Artificial Intelligence Human-Robot

Introduction:

Robots are used in industries to automate work and improve productivity. Robots occupy the human workspace. Robots can interact with humans and take instructions from humans to automate their work. Robots are being used in the human environment. Robots share working environments with humans to conduct tasks either with humans or alone.

Robots design is complicated. Its design includes spherical joints, compliant joints, distributed parallel actuation, and variable stiffness actuation. Motion planning and control make men and robots the same workspace. Planning techniques used for the static environment become ineffective for the crowded environment. Developing planning techniques that can be used in static and crowded environments is a complex and time-consuming task. To model unpredictable human motions that depend on time and the working environment is a complex and time-consuming task. There does not exist a pre-defined model that can reflect human motion.

To model human motion, sensor-based techniques are used in association with offline motion planning. In addition, sensor-based techniques are used to avoid obstacles.

Artificial Intelligence-based techniques are used to model human emotions. Artificial Intelligence-based techniques are used to build robot paths. These techniques are based on algorithmic and heuristic variations. Control algorithms are used to regulate virtual impedances. Robot activities include start, stop, slow, and move-up and down. To model these activities, fuzzy rules are used. One such model is a flexible framework. The flexible framework has rules that are used to avoid obstacles. To control the arm of the Cartesian robot path is used that work using different control points.

Robots motions are also modeled using distance-based rules. In addition to the distance-based rule, the speed at which the robot will approach an object, the speed at which the robot will move, the inertia of the robot, and momentum are used to model human robots. To model human robots, human physical limits are also considered. The safe points are calculated using the distance and velocity at the robot approaches an obstacle. The real-time movement of the robot is based on the quadratic goal-seeking function and quadratic obstacle-avoidance functions.

The super-quadratic functions are used to model the human body and can produce efficient and accurate results. However, a model for developing a human robot must use sensory information and control strategy.

To predict human motion safety mat system is also used. The sensory system of the safety system has a simple structure to control the robot to minimize computational complexity. To do this, self-organizing maps based on artificial neural networks are used. The self-organizing maps human behavior. This is done by using the super-quadric base model. To model human behavior reactive control system is used.

Artificial Neural Network:

Artificial Neural Networks are based on biological neural networks and can imitate human brains. Artificial Neural Network is based on artificial neurons. Artificial neurons are connected units and reflect neurons in the human brain.

A neural circuit is a network of neurons. A neuron is a cell that gets excited when electricity is passed. When a neuron is excited, it communicates with other cells. All the nerve cells are connected with a special connective called synapses. A neural circuit is connected to build a network.

An Artificial Neural Network (ANN) contains interconnected artificial neurons and imitates neurons in the human brain. Each interconnected neuron is capable of transmitting signals to other neurons. An artificial neuron process the received signal and then forward the received signal to other connected neurons.

The signal is specified in the form of a real number and each neuron process this real number using a non-linear function. All the neurons are connected with edges. Neurons and edges are modified as the learning proceeds. The strength of the signal is specified with the help of weight. The higher the weight, the stronger the signal. Neurons emit a signal only if it exceeds the signal emitting threshold.

There are many neurons, and they are arranged in layers. Each layer is assigned a specific function. Each layer receives an input process it and produces output. The input layer receives the signals, processes it, and produces output layer. The signals traverse the network multiple times across the network.

Neural networks are trained. The neural network training is done by using a set of examples. Each example has an input and a specified result. The neural network is based on probability. The neural network training is done by using an example. The training is evaluated by finding the difference between the output of the network and desired output. If any difference is found between processed output and desired output, it is treated as an error. This error value is used to fine-tune the network. This process is continued until the process output becomes similar to that of the target output. This process is repeated till the specified criteria have been met. This process is known as supervised learning and is an established artificial intelligence algorithm.

Supervised learning algorithms are used in recognizing images.

ANN is used to imitate the human brain architecture. The algorithms of ANN are designed to work the same way that the human brain can do the work. The neurons in ANN are connected so that the output of some of the neurons works as an input to other neurons. The ANN is a directed graph with each edge assigned a defined weight. The weight on each edge specifies how strong the influence of one node is on another node.

The Artificial Neural Network is built using Neurons, Connections and Weights, Propagation Function, Organization, Hyperparameter, learning, Learning rate, Cost function, and Backpropagation.

Each ANN consists of artificial neurons. Each artificial neuron has a defined set of inputs that produce a specified output. This output also works as an input to other neurons. The input to the ANN is the characteristics of the images or documents fed into the ANN. The ANN process this input and produce output.

All the input to the neuron is weighted. The inputs are added to produce a result. This result is called activation. The activation function then processes this result. The input data can be of type image, and output is the correct recognition of the image.

The ANN contains connections to connect artificial neurons with each other. The output of one neuron may act as input to other neurons. Moreover, in ANN, all connections are weighted. Thus a neuron can have a multi-degree connection.

To treat the output of one neuron as input to another neuron, the propagation function is used. The propagation function converts the output of one neuron as the input to the other neuron.

The neurons are connected in multiple layers. Neurons that belong to one layer are connected to the preceding and following layers. The layer of neurons that take input is the input layer, and the layer that produces output is the output layer. The layer of neurons between input and output layers is called neurons of the hidden layer. ANN doesn’t need to have multiple layers. ANN can be of a single layer or no layer. The neurons between different layers can be connected using different connection patterns.

