Tendon-driven continuum robots (TDCRs) face a critical trade-off between energy efficiency and static performance for navigating constrained environments, a challenge in medical and industrial applications. This study proposes a bi-objective optimization framework to enhance tendon placement and dimensional synthesis in a two-segment TDCR, featuring seven disks and four tendons per segment. Leveraging a kineto-static model based on piecewise constant curvature (PCC) theory and a multi-objective genetic algorithm (MOGA), radial tendon distances and angular offsets have been optimized. These solutions achieve up to 30% reduction in mechanical work and a 3-5% workspace expansion, validated through 100 randomized tendon force samples. The results offer practical guidelines for improving TDCR performance in both minimally invasive surgery and industrial inspection.

Tendon-driven continuum robots (TDCRs) are increasingly used in minimally invasive settings due to their compliance and dexterity, yet quantifying their effective workspace remains challenging because inverse kinematics is computationally expensive and sensitive to pose constraints. This paper presents a voxel-based capability mapping framework for a custom-built, two-segment TDCR with antagonistic tendon actuation and a non-interference routing sleeve around the pulley region. The method samples task-space targets within a discretized volume and enforces a downward tip-orientation constraint to reflect surgical operation requirements. For each sampled target pose, a particle swarm optimization (PSO) based inverse kinematics solver estimates a feasible configuration, and the resulting position and orientation errors are evaluated against predefined tolerances. Capability is then summarized per voxel as a success ratio over multiple randomized trials, producing a compact map that highlights regions with higher reliability for task execution. The resulting capability map provides practical insight for target selection and procedure planning while avoiding repeated online inverse-kinematics calls.

Tendon-driven continuum robots (TDCRs) have been widely investigated for minimally invasive surgery, inspection, and cluttered-environment operation because of their compliance and dexterity. However, dense workspace characterization and configuration selection remain challenging because inverse kinematics must be solved in a highly redundant, nonlinear space under tendon-actuation constraints. Existing capability map frameworks do not directly address configuration-level tendon feasibility or dexterity-aware selection among feasible alternatives. This paper presents a database-driven CAP framework that combines piecewise constant curvature kinematics with particle swarm optimization to construct an offline library of configurations consistent with the prototype tendon-routing and command-mapping model over 180 workspace voxels and 54,000 voxel-orientation samples. For each feasible sample, pose residuals and Jacobian-based dexterity descriptors are computed offline and stored in the database. At runtime, the method retrieves feasible entries for a queried voxel and ranks them through a Pareto-based trade-off between combined pose residual and inverse manipulability. The output is a dexterity-aware representative configuration for the queried local neighbourhood, rather than a newly optimized exact-pose solution. Motion-capture validation on a custom two-segment TDCR shows millimetre-order position errors in the evaluated ROI.

Fault-tolerant control is essential for surgical continuum robots, where actuator and sensor faults, time-varying delays, input saturation, disturbances, and nonlinearities can rapidly degrade accuracy and safety. This paper proposes a prescribed-time fault-tolerant framework that guarantees convergence within a user-defined settling time, independent of initial conditions, under stated assumptions. A descriptor-form observer simultaneously estimates sensor delays and fault signals, while a deep neural network (DNN) approximates unmodeled dynamics. Adaptive laws compensate for disturbances and approximation errors, and a time-base generator enforces prescribed-time convergence of the estimation errors. Building on these estimates, a resilient near-optimal controller is synthesized, whose residual term is learned online by a DNN via composite learning that combines tracking and observer errors. An auxiliary compensator mitigates input-delay and saturation effects. Lyapunov analysis provides sufficient LMI conditions for prescribed-time closed-loop stabilization. Simulations on a tendon-driven continuum robot under simultaneous actuator and sensor faults, time-varying delays, and saturation demonstrate superior tracking accuracy, faster convergence, and lower control effort compared with a representative robust baseline, highlighting the framework’s strong potential for safe and precise surgical manipulation.

