Improvement of an adaptive fuzzy-based obstacle avoidance algorithm using virtual and real kinect sensors

Csaba, G.

doi:10.1109/icccyb.2013.6617572

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Thus, the function for each linguistic variable is defined just in terms of a constant D i . As described in [28], for each linguistic value, different functions were defined as follows: The number of permutations in the linguistic values (Near, Medium, and Far) and the respective input variables (Left, Front, and Right) produce the set of fuzzy rules, which is defined and developed to obtain the output value corresponding to the turn in the mobile platform. The fuzzy control uses 27 rules, divided into four groups, depending on a common goal: rules for performing pronounced turns, rules for predicting collisions, rules for straight-line movements, and rules for basic turns.…”

Section: Pixelwise Bit Reductionmentioning

confidence: 99%

“…In this context, the robot can go to different places, and the system takes into account the work volume of the robot. Thus, the depth image processed by the previous module is divided into Left Subimage, Center Subimage, and Right Subimage (see Figure6a-c)[28]. Each sub-image has a size of 211 × 400 pixels, representing the possible reference paths where the mobile platform can go: turn right, forward, and turn left.…”

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confidence: 99%

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Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

et al. 2021

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Obstacle–Avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow for comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies a fuzzy inference system (FIS) to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on a multilayer perceptron (MLP) architecture, which is a class of feedforward artificial neural network (ANN), and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive Obstacle–Avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “experience” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

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Section: Pixelwise Bit Reductionmentioning

confidence: 99%

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confidence: 99%

Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

et al. 2021

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“…Also, for obstacle avoidance, two aspects need to be Right Subimage (see Fig. 6(a)-(c)) [25]. Each sub-image has a size of 211x400 pixels,…”

Section: Case Ii: Mobile Robot and Neural Networkmentioning

confidence: 99%

“…So the 360 function for each linguistic variable is defined just in terms of a constant D i . As described 361 in[25], for each linguistic value, different functions were defined as follows:362 • D1 =90 for VeryPositive, 363 • D2 = 60 for Positive, 364 • D3 = 30 for LittlePositive, 365 • D4 = 0 for Zero, 366 • D5 = −30 for LittleNegative, 367 • D6 = −60 for Negative, and 368 • D7 = −90 for VeryNegative. 369 The number of permutations in the linguistic values (Near, Medium, and Far) 370 and the respective input variables (Left, 373 groups, depending on a common goal: rules for performing pronounced turns, rules for 374 predicting collisions, rules for straight-line movements, and rules for basic turns.…”

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confidence: 99%

Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots based on Artificial Intelligence

Medina-Santiago¹,

Morales-Rosales²,

Hernández-Gracidas³

et al. 2021

Preprint

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Obstacle-avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies fuzzy logic to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on multi-layer perceptron and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive obstacle-avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “ experience ” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

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“…While we make no assumption about the intent of the robot manufacturer and it would be unusual for a technology to be described with the level of granularity that it's process for file writing is full detailed, there are many products that suggest that they are, or could be, programmed to write out data based on target presentation (Hogan et al, 1995;Lockery et al, 2011;Csaba, 2013;Crainic et al, 2014) and note that this is so in our own device, for which four identical robots were purchased from the same entity, for use in a multi-site clinical trial. Furthermore, we assume that the end-user would prefer the data be partitioned into movement epochs, not strictly by target achievement; as seen in Figure 1, these two criteria may yield two different datasets.…”

Section: Importance Of the Problemmentioning

confidence: 99%

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

et al. 2017

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Point-to-point exercising of the upper-limb, as elicited through the presentation of visual targets on a computer screen, is a ubiquitous paradigm in the robot-assisted rehabilitation of motor-impaired individuals. Kinematic data collected from the robot's sensors can be used to assess motor function; these data allow objective quantification of motor performance, an approach that shows promise both for guiding therapy and documenting patient progress. It is imperative that these datasets be fully understood and that tools be continually developed to support analysis and proper interpretation of robot-generated data. It is our experience that data collected from kinematic robots and partitioned according to target achievement may be prone to errors in analysis and interpretation because the movements of highly spastic individuals rarely stop within the target. Here, we propose that it is preferable to partition serial movement data based on local minima in velocity rather than target achievement; this design reflects the convention that movement epochs start and end at low or zero velocity, an assumption that is prevalent even in severely impaired individuals. Using a commercially available robot (MIT-Manus, Interactive Motion Technologies), we recorded movements from 16 moderate to severely impaired chronic stroke patients. Data partitioned according to target presentation typically interrupted movements in mid-motion: velocity at file start was 32.6 ± 26.4% of the overall velocity range. By re-apportioning, we obtained velocity at file start of 7.4 ± 9.5% of total range. Across 3,200 movements, 12.4 ± 10.4% of data points were re-allocated on average. Thus, our routine is capable of re-partitioning to more accurately reflect observed behavior. Our study is thus the first to identify and propose a solution to the problem of high relevance to the community of robot-aided rehabilitation specialists, i.e., suboptimal partitioning according to target achievement. Through the algorithm described in this paper, we were able to re-partition the data so that movement epochs were properly demarcated at velocity minima, thus adhering to the fundamental assumptions of human motor behavior and facilitating analysis of patient performance on a per-movement basis.

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Improvement of an adaptive fuzzy-based obstacle avoidance algorithm using virtual and real kinect sensors

Cited by 5 publications

References 12 publications

Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots based on Artificial Intelligence

A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data

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