Robotics engineering

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Robotics engineering is a branch of engineering that focuses on the conception, design, manufacturing, and operation of robots. It involves a multidisciplinary approach, drawing primarily from mechanical, electrical, software, and artificial intelligence (AI) engineering. [1] [2]

Contents

Robotics engineers are tasked with designing these robots to function reliably and safely in real-world scenarios, which often require addressing complex mechanical movements, real-time control, and adaptive decision-making through software and AI. [1]

Fundamental disciplines

Robotics engineering combines several technical disciplines, all of which contribute to the performance, autonomy, and robustness of a robot.

Mechanical engineering and kinematics

Mechanical engineering is responsible for the physical construction and movement of robots. This involves designing the robot's structure, joints, and actuators, as well as analyzing its kinematics and dynamics. [3]

Kinematics

Kinematic models are essential for controlling the movements of robots. Robotics engineers use forward kinematics to calculate the positions and orientations of a robot's end-effector, given specific joint angles, and inverse kinematics to determine the joint movements necessary for a desired end-effector position. These calculations allow for precise control over tasks such as object manipulation or locomotion. [4]

Actuation and materials

Robotics engineers select actuators—such as electric motors, hydraulic systems, or pneumatic systems—based on the robot's intended function, power needs, and desired performance characteristics. [5] Materials used in the construction of robots are also carefully chosen for strength, flexibility, and weight, with lightweight alloys and composite materials being popular choices for mobile robots. [6]

Electrical and electronics engineering

Robots depend on electrical systems for power, communication, and control.

Power management

Powering a robot's motors, sensors, and processing units requires sophisticated electrical circuit design. Robotics engineers ensure that power is distributed efficiently and safely across the system, often using batteries or external power sources in a way that minimizes energy waste. [7] [8]

Signal processing and sensors

A robot's ability to interact with its environment depends on interpreting data from various sensors. Electrical engineers in robotics design systems to process signals from cameras, LiDAR, ultrasonic sensors, and force sensors, filtering out noise and converting raw data into usable information for the robot's control systems. [9] [10]

Software engineering

Software engineering is a fundamental aspect of robotics, focusing on the development of the code and systems that control a robot's hardware, manage real-time decision-making, and ensure reliable operation in complex environments. Software in robotics encompasses both low-level control software and high-level applications that enable advanced functionalities. [11]

Embedded systems

Robotics engineers develop embedded systems that interface directly with a robot's hardware, managing actuators, sensors, and communication systems. These systems must operate in real-time to process sensor inputs and trigger appropriate actions, often with strict constraints on memory and processing power. [12] [13]

Software architectures and frameworks

Modern robots rely on modular and scalable software architectures. A popular framework in the field is the Robot Operating System (ROS), which facilitates communication between different subsystems and simplifies the development of robotic applications. Engineers use such frameworks to build flexible systems capable of handling tasks such as motion planning, perception, and autonomous decision-making. [14]

Real-time systems

Robots frequently operate in environments where real-time processing is critical. Robotics engineers design software that can respond to sensor data and control actuators within tight time constraints. This includes optimizing algorithms for low-latency and developing robust error-handling procedures to prevent system failure during operation. [15]

AI engineering

AI engineering plays an increasingly critical role in enabling robots to perform complex, adaptive tasks. It focuses on integrating artificial intelligence techniques such as machine learning, computer vision, and natural language processing to enhance a robot's autonomy and intelligence. [16]

Perception and computer vision

Robots equipped with AI-powered perception systems can process and interpret visual and sensory data from their surroundings. Robotics engineers develop algorithms for object recognition, scene understanding, and real-time tracking, allowing robots to perceive their environment in ways similar to humans. These systems are often used for tasks such as autonomous navigation or grasping objects in unstructured environments. [17] [18]

Machine learning for control and decision-making

Machine learning techniques, particularly reinforcement learning and deep learning, allow robots to improve their performance over time. Robotics engineers design AI models that enable robots to learn from their experiences, optimizing control strategies and decision-making processes. This is particularly useful in environments where pre-programmed behavior is insufficient, such as in search and rescue missions or unpredictable industrial tasks. [19] [20]

Control systems and feedback loops

Control systems engineering ensures that robots move accurately and perform tasks in response to environmental stimuli. Robotics engineers design control algorithms that manage the interaction between sensors, actuators, and software. [21] [22]

Closed-loop control

Most robots rely on closed-loop control systems, where sensors provide continuous feedback to adjust movements and behaviors. This is essential in applications like robotic surgery, where extreme precision is required, or in manufacturing, where consistent performance over repetitive tasks is critical. [22] [23]

Adaptive and nonlinear control systems

For more advanced applications, robotics engineers develop adaptive control systems that can modify their behavior in response to changing environments. Nonlinear control techniques are employed when dealing with complex dynamics that are difficult to model using traditional methods, such as controlling the flight of drones or autonomous underwater vehicles. [24] [25] [26]

Key tools and technologies

Robotics engineers leverage a wide array of software tools and technologies to design, test, and refine robotic systems.

