Small-scale magnetic robots that can assemble, disassemble, and propel under globally applied magnetic fields can be versatile modular subunits for manufacturing and in vivo operations. This paper presents a magnetic cuboid robot that contains assembled cubes with encapsulated, freely-rotating permanent magnets. This minimalistic and scalable design enables magnetic cubes to assemble under magnetic fields into a cube chain that can propel using pivot-walking locomotion. The magnets for propulsion are evenly distributed between the cubes, but individual cubes can only move when joined with at least one other. A vision-based closed-loop controller that modulates the cuboid robot’s position and orientation during pivot walking is presented. The controller is simulated to navigate cuboid robots to user-selected goal locations. A Breadth-First Search (BFS) path-planning algorithm for obstacle avoidance is used to generate optimal paths for closed-loop pivot walking. Two physical workspaces are tested, one with a large free space and the other with a maze. Experiments and simulations demonstrate that magnetic cuboid robots can navigate in complex mazes and selectively self-assemble into cube chains while following the optimal path generated by the motion planner with visual feedback control.

Objective: Flexible endoscopy is a valuable tool in diagnostic procedures, enabling examination of internal areas via natural orifices. An actuation system tends to improve the procedural outcomes by enabling controlled movements of the endoscope and offering a stable view of the operative field. A user interface is used to issue actuation commands to these systems. Thus, selection of an ideal user interface is vital to improve the ergonomics for the endoscopist and to ensure efficient endoscope navigation. The objective of this work is to perform an in-depth comparative analysis of various user interfaces to optimize endoscope maneuverability. Methods and procedures: A custom-built actuation system was used to maneuver a flexible endoscope. The actuation system enabled translational and rotational movement of the endoscope’s shaft as well as supported left/right and up/down steering of the endoscope’s distal end. Four user interfaces (head-motion based device, eye-gaze based device, a stylus, and a joystick) working under three interaction modes (continuous, discrete, threshold) along with a clutching mechanism were used to issue commands to the actuation system. A user study was conducted to assess the effectiveness of the user interfaces for two scenarios: Scenario-A, which involved maneuvering the endoscope’s distal end to focus on a localized operative field, and Scenario-B, which required targeting polyps during the withdrawal phase of a simulated colonoscopy. Results: In Scenario-A, the head motion-based device and stylus, when used in continuous interaction mode, resulted in the shorter task duration and fewer clutches. The joystick, operating under threshold interaction mode, also demonstrated a reduced task duration. Additionally, the joystick led to fewer instances of the endoscope’s focus shifting outside the localized operative field. In Scenario-B, eye gaze-based device under discrete interaction mode took the longest duration for task completion. The continuous mode of the stylus took the shortest duration to target polyps, once visualized in the operating field. However, it also required the highest number of clutches compared to other user interface and interaction modes. Conclusion: The joystick consistently outperformed other interfaces across all interaction modes. Performance among the other user interfaces varied based on the parameters of the scenarios. Head motion-based and eye-based user interfaces enabled hands-free manipulation of the endoscope. This study establishes a benchmark for enhancing both user interfaces and interaction modes in actuated flexible endoscopy.

Autonomous drone swarms deployed for surveillance, environmental monitoring, and infrastructure inspection must maintain reliable coverage of critical assets despite robot failures. This requires multi-coverage: each asset must be observed by multiple robots for redundancy, with coverage requirements varying by asset importance. While recent work [3] has solved the centralized problem optimally using integer programming, practical deployments face constraints that demand distributed solutions: robots operate with limited communication ranges, onboard computation restricts global planning, and partial system failures must not cause mission abort. We present a distributed multi-coverage algorithm for robot swarms operating with local sensing, local communication, and no global coordination (Code available at: https://doi.org/10.5281/zenodo.18626854 ).

