizing the ground group into a guarding formation using a navigation function, and then steering the aerial group along a trajectory that uniformly scans the enclosed regions. The novelty of the approach lays in combining decentralized flocking algorithms with navigation functions for obstacle avoidance, convergence to designated position, and direction control.

We develop a velocity field tracking-control scheme with proportional integral force feedback to transport and manipulate deformable material with a mobile manipulator. We assume that the deformable object is a damped underactuated mechanical system. The input-to-state stability properties of its zero dynamics are used to derive bounds on the admissible end-effector velocities and accelerations. It is shown both analytically and in simulation that using deformation feedback, we can avoid exciting excessive object deformations.

We present a navigation function through which a group of mobile agents can be coordinated to achieve a particular formation, both in terms of shape and orientation, while avoiding collisions between themselves and with obstacles in the environment. Convergence is global and complete, subject to the constraints of the navigation function methodology. Algebraic graph theoretic properties associated with the interconnection graph are shown to affect the shape of the navigation function. The approach is centralized but the potential function is constructed in a way that facilitates complete decentralization. The strategy presented will also serve as a point of reference and comparison in quantifying the cost of decentralization in terms of performance.

The paper introduces a set of nonsmooth control laws that enable a group of vehicles to synchronize their velocity vectors and move as a flock while avoiding collisions with each other and with static obstacles in their environment. In addition, all vehicles converge to a common destination point, accomplishing a group mission. The proposed control law can steer each vehicle based on local information that can be obtained from within a spherical neighborhood around it. While only the nearest neighbors and obstacles affect a vehicle's motion, as the vehicles move the neighborhoods change discontinuously. The induced discontinuities in the control law of each vehicle do not affect the stability properties of the group collision free motion and connectivity requirements on the interconnection network can be relaxed due to the common objective.

The paper investigates the stability properties of mobile agent formations which are based on leader following. We derive nonlinear gain estimates that capture how leader behavior affects the interconnection errors observed in the formation. Leader-to-formation stability (LFS) gains quantify error amplification, relate interconnection topology to stability and performance, and offer safety bounds for different formation topologies. Analysis based on the LFS gains provides insight to error propagation and suggests ways to improve the safety, robustness, and performance characteristics of a formation.

We apply a novel motion planning and control methodology, which is based on a non-smooth navigation function, to a point mobile robot moving amongst moving obstacles. The chattering introduced by the discontinuous potential field is suppressed using non-smooth backstepping. The combined controller guarantees global asymptotic convergence and collision avoidance. This controller is particularly suitable for real time implementation on systems with limited computational resources. The effectiveness of the proposed scheme is verified through computer simulations.

The paper presents the first result on ISS properties of dynamic unicycle models describing nonholonomic mobile robots. It is known that ISS is related to smooth stabilizability, however this relation cannot exclude the possibility of non smoothly stabilizable systems enjoying ISS properties. In fact, it is shown that in a certain topology that seems to suit the nonholonomic nature of the mobile robot, and by applying a particular control law, the closed loop system can be rendered locally ISS. Apart from any possible theoretical ramifications, this result encourages an ISS-based stability analysis of groups of mobile robots.

This paper presents the first motion planning methodology applicable to articulated, nonpoint nonholonomic robots with guaranteed collision avoidance and convergence properties. It is based on a new class of nonsmooth Lyapunov functions and a novel extension of the navigation function method to account for nonpoint articulated robots. The dipolar inverse Lyapunov functions introduced are appropriate for nonholonomic control and offer superior performance characteristics compared to existing tools. The new potential field technique uses diffeomorphic transformations and exploits the resulting point-world topology. The combined approach is applied to the problem of handling deformable material by multiple nonholonomic mobile manipulators in an obstacle environment to yield a centralized coordinating control law. Simulation results verify asymptotic convergence of the robots, obstacle avoidance, boundedness of object deformations, and singularity avoidance for the manipulators.

Presents a method of performing integrator backstepping in systems that are discontinuous, either due to their inherent structure or because of the applied control input. The proposed technique is applied to the stabilization problem of the dynamic system of a nonholonomic mobile robot. Simulation studies indicate that the methodology can also help alleviate the problem of chattering that is commonly associated with discontinuous nonholonomic controllers.

A new type of stability of leader follower formations is defined, based on input-to-state stability (ISS) properties of cascade interconnections. Formation ISS links leader input to internal state of the formation and characterizes the way this input affects performance. The effect of feedforward and feedback inter-agent communication is then investigated in this framework and it is indicated how the structure of interconnections and the amount of available information can affect stability performance.

The paper presents a new position/force controller, based on the philosophy of the parallel approach. The controller exploits the reaction compensation action of the inverse dynamics position controller and achieves superior transient performance. It incorporates a velocity dependent damping term. Stability is established and conditions for the control parameters are derived. Performance of the proposed controller is verified through computer simulations.

This paper presents a motion planner and nonholonomic controller for a mobile robot, with global collision avoidance and convergence properties. An appropriately designed (dipolar) potential field is combined with discontinuous state feedback. A new class of Lyapunov functions is introduced and used for nonholonomic navigation. The obstacle avoidance and global asymptotic stability properties are verified through simulations.

A nonholonomic motion planner for mobile manipulators moving in cluttered environments is presented. The approach is based on a discontinuous feedback law under the influence of a special potential field. Convergence is shown via Lyapunov's direct method. Utilizing redundancy, the methodology allows the system to perform secondary, configuration dependent, objectives such as singularity avoidance. It introduces an efficient feedback scheme for real time navigation of nonholonomic systems.

A manipulated deformable object is viewed as an underactuated mechanical system. In this context controllability issues are discussed and results on the nature of the constraints and the controllability properties of an important class of deformable objects being modeled with finite elements are stated. For this class of deformable objects the results permit to circumvent the usual procedure of calculating Lie brackets to establish a base for the associated Lie algebra, and answers the question of determining the kind of constraints imposed on the system in a straightforward algebraic way. Inequality constraints associated to material strength limitations are also included.