Simulation software

Before physical prototypes are created, robotics engineers use advanced simulation software to model and predict the behavior of robotic systems in virtual environments. MATLAB and Simulink are standard tools for simulating both the kinematics (motion) and dynamics (forces) of robots. These platforms allow engineers to develop control algorithms, run system-level tests, and assess performance under various conditions without needing physical hardware. ROS (Robot Operating System) is another key framework, facilitating the simulation of robot behaviors in different environments. [27]

CAD and 3D modeling

For mechanical design, robotics engineers use Computer-Aided Design (CAD) software, such as SolidWorks, AutoCAD, and PTC Creo, to create detailed 3D models of robotic components. These models are essential for visualizing the physical structure of the robot and for ensuring that all mechanical parts fit together precisely. CAD models are often integrated with simulation tools to test mechanical functionality and detect design flaws early in the process. [28]

Rapid prototyping and 3D printing

Once the designs are verified through simulation, rapid prototyping technologies, including 3D printing and CNC machining, allow for the fast and cost-effective creation of physical prototypes. These methods enable engineers to iterate quickly, refining the design based on real-world testing and feedback, reducing the time to market. [29] [30]

Finite element analysis (FEA)

To ensure the robustness and durability of robotic components, engineers perform structural testing using finite alement analysis (FEA) software like ANSYS and Abaqus. FEA helps predict how materials will respond to stress, heat, and other environmental factors, optimizing designs for strength, efficiency, and material usage. [31]

Hardware-in-the-loop (HIL) testing

To bridge the gap between simulation and physical testing, robotics engineers often use hardware-in-the-loop (HIL) systems. HIL testing integrates real hardware components into simulation models, allowing engineers to validate control algorithms and system responses in real-time without needing the full robotic system built, thus reducing risks and costs. [32]

Challenges

The complexity of robotics engineering presents ongoing challenges.

Robustness and fault tolerance

Designing robots that can reliably operate in unpredictable environments is a key engineering challenge. Engineers must create systems that can detect and recover from hardware malfunctions, sensor failures, or software errors. This is important in mission-critical applications such as space exploration or medical robotics. [33] [34]

Safety in human-robot interaction

Ensuring safety in human-robot interaction is a significant challenge in the field of robotics engineering. In addition to technical aspects, such as the development of sensitive control systems and force-limited actuators, engineers must address the ethical and legal implications of these interactions. AI algorithms are employed to enable robots to anticipate and respond to human behavior in collaborative environments; however, these systems are not without flaws. When errors occur—such as a robot misinterpreting human movement or failing to halt its actions in time—the issue of responsibility arises. [35]

This question of accountability poses a substantial ethical dilemma. Should the responsibility for such errors fall upon the engineers who designed the robot, the manufacturers who produced it, or the organizations that deploy it? Furthermore, in cases where AI algorithms play a key role in the robot's decision-making process, there is the added complexity of determining whether the system itself could be partly accountable. This issue is particularly pertinent in industries such as healthcare and autonomous vehicles, where mistakes may result in severe consequences, including injury or death. [36]

Current legal frameworks in many countries have not yet fully addressed the complexities of human-robot interaction. Laws concerning liability, negligence, and safety standards often struggle to keep pace with technological advancements. The creation of regulations that clearly define accountability, establish safety protocols, and safeguard human rights will be crucial as robots become increasingly integrated into daily life. [36] [37] [38]

Optimization of motion and energy efficiency

Robotics engineers must balance the need for high performance with energy efficiency. Motion-planning algorithms and energy-saving strategies are critical for mobile robots, especially in applications like autonomous drones or long-duration robotic missions where battery life is limited. [39] [40]

Related Research Articles

Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form of decisions. "Understanding" in this context signifies the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

<span class="mw-page-title-main">Computer engineering</span> Engineering discipline specializing in the design of computer hardware

Computer engineering is a branch of electrical engineering that integrates several fields of electrical engineering, electronics engineering and Computer Science required to develop computer hardware and software. Computer engineering is referred to as Electrical and Computer engineering OR Computer Science and Engineering at some universities

Mechatronics engineering, also called mechatronics, is an interdisciplinary branch of engineering that focuses on the integration of mechanical engineering, electrical engineering, electronic engineering and software engineering, and also includes a combination of robotics, computer science, telecommunications, systems, control, automation and product engineering.

<span class="mw-page-title-main">Stewart platform</span> Type of parallel manipulator

A Stewart platform is a type of parallel manipulator that has six prismatic actuators, commonly hydraulic jacks or electric linear actuators, attached in pairs to three positions on the platform's baseplate, crossing over to three mounting points on a top plate. All 12 connections are made via universal joints. Devices placed on the top plate can be moved in the six degrees of freedom in which it is possible for a freely-suspended body to move: three linear movements x, y, z, and the three rotations.

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.

<span class="mw-page-title-main">Multi-agent system</span> Built of multiple interacting agents

A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. With advancements in Large language model (LLMs), LLM-based multi-agent systems have emerged as a new area of research, enabling more sophisticated interactions and coordination among agents.

