Estimating above-ground biomass (AGB) is essential for monitoring vegetation and assessing carbon stock. Traditional field-based methods, although accurate, are costly and difficult to implement in densely vegetated environments. In this context, the use of UAVs equipped with LiDAR sensors has emerged as a promising solution for forest mapping. This study integrates Coverage Path Planning (CPP) algorithms with an autonomous navigation system that uses obstacle avoidance, enabling efficient exploration of unknown areas. Three CPP approaches were evaluated: Polygon Coverage, Grid Coverage, and Spiral Coverage. The experimental results indicate that combining the Grid Coverage method with autonomous navigation enhances forest area coverage, reduces execution time, and optimizes data acquisition for biomass estimation.
Interaction control has become increasingly important with the rise of human-robot collaboration. Among various strategies, impedance and admittance control stand out by regulating the relationship between velocity and force, rather than individual system states. Impedance control offers stability in stiff environments but may lack precision in free space, while admittance control performs well in non-contact tasks but can be unstable with stiff environments. Since stiff environments exhibit high admittance, impedance control is generally preferred, and vice versa. To overcome the limitations of each method, hybrid systems combining both approaches have been developed, typically categorized as switch-based or non-switch-based. This study proposes a comparative framework for such hybrids using a novel visualization method: the impedance space. New metrics are introduced to assess how well these systems achieve the target impedance, enabling quantitative performance evaluation across varying environmental conditions.
This paper presents the case study of admittance controller with dynamic compensator and impedance modulation for a Robotic Cane. The admittance controller provides easy control of the device without user instrumentation. Dynamic compensation corrects for the delay of the device’s dynamics. Impedance modulation provides the device with a fast and soft response. A female post-surgical user, 1.57m in height, 56kg in weight, and 32 years of age, participates in the experiments, which were carried out on a straight road. The support by Robotic Cane was evaluated applying the NASA TLX, the SUS and one adapted questionnaire. The responses obtained from the experimental test show that the control strategy presented in this paper is appropriate for providing the support required during the rehabilitation stage following knee surgery. The results show a low work load requirement (26.25%), high frequency of use (82.5%), and adequate perceived quality of the user experience (73.3%).
Learning from Demonstration (LfD) is a promising approach to teaching robotic manipulators by example, enabling intuitive and efficient behavior acquisition. However, most existing methods struggle with data efficiency and generalization, particularly in scenarios where only a few demonstrations are available. In this work, we propose an architecture that combines Task-Parameterized Gaussian Mixture Models (TP-GMM) with Bayesian Optimization (BO) for automatic hyperparameter tuning. TP-GMM enables the encoding of demonstrations in multiple local reference frames, capturing the relative dynamics of the task and facilitating generalization to unseen configurations. BO, in turn, automatically selects key model parameters, such as the number of components, covariance regularization, and input-output weights, by maximizing model performance via cross-validation. We validate the proposed approach in a real-world pick-and-place manipulation task using a Kinova Gen3 robot, with varying task parameters inferred through visual markers. By relying on only five demonstrations per subtask, our method achieved a 95% task success rate, outperforming both standard TP-GMM and a TP-GMM+EKF variant in terms of accuracy and consistency. These results demonstrate that our method outperforms non-parameterized models in accuracy and robustness while maintaining low computational cost and high sample efficiency, highlighting its potential for scalable and generalizable robotic learning.
This paper presents an enhanced version of the Blockly–ROS platform for mobile robot control, expanding its capabilities with loop structures and a gesture recognition module for Brazilian Sign Language (Libras). The proposed system integrates Blockly’s block-based visual programming with ROS 2 middleware, enabling both iterative logic and conditional control through real-time gesture detection using a YOLOv8n-based model. The platform was validated through a playful tic-tac-toe game scenario, where the robot autonomously navigated the board and executed movements based on hand-signed letters from the user. Experimental results demonstrate high accuracy in gesture recognition and precise trajectory execution, highlighting the potential of this approach for inclusive and interactive education. In particular, the system fosters playful Libras learning, motor laterality training, and the integration of computer vision with robotic control, offering a low-cost, accessible, and pedagogically robust environment for diverse educational contexts.
