Object reconstruction and inspection tasks play a crucial role in various robotics applications. Identifying paths that reveal the most unknown areas of the object is paramount in this context, as it directly affects reconstruction efficiency. This problem is known as the view path planning problem. Current methods often use sampling-based path planning techniques, evaluating potential views along the path to enhance reconstruction performance. However, these methods are computationally expensive as they require evaluating several candidate views on the path. To this end, we propose a computationally efficient solution that relies on calculating a focus point in the most informative (unknown) region and having the robot maintain this point in the camera field of view along the path. In this way, object reconstruction-related information is incorporated into the whole-body control of a mobile manipulator employing a visibility constraint without the need for an additional path planner. We conducted comprehensive and realistic simulations using a large dataset of 114 diverse objects of varying sizes from 57 categories to compare our method with a sampling-based planning strategy and a strategy that does not employ informative paths using Bayesian data analysis. Furthermore, to demonstrate the applicability and generality of the proposed approach, we conducted real-world experiments with an 8-DoF omnidirectional mobile manipulator and a legged manipulator. Our results suggest that, when compared to a sampling based strategy, there is no statistically significant difference in object reconstruction entropy, and there is a 52.3% probability that they are practically equivalent in terms of coverage. In contrast, our method is 6.2 to 19.36 times faster in terms of computation time and reduces the total time the robot spends between views by 13.76% to 27.9%, depending on the camera field of view and model resolution. When compared with strategies that do not exploit informative paths, our method improves, on average, coverage by 4.9% and entropy by 9.72% at the expense of spending 8.72% more time in the reconstruction process.

Aquatic robots require an accurate and reliable localization system to navigate autonomously and perform practical missions. Kalman filters (KFs) and their variants are typically used in aquatic robots to combine sensor data. The two critical drawbacks of KFs are the requirement for skilled tuning of several filter parameters and the fact that changes to how the Inertial Measurement Unit (IMU) is oriented necessitate modifying the filter. To overcome those problems, this paper presents a novel method of fusing sensor data from a Doppler Velocity Log (DVL) and IMU using an adaptive nonlinear estimator to provide dead reckoning localization for a small autonomous surface vehicle. The proposed method has only one insensitive tuning parameter and is agnostic to the configuration of the IMU. The system was validated using a small ASV in a 2.4×3.6×2.4 m water tank, with a motion capture system as ground truth, and was evaluated against a state-of-the-art method based on KFs. Experiments showed that the average drift error of the nonlinear filter was 0.16 m (s.d. 0.06 m) compared to 0.15 m (s.d. 0.05 m) for the state of the art, meaning that the benefits in terms of tuning and flexible configuration do not come at the expense of performance.

Vision-based perception has become prevalent in robotic applications, especially in those where the control loop relies on visual data, such as visual servoing. For those applications, ensuring that the features or target object remain visible to the camera is critical, necessitating visibility-aware control. In this paper, we propose a method to guarantee the visibility of a dynamic object using a constrained kinematic controller and Vector Field Inequalities (VFIs) to include a linear visibility constraint. Unlike existing methods, we introduce constraints into the kinematic controller to ensure the target's visibility without needing a trajectory optimizer or local planner. Our method maintains the target object in the camera field of view (FoV) by representing the FoV with four infinite planes and maintaining the distance between the target object and each plane higher than a predefined distance. We evaluated the proposed approach using a mobile manipulator in two simulations involving cluttered environments: the first scenario involves a stationary target object, whereas the second scenario presents a more challenging workspace involving a moving target. Our results demonstrate that the proposed approach successfully maintains the target within the FoV while avoiding obstacles in the workspace, showing the potential of our method to improve the safety and reliability of visual-servoing-based robotic systems.

This paper proposes a task planning and motion control framework that generates task plans for a linear temporal logic specification (LTL), which are then executed using a task-space constrained motion controller and a local task planner that overcomes local minima. We propose a new encoding for task specifications, directly in the task-space, as constraints of a mixed-integer linear program that can be used with off-the-shelf LTL linear encoding. We apply our framework to plan and execute trajectories for a free-flying robot and show that the task plan is accomplished without collisions, even in the presence of unexpected moving obstacles that are not considered in the planning phase, while control signal constraints are satisfied. To evaluate the local minima avoidance, we compare the local task planner with a sampling-based motion planner, and the results show a smoother trajectory with a faster execution and less total planning time when using our framework. Last, our framework scaled well with a longer LTL specification, as opposed to automata-based frameworks that usually suffer with the curse of the dimensionality.