Recent advances in mobile robotics have emphasized the need for systems capable of operating in unstructured environments, combining obstacle negotiation, stability, and adaptability. This study presents the preliminary design and testing of Brush.Q, an articulated ground robot featuring a novel structure distinct from existing wheel-legged robots, equipped with compliant brush-like wheels composed of multiple spokes. The main contribution is the experimental analysis of suspension capability across different wheel geometric profiles, combined with the assessment of obstacle-climbing performance. A simplified prototype was constructed to evaluate the effects of wheel rotation direction, spoke number, and spoke tapering. Results show that reducing the number of spokes improves obstacle-climbing at the expense of suspension, while higher spoke count and compliant geometry enhance suspension and stability. Spoke tapering improves obstacle climbing in the backward-facing configuration but consistently reduces suspension. Overall, these findings highlight the critical role of wheel geometry and the potential for reconfigurable spoked wheels to enhance adaptability and versatility in unstructured terrains.

This paper introduces a novel approach to the prescribed-time control of continuum surgical robots, focusing on four key areas: enhanced system safety, tailored transient tracking, steady-state tracking enhancement, and optimal learned control. The main contribution is the application of system state constraints on tracking error, transforming these constraints into an unconstrained problem using a monotone tube boundary. This method avoids the complexity of Model Predictive Control (MPC) and Control Barrier Functions (CBF) techniques, as well as the conservatism and fixed-boundary issues associated with the Barrier Lyapunov Function (BLF) method. By using a monotone tube boundary, the approach allows for the pre-assignment of transient characteristics for tracking error, avoiding excessive overshoot and lack of adjustability seen with the Prescribed Performance Function (PPF). The prescribed-time control philosophy enables pre-determination of settling time, enhancing precision and convergence rates essential for surgical applications. Additionally, an optimized prescribed-time control strategy using an actor-critic neural network-based Reinforcement Learning (RL) approach ensures controller optimality, reducing control effort, power consumption, and heat generation in the robot's actuators. The method adapts to dynamic environments, ensuring robust performance in various surgical scenarios. Simulation results on a two-segment continuum robot demonstrate the proposed method's advantages over state-of-the-art techniques.

This paper focuses on the optimal design of a tendon-driven continuum robot (TDCR) based on its feasible static workspace (FSW). The TDCR under consideration is a two-segment robot driven by eight tendons, with four tendon actuators per segment. Tendon forces are treated as design variables, while the feasible static workspace (FSW) serves as the optimization objective. To determine the robot’s feasible static workspace, a genetic algorithm optimization approach is employed to maximize a Euclidean norm of the TDCR’s tip position over the workspace. During the simulations, the robot is subjected to external loads, including torques and forces. The results demonstrate the effectiveness of the proposed method in identifying optimal tendon forces to maximize the feasible static workspace, even under the influence of external forces and torques.

For individuals with limited hand mobility due to injury or surgery, active hand-rehabilitation is critical for regaining range of motion, flexibility, and strength. This study presents a novel wearable glove designed to enhance active hand rehabilitation therapy. The wearable glove, fabricated using three-dimensional printing (3D printing) with flexible thermoplastic polyurethane (TPU 60) and durable tough polylactic acid (tough PLA), features adjustable inflatable chambers targeting the torque applied to metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints. These chambers enable personalized resistance training by allowing therapists to adjust air pressure within the chambers. The variation of stiffness and resistance force exerted by the chambers, as well as torsional stiffness at the MCP and PIP joints during finger bending, are investigated. This research contributes to the development of pressure-based personalized resistance training for hand rehabilitation. Results indicate that the adjustable chambers in the wearable device offer a promising approach to enhance hand rehabilitation.

Work related musculo-skeletal disorders represent a relevant percentage of occupational diseases in developed countries. Aside the laborers discomfort, they also reflect on companies with relevant economic effects. Nonetheless, the spread of exoskeletons for industrial applications is still limited. The reasons are to be sought on how they are perceived by workers: for many of them, their reduced mobility and not perfect adaptability to the kinematics of the human body make them more of a nuisance than a real help. The spread of wearable devices in industrial scenarios could bring advantages not only to jobs ergonomics and injuries assessment, but also to safety and risk management. The exoskeletons worn by labourers can be conceived as tools able to share quantitative data about the user posture and position within the working environment for active safety strategies and machines command. The object of the eXoft project is an active exoskeleton with innovative mechanical and actuation designs, conceived for integration in collaborative robotics environment. The purpose is to improve the comfort and safety of labourers tasked with heavy or repetitive jobs, while providing a tool for a safer integration of human-in-the-loop workflows.