Swarm robotics offers a robust and flexible solution for complex tasks, but navigating through narrow passages often leads to congestion and coordination issues. To address this, we propose the Multiple Line Coordination (MLC) algorithm, which enhances a previous single-line approach by introducing a new behavioral state, two waiting sub-states for refined control, a line size limit, support for multiple simultaneous lines, and explicit failure management. We evaluated MLC through extensive simulations across various swarm sizes, comparing it with the baseline strategy. The results show that MLC significantly reduces the average distance traveled, shortens the duration of close encounters, lowers the frequency of line entries per robot, and increases the overall success rate, particularly in smaller swarms. These outcomes confirm that MLC improves coordination and robustness in spatially constrained environments.
This paper describes the methodological process used to find and validate patterns of human behavior in collision avoidance when a human being moves and interacts with other humans. This study will enable a mobile robot to be equipped with such patterns as a behavioral framework when, during navigation, it encounters humans with whom it may collide, so that it can avoid such collisions in a similar way to humans. These behavior patterns have been preliminarily simulated and found to resemble those of real humans.
Unmanned aerial vehicles (UAVs), especially those that carry significant payloads, generally have a short flight range. Having a mobile platform to support them in long-range missions might increase their effectiveness. Therefore, an unmanned surface vehicle (USV) was designed to increase the UAV’s flight time. This paper presents the development of a mathematical dynamic model for a catamaran-type USV in collaboration with the UAV. The established modeling has 6 degrees of freedom to analyze the coupled dynamics during the landing and take-off operations of the UAV. This model integrates rigid-body mechanics, with parameters derived from the USV’s three-dimensional CAD model, hydrodynamic effects, and external forces and moments generated by the UAV. Numerical simulations were conducted to analyze the USV’s response to these disturbances. The results quantify the platform’s response, including heave, roll, and pitch oscillations.
This paper presents a semantics-aware path-planning framework that enables quadrupedal robots to navigate environments by walking on specific terrains while considering safety, energy, and time constraints. The proposed method identifies key traversable elements and classifies them on a preference scale in a cost function where the weights are obtained from real-world experiments. Walking systems treat floors, stairs, and low vegetation as traversable; however, each terrain type significantly impacts safety, time, and energy efficiency. Pavements provide the best time and energy efficiency, whereas stairs require higher power consumption and take longer to traverse. Low vegetation, such as tall grass, can conceal hazards like holes, posing a threat to the robotic agent. The proposed method integrates a probabilistic global path planner with an interface that generates mission files for the quadrupedal robot ANYmal. The planner takes as input a segmented point cloud, which enables the identification of key features such as traversable and avoidable areas. Real-world experiments with the ANYmal robot in an outdoor environment were conducted to validate the effectiveness of the proposed approach.
Drones are increasingly used in outdoor applications such as search and rescue, surveillance, urban delivery and monitoring, all of which require real time image capture or video streaming. For more complex tasks, such as detecting elements of interest or performing visual tracking control, if the drone lacks the necessary hardware and computational power, not only image processing but also flight and trajectory control must be executed offboard. In these cases, a reliable communication link becomes essential to ensure both image transmission and flight stability. This work evaluates the communication limitations of drone navigation systems that rely on offboard controllers using only Wi-Fi networks. Such architectures enable off-the-shelf drones to perform complex tasks more efficiently by offloading intensive processing to external servers. However, flight stability becomes heavily dependent on communication quality, particularly in environments with variable connectivity. This study highlights the challenges posed by Wi-Fi networks, including high latency and its negative impact on real time control performance.
This paper presents a trajectory tracking controller for a cable-suspended load transported by n unmanned aerial vehicles, enhanced with a repulsion force to mitigate intra-formation collisions. The approach leverages the potential fields method, where each agent is repelled by the others in the formation. Experimental validation with n = 5 demonstrated the scalability of the proposed framework, highlighting improvements in both formation safety during navigation tasks and trajectory tracking performance when the repulsion force is active. Furthermore, the results show that the controller augmented with the repulsion force successfully accomplished tasks that the baseline controller failed to do.
