•Kinematic problem-solving for delta robot.•DIY delta robot (electrical/mechanical).•Vision-based object tracking.•HSV-based object sorting algorithm.•Camera-to-robot frame position transformation.
...The main topic of this paper is how to develop pick and place delta robot with high efficiency mechanical parts and prosaically assemble it, additionally built electrical wiring diagram all of this supported by vision computer system. The system has a low-cost part and solves many problems to build this robot. Electrical section has two main components master and slave the master is raspberry pi and slave is Arduino, master is used to vision detect object and Arduino is used to invers kinematics equation, move motors to target position, control in hydraulic system to pull the object with suction cup. The process of vison system is passing through some steps, detect the object by color range, contouring box then send the real position to an Arduino. The experimental process proved the effectiveness and accuracy of the delta robot vision system in the sorting process from conveyor system. The target of system that function is applied with a low cost.
The main focus of this paper is to design and develop a system of two robot arms for classifying and sorting objects based on shape and size using machine vision. The system uses a low-cost and ...high-performance hierarchical control system including one master and two slaves. Each slave is a robot controller based on a microcontroller that receives commands from the master to control the robot arm independently. The master is an embedded computer used for image processing, kinematic calculations, and communication. A simple and efficient image processing algorithm is proposed that can be implemented in real-time, helping to shorten the time of the sorting process. The proposed method uses a series of algorithms including contour finding, border extraction, centroid algorithm, and shape threshold to recognize objects and eliminate noise. The 3D coordinates of objects are estimated just by solving a linear equation system. Movements of the robot's joints are planned to follow a trapezoidal profile with the acceleration/deceleration phase, thus helping the robots move smoothly and reduce vibration. Experimental evaluation reveals the effectiveness and accuracy of the robotic vision system in the sorting process. The system can be used in the industrial process to reduce the required time to achieve the task of the production line, leading to improve the performance of the production line.
Field-programmable gate arrays (FPGAs) and, recently, System on Chip (SoC) devices have been applied in a wide area of applications due to their flexibility for real-time implementations, increasing ...the processing capability on hardware as well as the speed of processing information in real-time. The most important applications based on FPGA/SoC devices are focused on signal/image processing, Internet of Things (IoT) technology, artificial intelligence (AI) algorithms, energy systems applications, automatic control and industrial applications. This paper develops a robot arm controller based on a programmable System-OnChip (SoC) device that combines the high-performance and flexibility of a CPU and the processing power of an FPGA. The CPU consists of a dual-core ARM processor that handles algorithm calculations, motion planning and manages communication and data manipulation. FPGA is mainly used to generate signals to control servo and read the feedback signals from encoders. Data from the ARM processor is transferred to the programmable logic side via the AXI protocol. This combination delivers superior parallel-processing and computing power, real-time performance and versatile connectivity. Additionally, having the complete controller on a single chip allows the hardware design to be simpler, more reliable, and less expensive.
This article proposes a formal method of designing robotic systems focusing on communication between components, as well as standardization of the messages between those components. The objective is ...to design a robotic system controller in a systematic way, focusing on communication at an abstract agent level. Communication, thus organized, and its properly defined specification facilitate the system’s further development. The method uses a standard message structure, based on IEEE FIPA standards, for communication within robotic systems composed of agents. Communication-focused top-down design of robotic systems based on binary decomposition is proposed, and used to design a companion robot working in the kitchen environment. The implemented robotic system is verified based on whether or not the specification conforms to the specified requirements. The characteristics of the designed communication are evaluated. The obtained results prove that the proposed method of designing robotic systems is formally correct, it facilitates the implementation of agents, and separates specification of the system from its implementation. The method of designing robotic systems is correct and useful. The proposed formal notation facilitates understanding of how the system operates and organizes the design process. It puts the communication between system components at the forefront. The resulting system specification facilitates the implementation. The tools for experimental evaluation of its characteristics enable the confirmation that it fulfills the requirements, and that the communication between the system components is correct.