We study the problem of deploying |R| mobile robots to maximize visibility coverage in a polygonal workspace while maintaining a line-of-sight communication path to a home location. The environment contains opaque obstacles, and robots become stationary sensing nodes once placed. Each node exposes visibility frontier windows—open segments of the current visibility boundary—and available robots evaluate these windows to select the point that yields the largest incremental visible area. Robots bid their predicted gain, and a decentralized auction installs the highest bidder as the next stationary node, preserving a connected “min-link” backbone. The process repeats until all robots are placed or the environment is fully covered. We present the algorithm, its complexity, and communication requirements. Experiments on synthetic maps show rapid, monotone growth of visible area and effective distributed decision-making.

We consider the problem of reconfiguring a two-dimensional connected grid arrangement of passive building blocks from a start configuration to a goal configuration, using a single active robot that can move on the tiles, remove individual tiles from a given location and physically move them to a new position by walking on the remaining configuration. The objective is to determine a schedule that minimizes the overall makespan, while keeping the tile configuration connected. We provide both negative and positive results. (1) We generalize the problem by introducing weighted movement costs, which can vary depending on whether tiles are carried or not, and prove that this variant is NP-hard. (2) We give a polynomial-time constant-factor approximation algorithm for the case of disjoint start and target bounding boxes, which additionally yields optimal carry distance for 2-scaled instances.

Four prominent collision avoidance methods are Arti.cial Potential Fields, Arti.cial Potential Fields expressed as Control Barrier Functions, Control Barrier Functions, and Reciprocal Velocity Obstacles. Prior work often assumes all agents are using the same obstacle avoidance methods. The methods differ in computational scalability, how they react to different types of obstacles, and in how they react to agents with heterogenous collision avoidance methods. This paper explores the scenario robustness and scalability of these methods through three key navigation scenarios: different structures of stationary obstacles, circle-crossing collision avoidance benchmarks, and defense against an antagonistic swarm.

Miniature Magnetic Rotating Swimmers (MMRSs) are emerging as a promising technology to improve minimally invasive vascular and cardiac surgeries. Currently, these procedures are typically performed using catheters, which are thin flexible tubes inserted into blood vessels. However, catheters rub against artery walls and can dislodge fat deposits, which can lead to complications such as stroke. In contrast, MMRSs are untethered, wirelessly controlled devices actuated by an external magnetic field. Their compact size could allow them to navigate the bloodstream of a patient and reach treatment areas without the risks associated with catheter use. Surgical tasks, such as tissue cutting or ablation, require significant power, but due to their miniature size, MMRSs lack the capacity to store sufficient onboard energy. This paper studies an MMRS that can be heated wirelessly via induction. The method allows transferring enough power to denature proteins.

A limited number of drones must monitor a large number of assets whose future motions are unknown, ensuring that each asset is monitored by at least one drone. The ideal configuration places the drone’s sensors as close as possible to their assets. This objective is achieved by minimizing the altitude of all drones, which in turn reduces the total area of their ground coverage footprints. There exists an optimal assignment of assets to drones. However, if the assets are moving, then the optimal assignment is not static. Assets must instead be swapped between drones.We present centralized and decentralized methods to cluster our moving assets statically each control iteration, and then our cover algorithm guarantees continuous 100% coverage over our moving assets while minimizing the total area covered by drones.

We present strategies for placing a swarm of mobile relays to provide a bi-directional wireless network that connects fixed (immobile) terminals. Neither terminals nor relays are permitted to transmit into disk-shaped no-transmission zones. We assume a planar environment and that each transmission area is a disk centered at the transmitter. We seek a strongly connected network between all terminals with minimal total cost, where the cost is the sum area of the transmission disks. Results for networks with increasing levels of complexity are provided. The solutions for local networks containing low numbers of relays and terminals are applied to larger networks. For more complex networks, algorithms for a minimum-spanning tree (MST) based procedure are implemented to reduce the solution cost. A procedure to characterize and determine the possible homotopies of a system of terminals and obstacles is described, and used to initialize the evolution of the network under the presented algorithms.

