Navigation Example Research Articles

The journey within: mental navigation as a novel framework for understanding psychotherapeutic transformation

BackgroundDespite the demonstrated efficacy of psychotherapy, the precise mechanisms that drive therapeutic transformations have posed a challenge and still remain unresolved. Here, we suggest a potential solution to this problem by introducing a framework based on the concept of mental navigation. It refers to our ability to navigate our cognitive space of thoughts, ideas, concepts, and memories, similar to how we navigate physical space. We start by analyzing the neural, cognitive, and experiential constituents intrinsic to mental navigation. Subsequently, we posit that the metaphoric spatial language we employ to articulate introspective experiences (e.g., “unexplored territory” or “going in circles”) serves as a robust marker of mental navigation.MethodsUsing large text corpora, we compared the utilization of spatial language between transcripts of psychotherapy sessions (≈ 12 M. words), casual everyday conversations (≈ 12 M. words), and fictional dialogues in movies (≈ 14 M. words). We also examined 110 psychotherapy transcripts qualitatively to discern patterns and dynamics associated with mental navigation.ResultsWe found a notable increase in the utilization of spatial metaphors during psychotherapy compared to casual everyday dialogues (U = 192.0, p = .001, d = 0.549) and fictional conversations (U = 211, p < .001, d = 0.792). In turn, analyzing the usage of non-spatial metaphors, we did not find significant differences between the three datasets (H = 0.682, p = 0.710). The qualitative analysis highlighted specific examples of mental navigation at play.ConclusionMental navigation might underlie the psychotherapy process and serve as a robust framework for understanding the transformative changes it brings about.

RCAviz: Exploratory search in multi-relational datasets represented using relational concept analysis

The conceptual structures built with Formal Concept Analysis (FCA) and its extensions are appropriate constructs for supporting Exploratory Search (ES). FCA indeed classifies a set of objects described by Boolean attributes in a concept lattice which is prone to (intra-lattice) navigation. Relational Concept Analysis (RCA), for its part, classifies several sets of objects connected through multiple binary relationships by using logical operators (quantifiers) which can be approximate. The output is a set of interconnected concept lattices, thus adding inter-lattice navigation opportunities. In this paper, we describe the web platform RCAviz, which aims to support such intra- and inter-lattice navigation. The user can select a subset of objects and attributes as a starting point for navigation. Then RCAviz shows the associated concept and its close intra- and inter-lattice neighbors. The user can access to the objects and attributes introduced and inherited in a concept. They then can navigate, i.e. zoom and pan the current view, and move from one concept to another. Additional views show the previous and the next conceptual structures, as well as an history which allows the user to browse its navigation. A navigation example is shown on a real dataset to illustrate the potential of RCAviz for ES.

Robust stutter bisimulation for abstraction and controller synthesis with disturbance

This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. To solve this problem, a finite-state abstraction of the original system is first constructed, and then a controller is synthesised for the abstraction. Due to the disturbances and uncertainty in the environment, future states cannot be predicted exactly, and the abstraction must take this into account. For this purpose, the robust stutter bisimulation relation is introduced, which preserves the existence of controllers for any given linear temporal logic formula that excludes the next operator. States are related by the robust stutter bisimulation relation if the same target sets can be guaranteed to be reached or avoided under control of some controller, thus ensuring that disturbances have similar effect on paths that start in related states. It is shown that there exists a controller enforcing a linear temporal logic formula for the original system if and only if a controller exists for the abstracted system. The approach is illustrated by a robot navigation example.

Convexifying State-Constrained Optimal Control Problem

This paper presents a method that convexifies state-constrained optimal control problems in the control-input space. The proposed method enables convex programming methods to find the globally optimal solution even if costs and control constraints are non-convex in control and convex in state, dynamics is non-affine in control and convex in state, and state constraints are convex in state. Under the above conditions, generic methods do not guarantee to find optimal solutions, but the proposed method does. The proposed approach is demonstrated in a sixteen-dimensional navigation example.