The use of robotic technologies for caregiving and assistance has become a very interesting research topic in the field of robotics. Towards this goal, the researchers at Politecnico di Torino are developing robotic solutions for indoor assistance. This paper presents the D.O.T. PAQUITOP project, which aims at developing a mobile robotic assistant for the hospital environment. The mobile robot is composed of a custom omnidirectional platform, named PAQUITOP, a commercial 6 dof robotic arm, sensors for monitoring vital signs in patients, and a tablet to interact with the patient. To prove the effectiveness of this solution, preliminary tests were conducted with success in the laboratories of Politecnico di Torino and, thanks to the collaboration with the Onlus Fondazione D.O.T. and the medical staff of Molinette Hospital in Turin (Italy), at the hematology ward of Molinette Hospital.

The paper presents a novel soft fingertip for soft robotics application in greenhouses and protected cultivation. The system is designed to be easily adapted to different commercial end-effectors while not compromising their functionalities. The use of auxiliary vessels with combined air and liquid presence allows the adjustment of the fingertip stiffness to guarantee different values of exerted contact force and stiffness when compressed. A simplified design method to evaluate the influence of the membrane shape and the set-up parameters on the system behavior is reported. The method was compared with experimental measurements on a first system prototype.

In this paper, the effects of wheel slip compensation in trajectory planning for mobile tractor-trailer robot applications are investigated. Firstly, a kinematic model of the proposed robot architecture is marked out, then an experimental campaign is done to identify if it is possible to kinematically compensate trajectories that otherwise would be subject to large lateral slip. Due to the close connection to the experimental data, the results shown are valid only for Epi.q, the prototype that is the main object of this manuscript. Nonetheless, the base concept can be usefully applied to any mobile robot subject to large lateral slip.

The Agri_q is an electric unmanned ground vehicle specifically designed for precision agriculture applications. Since it is expected to traverse on unstructured terrain, especially uneven terrain, or to climb obstacles or slopes, an eight-wheeled locomotion layout, with each pair of wheels supported by a bogie, has been chosen. The wide contact surface between the vehicle and the ground ensures a convenient weight distribution; furthermore, the bogie acts like a filter with respect to ground irregularities, reducing the transmissibility of the oscillations. Nevertheless, this locomotion layout entails a substantial lateral slithering along curved trajectories, which results in an increase of the needed driving torque. Therefore, reducing the number of ground contact points to compare the torque adsorption in different configurations, namely four, six, or eight wheels, could be of interest. This paper presents a reconfiguration mechanism able to modify the Agri_q locomotion layout by lifting one of the two wheels carried by the bogie and to activate, at the same time, a suspension device. The kinematic synthesis of the mechanism and the dynamic characteristics of the Agri_q suspended front module are presented.

In this paper, an innovative mobile and sustainable robot for precision agriculture, named "Agri.q", is presented. Characterized by a peculiar mechanical architecture and provided with specific sensors and tools, the Agri.q is able to operate in unstructured agricultural environments in order to fulfill several tasks as mapping, monitoring, and manipulating or collecting small soil and leaf samples. In addition, the rover is equipped with a top platform covered with solar panels, whose orientation can be exploited both to maximize the sunrays collection during the auto-charging phase and to permit a drone landing over a horizontal surface, regardless of the ground inclination. A particular attention to energy consumptions and sustainability has driven the mechanical design of the Agri.q powertrain: the weight reduction results into a limited number of small size locomotion motors, enhancing the importance of the harvested solar energy on the energy balance of the whole system. In this paper, all these characteristics are described and analyzed in detail. Moreover, some preliminary tests aimed at evaluating the energetic behaviour of the rover under different working and weather conditions are presented.