The neurons in ANN can be fully connected; each layer is connected to all the other neurons in the next layer. The connection can be polling with a set of neurons in one layer connected to a single neuron in the other layer. This leads to neuron optimization. The neurons in ANN form a directed acyclic graph and are also known as feedforward networks. When the neurons are connected that belong to the same or previous layer, this is known as a recurrent network.

The learning rate of the neural network is defined using the hyperparameter. Before the ANN learns using examples, the value of the hyperparameter is set, the value is a constant. So the value depends on the learning done by ANN.

ANN learns by executing examples based on observation. As a result, ANN learns to improve the accuracy and throughput of the network. The accuracy and throughput is improved by minimizing the errors. To reduce the error to 0, the ANN can be redesigned.

Learning Algorithm used to develop Human-Robot. :

To develop a human robot, three learning algorithms are used – Supervised Learning Algorithm, Unsupervised Learning Algorithm, and Reinforcement Learning Algorithm.

Supervised Learning Algorithm:

A supervised Learning Algorithm executes a set of examples as input and produces corresponding output. The algorithm process the input to produce the desired output. The algorithm reduces the error rate by processing input and producing the desired output. The error rate is reduced using mean-squared error. The supervised learning algorithm is used to classify the set of inputs. This can also be termed a classification algorithm. The supervised learning algorithm recognizes speech, face, gesture, and handwriting.

Unsupervised Learning Algorithm :

Unsupervised learning algorithms process input data using a defined cost function. The cost function is dependent on the domain of the model, and the domain decides the critical parameters of the algorithm. The unsupervised learning algorithms are used in designing and developing clusters, compression, and filtering.

Reinforcement Learning :

Reinforcement Learning Algorithm is used when action is taken based on developing conditions. The objective of the reinforcement learning algorithm is to obtain the optimized solution. The reinforcement learning algorithm uses predefined rules based on developing conditions. The next action in the reinforcement learning algorithm is based on prior solutions and choosing the most optimized solution from the available solutions.

Dynamic programming is an example of a reinforcement learning algorithm and is used to obtain the optimized routes from the available set of routes. Dynamic programming in association with ANN is also used to find suitable action in video games.

Dynamic programming in association with reinforcement is used in control problems, virtual games, and tasks that require decision-making.

Self-Learning :

Self-learning is a type of neural network also known as Crossbar Adaptive Array (CAA). It is a system that takes input and processes it using a pre-defined condition and produces output. The self-learning algorithm does not take actions based on developing conditions. The self-learning algorithm is used to evolve and work with cognition and emotion and thus is useful in developing human-robot.

This algorithm is actively used in behavioral environments and genetic environments.

Self-Organizing Maps:

The self-organizing map belongs to the unsupervised machine learning algorithm. The self-organizing map is used to produce a two-dimensional representation of the data set using a wide variety of dimensional data sets and to preserve the topological structure of the defined data set.

The self-organizing map belongs to the class ANN. The self-organizing map is based on a competitive learning algorithm. The Competitive learning algorithm is based on an unsupervised learning algorithm. The competitive learning algorithm is used to find the correct solution using the input data set. A competitive learning algorithm improves the efficiency of each node in the network. Moreover, a competitive learning algorithm is used to find a cluster in the data set.

Vector quantization and Self-Organization Maps are based on competitive learning algorithms. The three basic properties of a competitive learning algorithm include identifying a set of neurons that produce a different response when given a set of inputs. Second, identification of a limit set on each neuron, and third, a well-defined function that chooses the correct response based on a given set of inputs. Finally, the correct response is chosen by a single neuron active at that time.

The competitive learning algorithm is used to identify the cluster of nodes. This algorithm finds and sets up the sensors, identifies the highest output sensor, and improves the sensor’s signal strength.

Self-organizing maps are trained using a set of inputs to generate maps and classify the input data. The input data set with p dimension is mapped to a data set of two dimensions. The neurons’ arraignment takes the shape of hexagonal having two dimensions.

The self-organizing map is responsible for deciding the behavior of the whole network or part of the network to produce a response in a similar way when a particular set of input is received. This is useful for processing visual, auditory, or sensory information. This imitates the human brain and thus in developing robots.

Artificial Intelligence Human Robots:

Artificial Intelligence Human-Robot resembles the shape of the human body. Artificial Intelligence-based Human Robots are capable of interacting with tools and environments. Human Robots are used in the field of medicine and biotechnology. In addition, human Robots are capable of operating equipment and vehicles.

Robots use sensors. Sensors can sense the surrounding environment. Sensors are used in robotic paradigms. The robotic paradigm decides robot operations. Robots operate using three basic robotic elements: Sensing, Planning, and Acting.

Conclusion:

Artificial Neural Networks, self-organizing maps are used to build super-quadratic models to imitate motions like humans. The self-organizing maps use sensory systems to track human actions and develop a human model.

Robots are used in industries to automate the work and improve productivity. Robots occupy the human workspace. Robots can interact with humans and take instructions from humans to automate their work. Robots are being used in the human environment. Robots share the working environment with humans to conduct tasks either with humans or alone.

Robots design is complicated. Its design includes spherical joints, compliant joints, distributed parallel actuation, and variable stiffness actuation. Motion planning and control is done to make the workspace of human and robot the same. Planning techniques used for the static environment become ineffective for the crowded environment. Developing planning techniques that can be used in static and crowded environments is a complex and time-consuming task. To model unpredictable human motions that depend on time and working environment. There does not exist a pre-defined model that can reflect human motion.

To develop a human robot, three learning algorithms are used – Supervised Learning Algorithm, Unsupervised Learning Algorithm, and Reinforcement Learning Algorithm.