We implement and evaluate different methods for the reconfiguration of a connected arrangement of tiles into a desired target shape, using a single active robot that can move along the tile structure. This robot can pick up, carry, or drop off one tile at a time, but it must maintain a single connected configuration at all times. Becker et al. [5] recently proposed an algorithm that uses histograms as canonical intermediate configurations, guaranteeing performance within a constant factor of the optimal solution if the start and target configuration are well-separated. We implement and evaluate this algorithm, both in a simulated and practical setting, using an inchworm type robot to compare it with two existing heuristic algorithms.

This study investigates the efficacy of an untethered magnetic robot (UMR) for wireless mechanical and hybrid blood clot removal in ex vivo tissue environments. By integrating x-ray-guided wireless manipulation with UMRs, we aim to address challenges associated with precise and controlled blood clot intervention. The untethered nature and size of these robots enhance maneuverability and accessibility within complex vascular networks, potentially improving clot removal efficiency. We explore mechanical fragmentation, chemical lysis, and hybrid dissolution techniques that combine mechanical fragmentation with chemical lysis, highlighting their potential for targeted and efficient blood clot removal. Through experimental validation using an ex vivo endovascular thrombosis model within the iliac artery of a sheep, we demonstrate direct revascularization of a 13-mm-long, 1-day-old blood clot positioned inside the left common iliac artery. This was achieved by deploying a UMR into the abdominal aorta within 15 min. Additionally, both mechanical fragmentation and hybrid dissolution achieve a greater volume rate of change compared to no intervention (control) and chemical lysis alone. Mechanical fragmentation exhibits clot removal with a median of 0.87 mm3/min and a range of 2.81 mm3/min, while the hybrid approach demonstrates slower but more consistent clot removal, with a median of 0.45 mm3/min and a range of 0.23 mm3/min.

Robotic scope assistant systems allow surgeons to adjust the operative field view during surgery by robotically maneuvering laparoscopes. A Human-Robot Interface (HRI) is used for issuing commands to these systems, with an interaction mode mapping these commands to laparoscope movements. Optimizing the HRI and interaction mode can streamline laparoscope positioning as well as reduce cognitive workload, helping the surgeon focus on the surgical procedure. Comparing and assessing various HRIs and interaction modes is essential for efficient laparoscope maneuvering. This study evaluates HRIs based on head-motion, eye-motion, hand-motion, and voice-input operating under three interaction modes (namely: discrete, continuous, and threshold). The participants performed a user study comparing different HRIs under two simulated surgical scenarios (one in a real environment and the other in a virtual environment). The results indicated that head and eye-based HRIs performed well in continuous interaction mode, while the voice-based interface suffered from a delay. Conversely, hand-based HRIs demonstrated superior performance in both scenarios across all evaluation parameters. The study provides a benchmark for the comparison of different HRIs and provides insights into the effectiveness, limitations, and potential advantages of different HRIs.

External factors, including urban canyons and adversarial interference, can lead to Global Positioning System (GPS) inaccuracies that vary as a function of the position in the environment. This study addresses the challenge of estimating a static, spatially-varying error function using a team of robots. We introduce a State Bias Estimation Algorithm (SBE) whose purpose is to estimate the GPS biases. The central idea is to use sensed estimates of the range and bearing to the other robots in the team to estimate changes in bias across the environment. A set of drones moves in a 2D environment, each sampling data from GPS, range, and bearing sensors. The biases calculated by the SBE at estimated positions are used to train a Gaussian Process Regression (GPR) model. We use a Sparse Gaussian process-based Informative Path Planning (IPP) algorithm that identifies high-value regions of the environment for data collection. The swarm plans paths that maximize information gain in each iteration, further refining their understanding of the environment's positional bias landscape. We evaluated SBE and IPP in simulation and compared the IPP methodology to an open-loop strategy.

A common robotics sensing problem is to place sensors to robustly monitor a set of assets, where robustness is assured by requiring asset $p$ to be monitored by at least $\kappa(p)$ sensors. Given $n$ assets that must be observed by $m$ sensors, each with a disk-shaped sensing region, where should the sensors be placed to minimize the total area observed? We provide and analyze a fast heuristic for this problem. We then use the heuristic to initialize an exact Integer Programming solution. Subsequently, we enforce separation constraints between the sensors by modifying the integer program formulation and by changing the disk candidate set.