CSP2Turtle: Verified Turtle Robot Plans

Software verification is an important approach to establishing the reliability of critical systems. One important area of application is in the field of robotics, as robots take on more tasks in both day-to-day areas and highly specialised domains. Our particular interest is in checking the plans that robots are expected to follow to detect errors that would lead to unreliable behaviour. Python is a popular programming language in the robotics domain through the use of the Robot Operating System (ROS) and various other libraries. Python’s Turtle package provides a mobile agent, which we formally model here using Communicating Sequential Processes (CSP). Our interactive toolchain CSP2Turtle with CSP models and Python components enables plans for the turtle agent to be verified using the FDR model-checker before being executed in Python. This means that certain classes of errors can be avoided, providing a starting point for more detailed verification of Turtle programs and more complex robotic systems. We illustrate our approach with examples of robot navigation and obstacle avoidance in a 2D grid-world. We evaluate our approach and discuss future work, including how our approach could be scaled to larger systems.

Cavernous Malformations: What They Have Taught Us.

Cavernous Malformations: What They Have Taught Us.

Upper and Lower Bounds for End-to-End Risks in Stochastic Robot Navigation

We present an analytical method to estimate the collision probability of motion plans for autonomous agents with discrete-time dynamics operating under Gaussian motion and sensing uncertainty. Motion plans generated by planning algorithms cannot be perfectly executed by autonomous agents in reality due to the inherent uncertainties in the real world. Estimating end-to-end collision probability is crucial to characterize the safety of trajectories and plan risk-optimal trajectories. In this work, we derive upper and lower bounds for end-to-end collision probability of motion plans using results from probability theory. Using a ground robot navigation example, we demonstrate that our method is considerably faster than the naïve Monte Carlo sampling method and the proposed bounds are significantly less conservative than Boole's bound commonly used in the literature.

Model Predictive Control in Partially Observable Multi-Modal Discrete Environments

Autonomous systems operate in environments that can be observed only through noisy measurements. Thus, controllers should compute actions based on their beliefs about the surroundings. In these settings, we design a Model Predictive Controller (MPC) based on a continuous-state Linear Time-Invariant (LTI) system model operating in a discrete-state environment described by a Hidden Markov Model (HMM). Environment constraints are modeled as chance constraints and environment observations can be asynchronous with system state measurements and controller updates. We show how to approximate the solution of the MPC problem defined over the space of feedback policies by optimizing over a trajectory tree, where each branch is associated with an environment measurement. The proposed approach guarantees chance constraint satisfaction and recursive feasibility. Finally, we test the proposed strategy on navigation examples in partially observable environments, where the proposed MPC guarantees chance constraint satisfaction.

A Novel Federated Structure of Invariant EKF Using Left-/Right-Invariant Observations

In this article, we propose a novel federated structure of the invariant extended Kalman filter (IEKF) using the left- and right-invariant observations. A proper invariant error should be chosen to exploit the trajectory-independent property of the IEKF. For the left- and right-invariant observations, the left-IEKF (LIEKF) and the right-IEKF (RIEKF) are designed, respectively. However, in applications, both left- and right-invariant observations are often used together while using multiple aiding sensors to improve estimation performance. If either the LIEKF or RIEKF is selected and implemented as a centralized filter, the measurement matrix contains current state estimates in mismatched observation-related terms. As a result, as the state estimation error increases, the calculation of the Kalman gain is affected, and the filter cannot properly correct the error. For such situations, we propose a federated structure that updates the left- and right-invariant observations separately in the allocated subfilters. In the subfilter assigned with left-invariant observations, the error is corrected using the LIEKF, and in the subfilter assigned with right-invariant observations, the error is corrected using the RIEKF. Since each subfilter uses trajectory-independent Kalman gain, it is expected to improve the estimation performance compared to the centralized structure. The performance of the proposed method is evaluated and compared to the centralized LIEKF and RIEKF through Monte-Carlo simulations for an inertial navigation example. Also, the proposed method is validated by a publicly available dataset of a ground vehicle equipped with a global positioning system (GPS) and wheel encoders.

