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Cobots (Collaborative Robots), as a type of industrial robot, have helped overcome the barriers to human-robot collaboration, freeing robots from the constraints of safety guards or cages. Their groundbreaking performance and wide range of applications have ushered in a new era for the development of industrial robotics.
What Are Cobots?
Unlike traditional industrial robots, Cobots (Collaborative Robots) can share a workspace with humans without safety fences, thanks to advanced sensors, intelligent control algorithms, and flexible drive technologies. They are capable of perceiving and responding to changes in their external environment. When an abnormal situation is detected—such as contact with a human or other obstacles—the cobot can automatically slow down or stop to ensure safety. This design philosophy enables cobots to perform complex tasks without compromising human safety.
Cobots typically feature flexible mechanical structures and simplified programming methods, allowing for faster and easier deployment on production lines. With graphical programming interfaces or teach-by-demonstration programming, operators can manually guide the robot to complete specific tasks without the need for complex code writing. This not only lowers the barrier to entry but also enables production lines to adapt quickly to product changes.
How Do Cobots Work
| Robot Awareness: Cobots (Collaborative Robots) need the ability to perceive their surrounding environment—a process analogous to human subconscious perception. The robot perceives its surroundings through built-in sensors and, after collecting relevant data, processes and analyzes it using algorithms such as deep learning. This data includes information about the distance, direction, and speed of objects relative to the robot, enabling it to respond promptly to the external environment.
| Robot Thinking: Beyond awareness, collaborative robots require further analysis and judgment, which necessitates a certain level of “intelligence.” Cobots can leverage autonomous learning algorithms to continuously accumulate experience. By autonomously learning and continuously optimizing their algorithmic models, they can make decisions in an optimal manner. For certain specific tasks, robots can also utilize existing “knowledge bases” to make rule-based decisions.
| Robot Action: Once a robot possesses awareness and thinking capabilities, it can take corresponding actions based on its judgment of the surrounding environment and the task at hand. While working alongside humans, the robot can adapt to the human working environment through appropriate posture adjustments. Cobots can also adjust their paths, positions, etc., according to task requirements, to ensure successful task completion.
| Human-Robot Collaboration: The most important feature of collaborative robots is their close cooperation with humans. By designing appropriate collaboration strategies, robots and humans can coordinate effectively. Through interaction with humans, robots can gain more information, understand human needs and instructions, and respond with corresponding actions. This better ensures the quality of task completion.
Types of Cobots (Collaborative Robots)
By Structural Configuration
Cobots can be divided into single-arm and dual-arm collaborative robots.
| Single-arm cobots: Have only one arm, the most common form of robotic arm. Due to their flexible deployment and simple control, they are widely used in various industry scenarios.
| Dual-arm cobots: Possess redundant workspace, offering high efficiency and flexibility. They are suitable for applications requiring interactive collaboration or completing multiple actions in a limited space, such as the coordinated action of screwing a bottle cap with two hands.
By Number of Axes/Joints
| 6-axis cobots: Possess full degrees of freedom, enabling them to reach any point in space, offering good versatility.
| 7-axis cobots: When obstacles exist in the robot’s workspace between the target point and the robot base, a standard 6-axis arm may not be able to bypass the obstacle to achieve the desired pose at the target point. In such cases, a 7-axis cobot is needed.
| Limited-DOF cobots (<6 axes): For specific tasks involving simple, repetitive functions, a 6-axis robot is not always necessary. For applications like industrial sorting, spraying, or inspection, limited-DOF cobots are sufficient.
By Payload Capacity
| Light-duty cobots: Typically have a payload of 3-10kg. They are the most common type, mainly used in consumer electronics, testing, service, etc.
|Heavy-duty cobots: Typically have a payload above 10kg. Due to application scenarios and structural characteristics, cobot payloads generally do not exceed 20kg. They are often used in assembly, material handling, palletizing, and packaging.
| Desktop cobots: Typically have a payload of less than 3kg. They are mainly used in applications like sorting, dispensing, and gluing.