Reconfigurable mobile robots are well suited for inspection tasks in legacy nuclear facilities where access is restricted and the environment is often cluttered. A reconfig-urable snake robot, MIRRAX, has previously been developed to investigate such facilities. The joints used for the robot's reconfiguration introduce additional constraints on the robot's control, such as balance, on top of the existing actuator and collision constraints. This paper presents a set-point controller for MIRRAX using vector-field inequalities to enforce hard constraints on the robot's balance, actuator limits, and collision avoidance in a single quadratic programming formulation. The controller has been evaluated in simulation and early experiments in some scenarios. The results show that the controller generates feasible control inputs that enable the robot to retain its balance while moving with less oscillation and operating within the actuation and collision constraints.

Recent advancements in constrained kinematic control make it an attractive strategy for controlling robots with arbitrary geometry in challenging tasks. Most of the current works assume that the robot kinematic model is precise enough for the task at hand. However, with increasing demands and safety requirements in robotic applications, there is a need for a controller that compensates online for kinematic inaccuracies. We propose an adaptive constrained kinematic control strategy based on quadratic programming, which uses partial or complete task-space measurements to compensate online for calibration errors. Our method is validated in experiments that show increased accuracy and safety compared to a state-of-the-art kinematic control strategy.

This article presents a new kinematic control paradigm for redundant robots based on optimization. The general approach takes into account convex objective functions with inequality constraints and a specific equality constraint resulting from a Lyapunov function, which ensures closed-loop stability by design. Furthermore, we tackle an important particular case by using a convex combination of quadratic and l1-norm objective functions, making possible for the designer to choose different degrees of sparseness and smoothness in the control inputs. We provide a pseudoanalytical solution to this optimization problem and validate the approach by controlling the center of mass of the humanoid robot HOAP3.

In adult laparoscopy, robot-aided surgery is a reality in thousands of operating rooms worldwide, owing to the increased dexterity provided by the robotic tools. Many robots and robot control techniques have been developed to aid in more challenging scenarios, such as pediatric surgery and microsurgery. However, the prevalence of case-specific solutions, particularly those focused on non-redundant robots, reduces the reproducibility of the initial results in more challenging scenarios. In this paper, we propose a general framework for the control of surgical robotics in constrained workspaces under teleoperation, regardless of the robot geometry. Our technique is divided into a slave-side constrained optimization algorithm, which provides virtual fixtures, and with Cartesian impedance on the master side to provide force feedback. Experiments with two robotic systems, one redundant and one non-redundant, show that smooth teleoperation can be achieved in adult laparoscopy and infant surgery.

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still under-represented in procedures with constrained workspaces, such as deep brain neurosurgery and endonasal surgery. In these procedures, surgeons have restricted vision to areas near the surgical tooltips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings. In this paper, our vector-field-inequalities method is extended to provide dynamic active-constraints to any number of robots and moving objects sharing the same workspace. The method is evaluated with experiments and simulations in which robot tools have to avoid collisions autonomously and in real-time, in a constrained endonasal surgical environment. Simulations show that with our method the combined trajectory error of two robotic systems is optimal. Experiments using a real robotic system show that the method can autonomously prevent collisions between the moving robots themselves and between the robots and the environment. Moreover, the framework is also successfully verified under teleoperation with tool-tissue interactions.

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but is still underrepresented in deep brain neurosurgery and endonasal surgery where the workspace is constrained. In these conditions, the vision of surgeons is restricted to areas near the surgical tool tips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings, in particular in areas outside the surgical field-of-view. Active constraints can be used to prevent the tools from entering restricted zones and thus avoid collisions. In this paper, a vector field inequality is proposed that guarantees that tools do not enter restricted zones. Moreover, in contrast with early techniques, the proposed method limits the tool approach velocity in the direction of the forbidden zone boundary, guaranteeing a smooth behavior and that tangential velocities will not be disturbed. The proposed method is evaluated in simulations featuring two eight degrees-of-freedom manipulators that were custom-designed for deep neurosurgery. The results show that both manipulator-manipulator and manipulator-boundary collisions can be avoided using the vector field inequalities.

This paper introduces the Manipulation Task Model (MTM), an algebraic approach to describe manipulation tasks for robotic systems. The model is based on dual quaternion algebra to describe poses, linear and angular velocities, and forces and moments, all of which are joined to form the algebraic group of actions. We then use this group together with process algebra to describe temporal and logical relations between actions composing manipulation tasks. A simulation example of a cleaner robot shows that the proposed model is comprehensive and expressive enough, and that it provides robustness in relation to perturbations at the task level. Future developments will include merging the model with underlying controllers in order to provide a unified means for description, planning, and execution of manipulation tasks for robotic systems.