Modelling the Turtle Python library in CSP

Software verification is an important tool in establishing the reliability of critical systems. One potential area of application is in the field of robotics, as robots take on more tasks in both day-to-day areas and highly specialised domains. Robots are usually given a plan to follow, if there are errors in this plan the robot will not perform reliably. The capability to check plans for errors in advance could prevent this. Python is a popular programming language in the robotics domain, through the use of the Robot Operating System (ROS) and various other libraries. Python's Turtle package provides a mobile agent, which we formally model here using Communicating Sequential Processes (CSP). Our interactive toolchain CSP2Turtle with CSP model and Python components, enables Turtle plans to be verified in CSP before being executed in Python. This means that certain classes of errors can be avoided, and provides a starting point for more detailed verification of Turtle programs and more complex robotic systems. We illustrate our approach with examples of robot navigation and obstacle avoidance in a 2D grid-world.

Robust Linearly Constrained Invariant Filtering for a Class of Mismatched Nonlinear Systems

Standard state estimation techniques require a perfect knowledge of the system's model, that is, process and measurement equations, inputs and the corresponding noise statistics. In practice this assumption does not hold and therefore robust filtering methods must be accounted for. A new approach to tackle a potential model mismatch via linear constraints within the Kalman filter, and its extended version (EKF), has been recently shown to be a very promising solution. This letter further explores robust linearly constrained filtering in the context of the Invariant EKF (InEKF). With respect to the EKF, the InEKF is a recent filtering technique which has been shown to better handle the particular structure of a class of problems through the use of Lie groups. In this contribution, a new robust linearly constrained InEKF is introduced, together with particular parametric mismatched models and mitigation strategies through linear constraints. Numerical results for an illustrative navigation example are provided to show the performance improvement and support the discussion.

Distributed Estimation for Multi-Subsystem With Coupled Constraints

The problem of distributed constraint-coupled estimation for multi-subsystem is addressed, in which there exist coupled linear equality constraints generated by cooperating neighbor subsystems (nodes). Our goal is to design a distributed estimator such that the state estimates obtained by each node can satisfy the all/global constraints based on the fact that each node only knows the local coupling constraints with its neighbors. To this end, the global constrained weighted least squares (GCWLS) optimization, as a centralized processing scheme, is presented firstly with its recursive implementation. Then, the distributed estimator of each node is constructed based on the structure of the above recursive centralized estimator and the basic idea of consensus iterative. Moreover, the sufficient conditions are established to guarantee that the iterative solution of the designed distributed estimator is able to converge to the centralized GCWLS estimates, which demonstrates that the local estimates obtained by the distributed estimator satisfy the all/global constraints. Finally, an example of collaborative navigation for formation airplanes is provided to demonstrate the effectiveness of the proposed estimator.

Analysis and Selection of Shipping Route in Ocean

This electronic document is a “live” template. Ocean navigation is a long-distance cross ocean navigation. There are three kinds of ocean routes in common use: great circle route, rhumb line route and mixed route. The great circle route is usually used when ships are sailing in the ocean, but sometimes in the mid latitude sea area, the rhumb line route is a choice in the mid latitude sea area. Combined with the calculation of a navigation example, this paper makes some analysis on the selection of ocean routes in mid latitude sea area, illustrates the advantages and disadvantages of these three ocean routes with data, and draws a conclusion on how to choose the routes.

