Dynamic Active Constraints Research Articles

Caring About the Human Operator: Haptic Shared Control for Enhanced User Comfort in Robotic Telemanipulation.

Haptic shared control enables a human operator and an autonomous controller to share the control of a robotic system using haptic active constraints. It has been used in robotic teleoperation for different purposes, such as navigating along paths minimizing the torques requested to the manipulator or avoiding possibly dangerous areas of the workspace. However, few works have focused on using these ideas to account for the user's comfort. In this article, we present an innovative haptic-enabled shared control approach aimed at minimizing the user's workload during a teleoperated manipulation task. Using an inverse kinematic model of the human arm and the rapid upper limb assessment (RULA) metric, the proposed approach estimates the current user's comfort online. From this measure and an a priori knowledge of the task, we then generate dynamic active constraints guiding the users towards a successful completion of the task, along directions that improve their posture and increase their comfort. Studies with human subjects show the effectiveness of the proposed approach, yielding a 30% perceived reduction of the workload with respect to using standard guided human-in-the-loop teleoperation.

Dynamic Active Constraints for Surgical Robots Using Vector-Field Inequalities

Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still underrepresented 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 work, 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.

Deformation invariant bounding spheres for dynamic active constraints in surgery

Active constraints are collaborative robot control strategies, which can be used to guide a surgeon or protect delicate tissue structures during robot-assisted surgery. Tissue structures of interest often move and deform throughout a surgical intervention, and therefore, dynamic active constraints, which adapt and conform to these changes, are required. A fundamental element of an active constraint controller is the computation of the geometric relationship between the constraint geometry and the surgical instrument. For a static active constraint, there are a variety of computationally efficient methods for computing this relative configuration; however, for a dynamic active constraint, it becomes significantly more challenging. Deformation invariant bounding spheres are a novel bounding volume formulation, which can be used within a hierarchy to allow efficient proximity queries within dynamic active constraints. These bounding spheres are constructed in such a way that as the surface deforms, they do not require time-consuming rebuilds or updates, rather they are implicitly updated and continue to represent the underlying geometry as it changes. Experimental results show that performing proximity queries with deformation invariant bounding sphere hierarchies is faster than common methods from the literature when the deformation rate is within the range expected from conventional imaging systems.

Dimensionality Reduction in Controlling Articulated Snake Robot for Endoscopy Under Dynamic Active Constraints.

This paper presents a real-time control framework for a snake robot with hyper-kinematic redundancy under dynamic active constraints for minimally invasive surgery. A proximity query (PQ) formulation is proposed to compute the deviation of the robot motion from predefined anatomical constraints. The proposed method is generic and can be applied to any snake robot represented as a set of control vertices. The proposed PQ formulation is implemented on a graphic processing unit, allowing for fast updates over 1 kHz. We also demonstrate that the robot joint space can be characterized into lower dimensional space for smooth articulation. A novel motion parameterization scheme in polar coordinates is proposed to describe the transition of motion, thus allowing for direct manual control of the robot using standard interface devices with limited degrees of freedom. Under the proposed framework, the correct alignment between the visual and motor axes is ensured, and haptic guidance is provided to prevent excessive force applied to the tissue by the robot body. A resistance force is further incorporated to enhance smooth pursuit movement matched to the dynamic response and actuation limit of the robot. To demonstrate the practical value of the proposed platform with enhanced ergonomic control, detailed quantitative performance evaluation was conducted on a group of subjects performing simulated intraluminal and intracavity endoscopic tasks.

Context specific descriptors for tracking deforming tissue

In minimally invasive surgery, deployment of motion compensation, dynamic active constraints and adaptive intra-operative guidance require accurate estimation of deforming tissue in 3D. To this end, the use of vision-based techniques is advantageous in that it does not require the integration of additional hardware to the existing surgical settings. Deformation can be recovered by tracking features on the surface of the tissue. Existing methods are mostly based on ad hoc machine vision techniques that have generally been developed for rigid scenes or use pre-determined models with parameters fine tuned to specific surgical settings. In this work, we propose a novel tracking technique based on a context specific feature descriptor. The descriptor can adapt to its surroundings and identify the most discriminate information for tracking. The feature descriptor is represented as a decision tree and the tracking process is formulated as a classification problem for which log likelihood ratios are used to improve classifier training. A recursive tracking algorithm obtains examples of tissue deformation used to train the classifier. Additional training data is generated by geometric and appearance modelling. Experimental results have shown that the proposed context specific descriptor is robust to drift, occlusion, and changes in orientation and scale. The performance of the algorithm is compared with existing tracking algorithms and validated with both simulated and in vivo datasets.

Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization

This paper presents an improved quantum-behaved particle swarm optimization (IQPSO) for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing emission cost. In this paper, quantum-behaved particle swarm optimization is improved employing heuristic strategies in order to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also devised to handle the reservoir storage volumes constraints. To show feasibility and effectiveness of the proposed method, different case studies, such as economic load scheduling (ELS), economic emission scheduling (EES) and combined economic emission scheduling (CEES) in hydrothermal scheduling, are carried out and the test results are compared with those of other methods reported in the literature. It is also very important to note that the proposed method is capable of yielding higher-quality solutions while strictly satisfying all constraints of the test system.

An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

From medical images to minimally invasive intervention: Computer assistance for robotic surgery

Minimally invasive surgery has been established as an important way forward in surgery for reducing patient trauma and hospitalization with improved prognosis. The introduction of robotic assistance enhances the manual dexterity and accuracy of instrument manipulation. Further development of the field in using pre- and intra-operative imaging guidance requires the integration of the general anatomy of the patient with clear pathologic indications and geometrical information for preoperative planning and intra-operative manipulation. It also requires effective visualization and the recreation of haptic and tactile sensing with dynamic active constraints to improve consistency and safety of the surgical procedures. This paper describes key technical considerations of tissue deformation tracking, 3D reconstruction, subject-specific modeling, image guidance and augmented reality for robotic assisted minimally invasive surgery. It highlights the importance of adapting preoperative surgical planning according to intra-operative data and illustrates how dynamic information such as tissue deformation can be incorporated into the surgical navigation framework. Some of the recent trends are discussed in terms of instrument design and the usage of dynamic active constraints and human–robot perceptual docking for robotic assisted minimally invasive surgery.