Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm

AbstractThis paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional–integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA*) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA* path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator.

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COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

Journal of Marine Science and Engineering ◽

10.3390/jmse9040405 ◽

2021 ◽

Vol 9 (4) ◽

pp. 405

Author(s):

Raphael Zaccone

Keyword(s):

Path Planning ◽

Real Time ◽

Collision Avoidance ◽

Autonomous Navigation ◽

Optimal Path ◽

Open Water ◽

Optimal Path Planning ◽

Guidance And Control ◽

Real Time Applications ◽

And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

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Real-Time Path Planning Based on Harmonic Functions under a Proper Generalized Decomposition-Based Framework

Sensors ◽

10.3390/s21123943 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3943

Author(s):

Nicolas Montés ◽

Francisco Chinesta ◽

Marta C. Mora ◽

Antonio Falcó ◽

Lucia Hilario ◽

...

Keyword(s):

Path Planning ◽

Real Time ◽

Harmonic Functions ◽

Autonomous Navigation ◽

Numerical Technique ◽

Computational Cost ◽

Main Idea ◽

Main Property ◽

Proper Generalized Decomposition ◽

Computational Vademecum

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.

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Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-06-2020-0023 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Chittaranjan Paital ◽

Saroj Kumar ◽

Manoj Kumar Muni ◽

Dayal R. Parhi ◽

Prasant Ranjan Dhal

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Mobile Robots ◽

Real Time ◽

Autonomous Navigation ◽

Simulation Software ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Content Type ◽

Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

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Development of a magnetic tracking system for monitoring soil movements induced by geohazards

E3S Web of Conferences ◽

10.1051/e3sconf/20199217007 ◽

2019 ◽

Vol 92 ◽

pp. 17007 ◽

Cited By ~ 2

Author(s):

Xiaoyu Chen ◽

Rolando P. Orense

Keyword(s):

Magnetic Fields ◽

Permanent Magnets ◽

Tracking System ◽

Soil Liquefaction ◽

Small Scale ◽

Interior Point Algorithm ◽

Tracking Accuracy ◽

Tracking Errors ◽

Magnetic Tracking ◽

Soil Movements

In the study of geotechnical hazards, such as soil liquefaction and landslides, the analysis of soil movements is always one of the major preoccupations. An efficient movement sensing technique requires the tracking of subsurface soil for the purpose of examining the mechanism involved. A magnetic tracking system is therefore proposed, with permanent magnets as trackers and magnetometers as receivers. When permanent magnets, deployed within the soil to serve as excitation sources, move with soil body during a geotechnical event, they generate static magnetic fields whose flux densities are related with the positions and orientations of the magnets. Magnetometers are used as receivers to detect the generated magnetic fields, which can be further used in calculating the magnets' locations and orientations based on appropriately developed algorithms. Comparison between situations where the trackers are exposed to air and embedded within soil was conducted to evaluate the influence of soil (wet and dry) on the tracking accuracy. Also, multi-objective tracking is realized by using the particle swarm optimization (PSO) technique combined with interior-point algorithm. The tracking errors are evaluated and applications of the proposed system in small-scale laboratory tests for geohazards are discussed.

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An Autonomous Path Controller in a System on Chip for Shrimp Robot

Electronics ◽

10.3390/electronics9030441 ◽

2020 ◽

Vol 9 (3) ◽

pp. 441 ◽

Cited By ~ 3

Author(s):

Sergio Barrios-dV ◽

Michel Lopez-Franco ◽

Jorge D. Rios ◽

Nancy Arana-Daniel ◽

Carlos Lopez-Franco ◽

...