Obesity is known to be growing worldwide. The World Health Organization (WHO) reports that obesity has tripled since 1975. In 2016, 39% of adults over 18 years old were overweight, and 13% were obese. Obesity is mostly preventable by adopting lifestyle improvements, enhancing diet quality, and doing physical exercise. The workload of the physical exercises should be proportionate to the patient’s capabilities. However, it must be considered that obese people are not used to training; they may not endure physical exertion and, even more critically, they could have some psychological impediments to the workouts. Physical exercises and equipment must, therefore, guarantee comfort and prevent situations in which the bariatric individual may feel inadequate. For these reasons, this study aims to design an innovative system to approach simple physical activities, like leg and arm exercises, to bariatric users to enable them to recover mobility and muscle tone gradually. The leading feature of this architecture is the design of hidden exercise mechanisms to overcome the psychological barriers of the users toward these kinds of machines. This paper proposes the initial design of the main sub-systems composing the rehabilitation machine, namely the leg curl and leg extension mechanism and its control architecture, the upper body exercises system, and a series of regulation mechanisms required to accommodate a wide range of users. The proposed functional design will then lead to the development of a prototype to validate the machine.

In this paper, an innovative UGV (Unmanned Ground Vehicle), named Agri.q, is presented. The rover is specifically designed for precision agriculture applications and is able to work in unstructured environment on irregular soil, cooperating with drones, if necessary. It is equipped with specific tools and sensors to per-form specific tasks, i.e. mapping the field, monitoring the crops and collecting soil and leaf samples. In addition, it is provided with a two degrees of freedom landing platform able to self-orient to ensure a safe drone docking or even to maximize the sunrays collection during the auto-charging phase. In this way, the rover autonomy and sustainability are increased. The functional design of the rover and the design of its actuation system are reported herein; furthermore, the first prototype is described and some preliminary results obtained during experimental tests are discussed.

This paper deals with the design of a mechanism able to modify the locomotion layout of a novel UGV (Unmanned Ground Vehicle), named Agri_q, specifically designed for precision agriculture applications. Taking into account that it has to move over an irregular and loose soil, the rover is equipped with eight wheels, each couple supported by a rocker, which ensure a wide contact surface between the vehicle and the ground. A limit of this solution is the substantial lateral slithering occurring when the rover engages curved trajectories, which causes an increase of the needed driving torque. Therefore, reducing the number of ground contact points to compare the torque adsorption in different configurations, namely four, six or eight wheels, could be of interest. The designed mechanism has to lift one of the two wheels connected to the rocker providing at the same time a suspension system to reduce the vibrations transmitted to the sub-chassis. Having analyzed the system requirements, two possible functional solutions are proposed and compared. Finally, the synthesis of the chosen mechanism is presented.

In this paper, a novel UGV (unmanned ground vehicle) for precision agriculture, named “Agri.q,” is presented. The Agri.q has a multiple degrees of freedom positioning mechanism and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. In particular, the integration of a 7 DOFs (degrees of freedom) manipulator and a mobile frame results in a reconfigurable workspace, which opens to samples collection and inspection in non-structured environments. Moreover, Agri.q mounts an orientable landing platform for drones which is made of solar panels, enabling multi-robot strategies and solar power storage, with a view to sustainable energy. In fact, the device will assume a central role in a more complex automated system for agriculture, that includes the use of UAV (unmanned aerial vehicle) and UGV for coordinated field monitoring and servicing. The electronics of the device is also discussed, since Agri.q should be ready to send-receive data to move autonomously or to be remotely controlled by means of dedicated processing units and transmitter-receiver modules. This paper collects all these elements and shows the advances of the previous works, describing the design process of the mechatronic system and showing the realization phase, whose outcome is the physical prototype.

This paper deals with the design of a UGV (unmanned ground vehicle) for precise agriculture applications, named Agri.q02. The UGV can be considered the evolution of a previously presented vehicle, the Agri.q, with a novel layout and extended capabilities. Able to operate in unstructured environments and equipped with solar panels, the UGV permits a UAV (unmanned aerial vehicle) landing and recharging, even when positioned on irregular terrain or steep slopes. These features are provided by a positioning mechanism able to keep the landing platform horizontal above the soil; this mechanism was completely redesigned and its dimensional synthesis is presented herein. Finally, in order to collect ground or leaves' samples for crop monitoring purposes, the novel version of the rover is also equipped by a robotic collaborative arm with a gripper at its end.