The use of multi-robot teams in communication-denied environments poses a challenge to coordinated operations due to signal degradation and the lack of a direct line-of-sight (LOS) caused by obstacles. This paper introduces a centralized, coordinated motion planner designed to establish and maintain a communication chain by ensuring uninterrupted LOS connectivity. Our methodology leverages a visibility graph and a custom path-finding algorithm that optimizes for both path length and the number of required relay agents. The system then generates a deterministic initial order of robots movement through the path and incorporates a reactive re-planning mechanism to handle environmental interruptions, such as signal blockages or dynamic obstacles, reorganizing the robots to a new calculated path. The use of these two order of movements algorithms provides a flexible framework that allows a choice between a computationally intensive adaptive repair for a direct solution, or a simpler, faster full redeployment for more complex scenarios. The approach was validated in a simulation environment using ROS 2, Gazebo, and Open-RMF. Results demonstrate the fleet’s ability to execute a nominal deployment, detect and react to blockages by reconfiguring its formation, and circumnavigate obstacles while preserving LOS connectivity at every step, confirming the viability of the strategy for multi-robots communication networks. The comparison of the adaptive re-planing method against the deterministic one presented a gain of 12.21% of the total distance traveled by all the robots and 22.73% in the number of movement to reach the goal.
Autonomous navigation in complex environments requires robots to plan safe and robust paths, and potential field techniques based on Boundary Value Problems (BVP) have shown promise for this task. However, when applied to robot swarms, many existing approaches fail to maintain group cohesion in cluttered environments, often allowing the swarm to split unintentionally. In this work, we propose a novel strategy that dynamically computes elastic swarm formations using BVP-based potential fields with local distortions, while coordinating velocity to preserve formation integrity. Our method enables the swarm to expand or contract according to environmental constraints, ensuring uniform progress toward the goal without leaving individual robots behind. Experimental results in both simulated and real-world settings demonstrate that our approach allows the swarm to navigate safely and effectively through complex spaces, consistently reaching the goal as a single, cohesive group. In addition, comparative analysis with recent swarm navigation methods shows that our approach achieves favorable results, particularly in terms of consistent inter-robot spacing and stable formation behavior.
This work addresses the control problem of a foldable quadrotor whose in-flight arm reconfiguration changes its moment of inertia. A Linear Parameter Varying (LPV) controller is first designed to maintain consistent performance across configurations, but its computational cost limits its use on low-cost autopilots. Based on frequency domain analysis, we approximate the LPV with a low-order controller, akin to an adaptive PID, achieving similar response at lower complexity. Simulations and experimental tests on a Cortex M3-based platform, using both fixed-point and doubled-precision implementations, show similar behavior. The approach approximates LPV-like performance with PID-level efficiency, enabling real-time deployment on resource-constrained UAVs.
This paper explores the generation and registration of multidomain point clouds for offshore structures by integrating data from sensors on autonomous vehicles operating in two different environments: air and water. The maintenance and inspection of offshore structures, such as oil platforms and wind turbines, are challenging due to their remote locations and harsh environmental conditions. Thus, mobile robotics utilizing Unmanned Aerial Vehicles (UAVs) and Remotely Operated Vehicles (ROVs) provides a safer and more precise alternative for data collection. This research proposes the use of LiDAR and sonar sensors to capture 3D data of these multi-environment structures. The experimental validation is based on state-of-the-art simulations in HoloOcean. Registration algorithms such as Iterative Closest Point (ICP) and TEASER++ were evaluated for their effectiveness in merging point clouds from both domains, air and water. The findings indicate that while these algorithms perform well with geometrically simple and overlapping data, they fail to converge for complex, nonoverlapping point clouds from different sources, highlighting a significant challenge in cross-domain sensor fusion and opening a room for new algorithms dealing with multiple domains.