Interactive navigation of multiple convex patches

AbstractAmong the approaches to multi‐criteria decision making, Pareto navigation is a powerful, interactive tool that has been successfully applied to a variety of real‐world problems with continuous decision variables, including chemical process design, drug manufacturing, logistical vehicle routing problems, and radiotherapy treatment planning. However, many real‐life problems are formulated using both continuous and binary decision variables. In this work, we introduce patch navigation as an algorithmic concept that extends Pareto navigation to this type of problem where the number of binary variables is relatively small. The underlying idea is the navigation across a finite set of individual, convex fronts each associated with a specific configuration of the binary variables (patches). We show how the user interactions employed in current Pareto front navigation, namely selection and restriction, can be adopted to handle multiple patches. These routines enable the decision maker (DM) to change the solution in small increments while controlling the related trade‐offs. We also describe additional, patch‐specific routines that enable the DM to consider only an individually chosen subset of patches in the navigation. To illustrate patch navigation, and to demonstrate its usefulness for real‐life problems, we present numerical examples of patch navigation along with an application motivated by radiotherapy planning.

The Use of Skin Staples as Fiducial Markers to Confirm Intraoperative Spinal Navigation Registration and Accuracy.

With the advent of intraoperative computed tomography (CT) for image guidance, numerous examples of accurate navigation being applied to cranial and spinal pathology have come to light. For spinal disorders, the utilization of image guidance for the placement of percutaneous spinal instrumentation, complex osteotomies, and minimally invasive approaches are frequently utilized in trauma, degenerative, and oncological pathologies. The use of intraoperative CT for navigation, however, requires a low target registration error that must be verified throughout the procedure to confirm the accuracy of image guidance. To present the use of skin staples as a sterile, economical fiducial marker for minimally invasive spinal procedures requiring intraoperative CT navigation. Staples are applied to the skin prior to obtaining the registration CT scan and maintained throughout the remainder of the surgery to facilitate confirmation of image guidance accuracy. This low-cost, simple, sterile approach provides surface landmarks that allow reliable verification of navigation accuracy during percutaneous spinal procedures using intraoperative CT scan image guidance. The utilization of staples as a fiducial marker represents an economical and easily adaptable technique for ensuring accuracy of image guidance with intraoperative CT navigation.

Constrained Cross-Entropy Method for Safe Reinforcement Learning

We study a safe reinforcement learning problem, in which the constraints are defined as the expected cost over finite-length trajectories. We propose a constrained cross-entropy-based method to solve this problem. The key idea is to transform the original constrained optimization problem into an unconstrained one with a surrogate objective. The method explicitly tracks its performance with respect to constraint satisfaction and thus is well suited for safety-critical applications. We show that the asymptotic behavior of the proposed algorithm can be almost-surely described by that of an ordinary differential equation. Then, we give sufficient conditions on the properties of this differential equation for the convergence of the proposed algorithm. At last, we show the performance of the proposed algorithm in two simulation examples. In a constrained linear-quadratic regulator example, we observe that the algorithm converges to the global optimum with high probability. In a 2-D navigation example, we find that the algorithm effectively learns feasible policies without assumptions on the feasibility of initial policies, even with non-Markovian objective functions and constraint functions.

Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning

Reinforcement learning (RL) is a framework to optimize a control policy using rewards that are revealed by the system as a response to a control action. In its standard form, RL involves a single agent that uses its policy to accomplish a specific task. These methods require large amounts of reward samples to achieve good performance, and may not generalize well when the task is modified, even if the new task is related. In this paper we are interested in a collaborative scheme in which multiple agents with different tasks optimize their policies jointly. To this end, we introduce cross-learning, in which agents tackling related tasks have their policies constrained to be close to one another. Two properties make our new approach attractive: (i) it produces a multi-task central policy that can be used as a starting point to adapt quickly to one of the tasks trained for, in a situation when the agent does not know which task is currently facing, and (ii) as in meta-learning, it adapts to environments related but different to those seen during training. We focus on continuous policies belonging to reproducing kernel Hilbert spaces for which we bound the distance between the task-specific policies and the cross-learned policy. To solve the resulting optimization problem, we resort to a projected policy gradient algorithm and prove that it converges to a near-optimal solution with high probability. We evaluate our methodology with a navigation example in which agents can move through environments with obstacles of multiple shapes and avoid obstacles not trained for.