This paper proposes a bionic robot controller equipped with intelligent perception and autonomous planning modules to address the manufacturing industry's requirements for small-batch, customized, ...and autonomous task. Three crucial components: motion control module, vision perception module, and autonomous planning module are integrated into the controller based on the ROS framework and ECI platform. Taking multi-object rearrangement problem as an example, a dual-robot collaborative system is established for validation of the controller. The controller deploys the YOLOv5_OBB network for object recognition and localization. Leveraging the task sequence planning and RRT-Growth-Angle algorithm improved in this paper, it autonomously plans collision-free trajectories for the robots, facilitating their movement from the starting point to the grasping position and further to the placement location. The motion control algorithm collaboratively controls dual-robot, ensuring the precise and stable rearrangement of multiple objects into predefined positions. The results affirm that the bionic robot controller effectively mimics the three essential components of human-like functionality-perceiving, pondering, and acting-enabling it to autonomously and intelligently tackle complex tasks, which verifies the viability of the method.
In this work, we present the first steps toward the creation of a new neurorobotics model of Parkinson's Disease (PD) that embeds, for the first time in a real robot, a well-established computational ...model of PD. PD mostly affects the modulation of movement in humans. The number of people suffering from this neurodegenerative disease is set to double in the next 15 years and there is still no cure. With the new model we were capable to further explore the dynamics of the disease using a humanoid robot. Results show that the embedded model under both conditions,
healthy
and
parkinsonian
, was capable of performing a simple behavioural task with different levels of motor disturbance. We believe that this neurorobotics model is a stepping stone to the development of more sophisticated models that could eventually test and inform new PD therapies and help to reduce and replace animals in research.
We present a description of an ASM-network, a new habit-based robot controller model consisting of a network of adaptive sensorimotor maps. This model draws upon recent theoretical developments in ...enactive cognition concerning habit and agency at the sensorimotor level. It aims to provide a platform for experimental investigation into the relationship between networked organizations of habits and cognitive behavior. It does this by combining (1) a basic mechanism of generating continuous motor activity as a function of historical sensorimotor trajectories with (2) an evaluative mechanism which reinforces or weakens those historical trajectories as a function of their support of a higher-order structure of higher-order sensorimotor coordinations. After describing the model, we then present the results of applying this model in the context of a well-known minimal cognition task involving object discrimination. In our version of this experiment, an individual robot is able to learn the task through a combination of exploration through random movements and repetition of historic trajectories which support the structure of a pre-given network of sensorimotor coordinations. The experimental results illustrate how, utilizing enactive principles, a robot can display recognizable learning behavior without explicit representational mechanisms or extraneous fitness variables. Instead, our model's behavior adapts according to the internal requirements of the action-generating mechanism itself.
The interest of central pattern generators in robot motor coordination is universally recognized so much so that a lot of possibilities on different scales of modeling are nowadays available. While ...each method obviously has its advantages and drawbacks, some could be more suitable for human–robot interactions. In this paper, we compare three oscillator models: Matsuoka, Hopf and Rowat–Selverston models. These models are integrated to a control architecture for a robotic arm and evaluated in simulation during a simplified handshaking interaction which involves constrained rhythmic movements. Furthermore, Hebbian plasticity mechanisms are integrated to the Hopf and Rowat–Selverston models which can incorporate such mechanisms, contrary to the Matsuoka. Results show that the Matsuoka oscillator is subpar in all aspects and for the two others, that plasticity improves synchronization and leads to a significant decrease in the power consumption.
Interventional surgical robots are widely used in neurosurgery to improve surgeons' working environment and surgical safety. Based on the actual operational needs of surgeons' feedback during ...preliminary in vivo experiments, this paper proposed an isomorphic interactive master controller for the master-slave interventional surgical robot. The isomorphic design of the controller allows surgeons to utilize their surgical skills during remote interventional surgeries. The controller uses the catheter and guidewire as the operating handle, the same as during actual surgeries. The collaborative operational structure design and the working methods followed the clinical operational skills. The linear force feedback and torque feedback devices were designed to improve the safety of surgeries under remote operating conditions. An eccentric force compensation was conducted to achieve accurate force feedback. Several experiments were carried out, such as calibration experiments, master-slave control performance evaluation experiments, and operation comparison experiments on the novel and previously used controllers. The experimental results show that the proposed controller can perform complex operations in remote surgery applications and has the potential for further animal experiment evaluations.
Crowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or ...impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment; the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particular, perception is based on a raycasting model which is tailored on the ANN. An in-depth analysis of a number of parameters of the environment and the robot is performed and a comparative analysis is presented; finally, results of the performance of the controller trained with NEAT are compared to those of a human driver who takes over the controller itself. Results show that the intelligent controller is able to perform on par with the human, within the simulated environment.
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