<span class="mw-page-title-main">Mobile robot</span> Type of robot

A mobile robot is an automatic machine that is capable of locomotion. Mobile robotics is usually considered to be a subfield of robotics and information engineering.

Modular self-reconfiguring robotic systems or self-reconfigurable modular robots are autonomous kinematic machines with variable morphology. Beyond conventional actuation, sensing and control typically found in fixed-morphology robots, self-reconfiguring robots are also able to deliberately change their own shape by rearranging the connectivity of their parts, in order to adapt to new circumstances, perform new tasks, or recover from damage.

Hardware-in-the-loop (HIL) simulation, also known by various acronyms such as HiL, HITL, and HWIL, is a technique that is used in the development and testing of complex real-time embedded systems. HIL simulation provides an effective testing platform by adding the complexity of the process-actuator system, known as a plant, to the test platform. The complexity of the plant under control is included in testing and development by adding a mathematical representation of all related dynamic systems. These mathematical representations are referred to as the "plant simulation". The embedded system to be tested interacts with this plant simulation.

Adaptable Robotics refers to a field of robotics with a focus on creating robotic systems capable of adjusting their hardware and software components to perform a wide range of tasks while adapting to varying environments. The 1960s introduced robotics into the industrial field. Since then, the need to make robots with new forms of actuation, adaptability, sensing and perception, and even the ability to learn stemmed the field of adaptable robotics. Significant developments such as the PUMA robot, manipulation research, soft robotics, swarm robotics, AI, cobots, bio-inspired approaches, and more ongoing research have advanced the adaptable robotics field tremendously. Adaptable robots are usually associated with their development kit, typically used to create autonomous mobile robots. In some cases, an adaptable kit will still be functional even when certain components break.

Cyber-Physical Systems (CPS) are mechanisms controlled and monitored by computer algorithms, tightly integrated with the internet and its users. In cyber-physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context. CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science. The process control is often referred to as embedded systems. In embedded systems, the emphasis tends to be more on the computational elements, and less on an intense link between the computational and physical elements. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture; nevertheless, CPS presents a higher combination and coordination between physical and computational elements.

<span class="mw-page-title-main">Robotics</span> Design, construction, use, and application of robots

Robotics is the interdisciplinary study and practice of the design, construction, operation, and use of robots.

<span class="mw-page-title-main">Open-source robotics</span> Open-source branch of robotics

Open-source robotics is a branch of robotics where robots are developed with open-source hardware and free and open-source software, publicly sharing blueprints, schematics, and source code. It is thus closely related to the open design movement, the maker movement and open science.

The following outline is provided as an overview of and topical guide to robotics:

<span class="mw-page-title-main">Hendrik Van Brussel</span> Belgian roboticist

Hendrik (Rik) Van Brussel is a Belgian emeritus professor of mechanical engineering of the KU Leuven, world-renowned for his research on robotics, mechatronics and holonic manufacturing systems.


Rapid Control Prototyping (RCP) is a type of simulation methodology that allows for the rapid evaluation of control systems, especially for large machinery. It can test and evaluate algorithms as well as associated components such as sensors, actuators, pumps etc. The system requires some type of mock up, usually a scaled down version of the system to be tested, plus high powered computer simulation software. Rapid Control Prototyping has gained popularity thanks to its ability to accelerate product development and reduce their time-to-market. The approach also helps mitigate design risks, thanks to their early identification.

<span class="mw-page-title-main">Soft robotics</span> Subfield of robotics

Soft robotics is a subfield of robotics that concerns the design, control, and fabrication of robots composed of compliant materials, instead of rigid links. In contrast to rigid-bodied robots built from metals, ceramics and hard plastics, the compliance of soft robots can improve their safety when working in close contact with humans.

<span class="mw-page-title-main">Yoram Koren</span> Israeli-American engineering academic

Yoram Koren is an Israeli-American academic. He is the James J. Duderstadt Distinguished University Professor Emeritus of Manufacturing and the Paul G. Goebel Professor Emeritus of Engineering at the University of Michigan, Ann Arbor. Since 2014 he is a distinguished visiting professor at the Technion – Israel Institute of Technology.

<span class="mw-page-title-main">Silvia Ferrari</span> American aerospace engineer

Silvia Ferrari is an Italian-American aerospace engineer. She is John Brancaccio Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University and also the director of the Laboratory for Intelligent Systems and Control (LISC) at the same university.

A continuum robot is a type of robot that is characterised by infinite degrees of freedom and number of joints. These characteristics allow continuum manipulators to adjust and modify their shape at any point along their length, granting them the possibility to work in confined spaces and complex environments where standard rigid-link robots cannot operate. In particular, we can define a continuum robot as an actuatable structure whose constitutive material forms curves with continuous tangent vectors. This is a fundamental definition that allows to distinguish between continuum robots and snake-arm robots or hyper-redundant manipulators: the presence of rigid links and joints allows them to only approximately perform curves with continuous tangent vectors.

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