This work addresses the task-space design problem of a linear-quadratic optimal tracking controller for robotic manipulators using the unit dual quaternion formalism. The efficiency, compactness, and lack of singularity of the representation render the unit dual quaternion a suitable framework for simultaneously describing the attitude and the position of the end-effector. Motivated by the advantages of this kinematic description, we propose a new task-space linear-quadratic optimal tracking controller in order to find an optimal trajectory for the end-effector, providing a tool to balance more conveniently the end-effector error and its task-space velocity. This is possible because the kinematic control problem using the dual quaternion transformation invariant error can be reduced to an affine time-varying system. The proposed optimal tracking controller allows the compensation of trajectory induced disturbances, as well as other modeled additive disturbances and known bias. Simulation results with different design parameters provide a performance overview, in comparison with standard kinematic controllers with and without a feed-forward term, for tracking a desired reference.

This paper presents a new strategy for task space control in the cooperative manipulation framework. We extend the cooperative dual task-space (CDTS) - which uses dual quaternions to represent the bimanual manipulation-to explicitly regard self-motion dynamics that arise from redundant kinematics. In this sense, we propose a flexible task execution criterion that enriches the Jacobian null space with additional degrees of freedom by relaxing control requirements upon specific geometric task objectives. The criterion is satisfied with a hysteresis-based switching strategy that ensures stability and convergence upon traditional and relaxed constraints. Simulation results highlight the importance and effectiveness of the proposed technique.

This paper addresses the H∞robust control problem for robot manipulators using unit dual quaternion representation, which allows an utter description of the end-effector transformation without decoupling rotational and translational dynamics. We propose three different H∞control criteria that ensure asymptotic convergence, whereas reducing the influence of disturbances upon the system stability. Also, with a new metric of dual quaternion error in SE(3) we prove independence from robot coordinate changes. Simulation results highlight the importance and effectiveness of the proposed approach in terms of performance, robustness, and energy efficiency.

This paper presents a semi-automatic system for robotically assisted 2D needle steering that uses duty-cycling to perform insertions with arcs of adjustable curvature radius. It combines image feedback manually provided by an operator with an adaptive path planning strategy to compensate for system uncertainties and changes in the workspace during the procedure. Experimental results are presented to validate the proposed platform.

In this paper we propose a novel approach for interactive manipulation involving a human and a humanoid. The interaction is represented by means of the relative configuration between the human's and the robot's hands. Based on this principle and a set of mathematical tools also proposed in the paper, a large set of tasks can be represented intuitively. We also introduce the concept of simultaneous handling using mirrored movements, where the human controls the robot and simultaneously interacts with it by means of a common manipulated object. Illustrative experiments are performed to validate the proposed techniques.

In this paper we present a novel approach in human/robot collaboration, where the robot controls not only its arm but also the human's by means of functional electrical stimulation (FES). The task is described by using the cooperative dual task-space approach, providing a considerable degree of invariance with respect to the morphology of the agents involved. Experimental results in a “ball in the hoop” task using healthy blindfolded subjects show the validity of the approach and encourages further experiments with impaired subjects, for instance hemiplegic or quadriplegic patients.

This paper presents an adaptive approach for 2D motion planning of steerable needles. It combines duty-cycled rotation of the needle with the classic Rapidly-Exploring Random Tree (RRT) algorithm to obtain fast calculation of feasible trajectories. The motion planning is used intraoperatively at each cycle to compensate for system uncertainties and perturbations. Simulation results demonstrate the performance of the proposed motion planner on a workspace based on ultrasound images.

We propose a set of control strategies for performing two arm manipulation with the goal of simplifying the task definition. In order to develop these strategies we propose a new representation, derived from the cooperative task-space, in the dual quaternion domain. The result is a compact and “singularity free” representation for two arm systems, named cooperative dual task-space. All the proposed control strategies share the same general scheme and are derived by using an analytical approach. Moreover, the mathematical treatment is given in a coherent and systematic fashion, and thus other strategies may be derived using the same argument. Experimental results show the effectiveness and usefulness of the cooperative dual task-space framework and the proposed control strategies.

We propose in this paper a new concept of unified position/orientation control of robot manipulator by describing the end-effector motion as a dual quaternion involving both translation and rotation. The development of the forward kinematic model and Jacobian matrix in dual quaternion space is detailed as well as the stability of the controller. At last, simulation and experimental results highlight the efficiency and performance of this controller.

This paper presents a new path planning algorithm that uses adaptive random walks to incrementally construct a roadmap in the robot's free configuration space. This algorithm, named Incremental Adaptive Random Walks (iARW), uses a modified version of the ARW algorithm proposed by Carpin and Pillonetto for exploring the configuration space and storing the discovered path in a roadmap. Thus, the main idea is to use bidirectional adaptive random walks to explore the configuration space but also to use and expand the roadmap whenever possible.With this approach it is possible to construct a roadmap that captures the connectivity of the free configuration space without a preprocessing phase. A comparison of our approach with other state of the art path planners illustrates the good performance of the proposed method.