We present and compare numerically three methods to determine the optimal configurations of a network of agents for the joint tasks of communication and target tracking. The first two methods, “Tracking first”(TF) and “Communication first”(CF), rely on an iterative procedure where the two tasks take turns in individually optimizing the network and differ in the choice of the startup task. The third method, “Simultaneous optimization”(SO), accounts for optimization constraints of both tasks and satisfies them simultaneously. Method TF yields the best results in terms of cost, while method SO is the fastest.

We present a magnetic camera system developed to detect ferrous or ferromagnetic objects. The main motivation is detection and tracking of underwater pipelines. Many industries, such as oil and gas, must perform inspection and maintenance of pipelines and automation is desirable. An electromagnet generates a static magnetic field which is read by an array of Hall-effect sensors. The presence of ferromagnetic materials distorts this field, which can be detected by the sensors and creates a magnetic image. The grid configuration of the camera allows for quick computation of the center of mass and general orientation of detected pipes, facilitating tracking. This camera is carried by an ROV and tested in a pool environment.

We present progress on the problem of reconfiguring a 2D arrangement of building material by a cooperative group of robots. These robots must avoid collisions, deadlocks, and are subjected to the constraint of maintaining connectivity of the structure. We develop two reconfiguration methods, one based on spatio-temporal planning, and one based on target swapping, to increase building efficiency. The first method can significantly reduce planning times compared to other multi-robot planners. The second method helps to reduce the amount of time robots spend waiting for paths to be cleared, and the overall distance traveled by the robots.

We present an analytic solution to the 3D Dubins path problem for paths composed of an initial circular arc, a straight component, and a final circular arc. These are commonly called CSC paths. By modeling the start and goal configurations of the path as the base frame and final frame of an RRPRR manipulator, we treat this as an inverse kinematics problem. The kinematic features of the 3D Dubins path are built into the constraints of our manipulator model. Furthermore, we show that the number of solutions is not constant, with up to seven valid CSC path solutions even in non-singular regions. An implementation of solution is available at https://github.com/aabecker/dubins3D.

Magnetic modular cubes are cube-shaped bodies with embedded permanent magnets. The cubes are uniformly controlled by a global time-varying magnetic field.A 2D physics simulator is used to simulate global control and the resulting continuous movement of magnetic modular cube structures. We develop local plans, closed-loop control algorithms for planning the connection of two structures at desired faces. The global planner generates a building instruction graph for a target structure that we traverse in a depth-first-search approach by repeatedly applying local plans.We analyze how structure size and shape affect planning time. The planner solves 80% of the randomly created instances with up to 12 cubes in an average time of about 200 seconds.

Objective: Variable-view rigid scopes offer advantages compared to traditional angled laparoscopes for examining a diagnostic site. However, altering the scope’s view requires a high level of dexterity and understanding of spatial orientation. This requires an intuitive mechanism to allow an operator to easily understand the anatomical surroundings and smoothly adjust the scope’s focus during diagnosis. To address this challenge, the objective of this work is to develop a mechanized arm that assists in visualization using variable-view rigid scopes during diagnostic procedures.Methods: A system with a mechanized arm to maneuver a variable-view rigid scope (EndoCAMeleon - Karl Storz) was developed. A user study was conducted to assess the ability of the proposed mechanized arm for diagnosis in a preclinical navigation task and a simulated cystoscopy procedure.Results: The mechanized arm performed significantly better than direct maneuvering of the rigid scope. In the preclinical navigation task, it reduced the percentage of time the scope’s focus shifted outside a predefined track. Similarly, for simulated cystoscopy procedure, it reduced the duration and the perceived workload.Conclusion: The proposed mechanized arm enhances the operator’s ability to accurately maneuver a variable-view rigid scope and reduces the effort in performing diagnostic procedures.Clinical and Translational Impact Statement: The preclinical research introduces a mechanized arm to intuitively maneuver a variable-view rigid scope during diagnostic procedures, while minimizing the mental and physical workload to the operator.