Keyword(s):

Path Planning ◽

Real Time ◽

Trajectory Tracking ◽

Neural Control ◽

Tracking System ◽

Kinematic Model ◽

System On Chip ◽

Optimal Controller ◽

Real Time System ◽

On Chip

This paper presents a path planning and trajectory tracking system for a BlueBotics Shrimp III®, which is an articulate mobile robot for rough terrain navigation. The system includes a decentralized neural inverse optimal controller, an inverse kinematic model, and a path-planning algorithm. The motor control is obtained based on a discrete-time recurrent high order neural network trained with an extended Kalman filter, and an inverse optimal controller designed without solving the Hamilton Jacobi Bellman equation. To operate the whole system in a real-time application, a Xilinx Zynq® System on Chip (SoC) is used. This implementation allows for a good performance and fast calculations in real-time, in a way that the robot can explore and navigate autonomously in unstructured environments. Therefore, this paper presents the design and implementation of a real-time system for robot navigation that integrates, in a Xilinx Zynq® System on Chip, algorithms of neural control, image processing, path planning, and inverse kinematics and trajectory tracking.

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Implementation of a real-time moving object tracking system using visual servoing

Robotica ◽

10.1017/s0263574703005332 ◽

2003 ◽

Vol 21 (6) ◽

pp. 615-625 ◽

Cited By ~ 16

Author(s):

M-C. Tsai ◽

K-Y. Chen ◽

M-Y. Cheng ◽

K.C. Lin

Keyword(s):

Real Time ◽

Visual Servoing ◽

Tracking System ◽

Moving Target ◽

Tracking Accuracy ◽

Security Systems ◽

Detection Techniques ◽

Control Scheme ◽

High System ◽

Automatically Tracking

Due to the increasing popularity of surveillance and security systems, the problem of automatically tracking a moving target by visual servoing has become a research topic deserving more investigation. Nonetheless, the success of tracking a moving target in real-time primarily depends on the performance of the motion detection techniques employed. This paper addresses visual tracking control of an unknown target that could be motional arbitrarily in the scene. A pan-tilt mechanism is used to gain the flexibility of tracking, and the so-called region-based matching method and motion energy method are modified and proposed in this study to detect a moving target based on the consecutive images acquired. A visual servo control scheme that adopts proportional control in the visual loop for reducing the servo lagging is proposed using output disturbance feedforward compensation. Experimental results show the superiority of the proposed method in achieving high system bandwidth and tracking accuracy.

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Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.190-191.906 ◽

2012 ◽

Vol 190-191 ◽

pp. 906-910 ◽

Cited By ~ 1

Author(s):

Hong Jiang Liu

Keyword(s):

Probability Density Function ◽

Particle Filter ◽

Probability Density ◽

Real Time ◽

Density Function ◽

Tracking System ◽

Interacting Multiple Model ◽

Multiple Model ◽

Tracking Accuracy ◽

Unscented Particle Filter

In order to study the tracking problem of maneuvering image sequence target in complex environment with multi-sensor array, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The motion array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.

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Dynamic Track Management in MHT for Pedestrian Tracking Using Laser Range Finder

Mathematical Problems in Engineering ◽

10.1155/2015/545204 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Abdul Hadi Abd Rahman ◽

Hairi Zamzuri ◽

Saiful Amri Mazlan ◽

Mohd Azizi Abdul Rahman ◽

Yoshio Yamamoto ◽

...

Keyword(s):

Real Time ◽

Autonomous Navigation ◽

Tracking System ◽

Computational Cost ◽

Laser Range Finder ◽

Tracking Problem ◽

Range Finder ◽

Pedestrian Tracking ◽

Laser Range ◽

Track Management

Real time pedestrian tracking could be one of the important features for autonomous navigation. Laser Range Finder (LRF) produces accurate pedestrian data but a problem occurs when a pedestrian is represented by multiple clusters which affect the overall tracking process. Multiple Hypothesis Tracking (MHT) is a proven method to solve tracking problem but suffers a large computational cost. In this paper, a multilevel clustering of LRF data is proposed to improve the accuracy of a tracking system by adding another clustering level after the feature extraction process. A Dynamic Track Management (DTM) is introduced in MHT with multiple motion models to perform a track creation, association, and deletion. The experimental results from real time implementation prove that the proposed multiclustering is capable of producing a better performance with less computational complexity for a track management process. The proposed Dynamic Track Management is able to solve the tracking problem with lower computation time when dealing with occlusion, crossed track, and track deletion.

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