Hydraulic actuators are widely used in robotic systems due to their high power density and fast response capabilities. However, their control poses significant challenges, such as nonlinear flow-pressure characteristics, internal leakage, and complex friction dynamics. This paper proposes an enhanced model-based hydraulic load-independent force controller for impedance control, combining nonlinear feedback linearization with a Higher Order Nonlinear Disturbance Observer (HOn-DOB) to address these challenges and compensate for other unmodeled dynamics. The proposed approach can effectively impose the desired closed-loop dynamics under conditions, resulting in improved force-tracking performance that is robust to modelling errors. The efficacy of the controller is demonstrated through both simulation and experimental validation.
Coning and deconing twistlocks are human-intensive operations performed in ports around the world. These activities pose operational and safety risks, and automating these processes can significantly improve efficiency and throughput during port loading and unloading operations. This study presents an autonomous mobile collaborative robot designed for twistlock coning and deconing tasks in port settings, using machine vision and customized hybrid end-effectors to identify and manipulate container twistlocks. The developed system aims to achieve complete automation of the coning and deconing processes. Hybrid end effectors, which combine traditional and soft robotic components, were engineered to handle a wide range of commercial twistlocks. The proposed system’s key advantage lies in its adaptability to various commercial twist-lock types and its applicability across different horizontal transport methods and container types. The system reliably detects target twistlock positions around a container in a real wharf environment, autonomously navigates to these positions, classifies and localizes target twistlocks, and uses two onboard manipulators to cone or decone the twistlocks using hybrid grippers. This system represents a significant step towards automating coning and deconing on the wharf side, optimizing workflow, improving safety, and achieving competitive cycle times.
This paper presents the design and experimental validation of a Model Predictive Control (MPC) framework for trajectory tracking of a quadrotor in dynamic environments. The proposed strategy combines a predictive control formulation with an obstacle avoidance mechanism that linearizes collision constraints at each step of the prediction horizon using time-varying tangent planes. To enhance robustness and maintain solver feasibility in complex scenarios, the optimization problem incorporates soft constraints utilizing slack variables, allowing temporary constraint violations at a high penalty cost. The controller is implemented in a real-time system using ROS and validated in experiments with a quadrotor, whose state is provided by a motion capture system. Validation scenarios involving multiple static and dynamic obstacles demonstrate the effectiveness of the proposed approach for safe and reliable operation in dynamic environments.
Robotic leg prostheses have proven effective in reproducing the biomechanical functions of a missing limb. However, the actuators used to perform movements are typically large and heavy, limiting the device's performance and usability. In this work, we present the design of a lightweight and compact actuator for the knee joint of a robotic leg prosthesis. The actuator employs a frameless brushless motor with a compact cycloidal gearhead at its core. The actuator is capable of delivering up to 81 Nm of torque and 160 rpm of rotation. The final prototype weighs 1.31 kg and is 95 mm in diameter and 64 mm in length. In the next steps of the project, the actuator will be manufactured and integrated into the UFES Leg, a robotic leg prosthesis.
This work presents an innovative approach for smooth trajectory generation in robotic manipulators, using a hyperbolic tangent function whose slope is dynamically modulated by an inverted Gaussian profile. This formulation enables the construction of S-type motion profiles with third-order kinematic continuity (C3), ensuring that position, velocity, acceleration, and jerk are continuous. In particular, the jerk starts and ends at zero, a condition that eliminates residual vibrations and prevents abrupt dynamic excitations when initiating or completing a movement. This guarantees smooth boundary conditions while optimizing both agility and smoothness, without the complexity of high-degree polynomials or conventional segmentations. Validation through simulations in MATLAB/Simulink on a 3-degree-of-freedom manipulator demonstrated that the strategy effectively mitigates jerk. Key results include a JRMS value of 0.548, indicating a significant reduction in vibrations, with a balanced dynamic response. Compared to classical profiles such as the sigmoid or standard tanh, this adaptive approach exhibited superior control and enhanced dynamic continuity, even in multipoint trajectories in Cartesian space, establishing itself as a robust and efficient alternative.