Automatic Patrol and Inspection Method for Machinery Diagnosis Robot—Sound Signal-Based Fuzzy Search Approach

This paper developed the basic theories and techniques for a plant machinery diagnosis robot (MDR). The workplace of MDR is usually a large-scale plant or a place with a dangerous environment. It must autonomously carry out the condition monitoring of plant machinery with sensors in order to detect machinery faults for securing the safety of the plant. In this paper, the basic function, concept and structure of the MDR are discussed. The intelligent control method by rough set, fuzzy neural network (FNN) and self-location-azimuth correction for the MDR that patrols on the inspection route are proposed. Additionally, navigation and search method by fuzzy inference using extracted fault sound signals are also proposed, by which the MDR will be navigated to the near side of the faulty machine when the fault of a machine has been detected during inspection. Finally, examples of practical navigation and fault machine searching are also shown.

Chemotaxis strategies of bacteria with multiple run modes.

Bacterial chemotaxis-a fundamental example of directional navigation in the living world-is key to many biological processes, including the spreading of bacterial infections. Many bacterial species were recently reported to exhibit several distinct swimming modes-the flagella may, for example, push the cell body or wrap around it. How do the different run modes shape the chemotaxis strategy of a multimode swimmer? Here, we investigate chemotactic motion of the soil bacterium Pseudomonas putida as a model organism. By simultaneously tracking the position of the cell body and the configuration of its flagella, we demonstrate that individual run modes show different chemotactic responses in nutrition gradients and, thus, constitute distinct behavioral states. On the basis of an active particle model, we demonstrate that switching between multiple run states that differ in their speed and responsiveness provides the basis for robust and efficient chemotaxis in complex natural habitats.

Role of patient navigation in improving access to radiotherapy treatment in a limited resource setting.

e19019 Background: Approximately 60% of cancer patients in low- and middle-income countries (LMICs) require radiotherapy (RT), with RT contributing 40% of curative treatment for cancers. Timely RT impacts prognosis. There are several difficulties in accessing RT in the Brazilian public health system: deficit of devices, obsolete machines, heterogeneous distribution of services, lack of investment in human resources and socioeconomic obstacles. Thus, it becomes necessary to optimize available resources. Although there are examples of successful patient navigation (PN) programs in vulnerable populations in high-income countries (HICs), data is scarce for LMICs. Considering the high incidence of cancer, complex difficulties in accessing treatment, and the positive results of PN in HICs, it was decided to evaluate this tool to improve RT access in the public system in Brazil. Methods: This pilot study was carried out in in a public school hospital in Belo Horizonte, Brazil, in partnership with the Global Cancer Institute, with a historical cohort as the control arm. The primary objective was to evaluate the changes in the time from definitive diagnosis to the start of treatment with the implementation of PN. Secondary objectives included: evaluating changes in the time between the beginning and end of RT, identification of the obstacles to access care, and quality of life data. Candidates for the program would be those considered for neoadjuvant or definitive RT, without previous cancer treatment. Results: 124 patients were included in the retrospective arm (12 excluded) and 73 in the navigation arm (1 excluded). Most had loco-regionally advanced disease, the most prevalent sites being esophagus, head, neck, and rectum. PN decreased the median time from biopsy result to the beginning of RT from 108 to 74 days (p &lt; 0.001). PN also decreased the time between biopsy results and referral to RT (53 to 40.5 days, p = 0.011); between the referral and the first consultation in the RT (25 to 13 days, p &lt; 0.001) and between the referral and the end of the RT (98 to 78 days, p &lt; 0.003). There was an increase in the proportion of patients who started RT within 60 days (maximum period established by law to start cancer treatment), from 20.5% in the retrospective arm to 38.5% in the prospective arm (p = 0.026). PN in this context showed no impeditive costs ( &lt; $140 USD per patient) with a satisfaction rate greater than 90% from patients. Conclusions: In an oncological context of socioeconomic vulnerability, PN is a financially viable and efficient tool to optimize access to timely radiotherapy.