Cobot Collaboration Modes
The International Federation of Robotics (IFR) classifies human-robot collaboration into four modes based on the level of collaboration and requirements for internal safety features and external sensors. The mainstream modes for cobots currently include:
| Coexistence: Humans and robots work in the same workspace but their work areas are separated. There is no direct contact or interaction. For example, in a factory workshop, a robot performs automated production in a fixed area while a human worker performs other operations in an adjacent area, with physical separation or a safety distance between them.
| Sequential Collaboration: Humans and cobots share part or all of the workspace but do not work on the part or machine simultaneously. They complete tasks sequentially; one finishes a part of the work, and the other proceeds with subsequent operations. For example, on an assembly line, a robot first completes the initial assembly of components, followed by a human worker performing fine adjustments or quality inspections.
| Cooperation: The cobot and human work simultaneously on the same part or task, requiring close coordination. This mode demands high safety and flexibility from the robot. For example, in automotive manufacturing, a robot and a human worker collaborate on body welding or parts installation, with the robot handling highly repetitive tasks and the human handling tasks requiring judgment and adjustment.
| Responsive Collaboration: The cobot responds in real-time to human actions and commands, dynamically adjusting its behavior based on human operations to achieve closer collaboration. This mode requires advanced perception and intelligent control capabilities from the robot and is typically used for tasks needing high flexibility and precise coordination.
Components of Cobots (Collaborative Robots)
| Servo Joints: Servo joints are the power units of a cobot. They typically use an integrated design, incorporating the driver into the joint, making each joint a control unit. A servo joint comprises six parts: a harmonic drive, a torque motor, sensors, a driver, a brake, and a housing. The specific composition is shown in the following figure: [Link Placeholder]
| Link Housing and Safety Skin
Link Housing: The link housing refers to the upper and lower shells of the cobot, used to connect the servo joints in series to form the overall power transmission and robot configuration. Configuration here refers to the number of degrees of freedom. Cobots typically have 6 or 7 degrees of freedom, hence the terms 6-axis or 7-axis robots. The link housing is usually designed with smooth, rounded edges and no sharp corners to ensure safety during human-robot collaboration.
Safety Skin: Some cobots are equipped with a safety skin on their surface to enhance safety. This technology is typically based on capacitive or inductive proximity sensors, or tactile sensing skin technology covering the robot’s surface, enabling real-time perception of the robot’s surroundings. Even during high-speed operation, if an obstacle is detected, the robot can perform an emergency stop in a very short time to avoid collision.
| Control Cabinet and Teach Pendant
Control Cabinet: The control cabinet acts as the “brain” for the robot’s high-level control. It typically houses a high-performance industrial PC (such as a compact industrial PC or an NUC) to handle computationally intensive high-level tasks. Correspondingly, the servo drivers mentioned earlier, due to size and power constraints, usually handle more basic, low-level computations. The control cabinet also integrates power modules to convert 220V AC power to the 48V or 24V DC power required by the servo joints, along with power safety modules to ensure the electromagnetic compatibility and electrostatic discharge protection of the entire robot’s electrical system.
Teach Pendant: The teach pendant allows direct user interaction. Users can operate and control the robot directly via the teach pendant without needing a PC. Teach pendants typically feature highly encapsulated graphical programming interfaces and well-designed human-machine interfaces to enhance user-friendliness. Furthermore, teach pendants are generally required to have a red emergency stop button integrated, providing the highest priority braking in case of an emergency to enhance operational safety.
| End-Effector: In practical applications, appropriate end-effectors are integrated at the robot’s末端 to enhance its manipulation and grasping capabilities. End-effectors generally appear in three forms: vacuum grippers/suction cups, grippers, and dexterous hands.
Advantages of Cobots over Traditional Robots
| Leveraging Human-Robot Collaboration: The biggest breakthrough of cobots is their ability to work alongside humans directly without safety fences. This not only reduces the distance between human and robot, significantly decreasing the footprint of the workstation, but more importantly, it fully combines the strengths of humans and machines. Robots assist humans in completing highly repetitive, high-precision tasks, while humans handle tasks requiring flexibility and continuous optimization.
| Lower Cost: The average price of a cobot is often 75% to 85% of that of a comparable traditional industrial robot, offering a clear cost advantage and thus a shorter return on investment period. Deployment costs are also lower, as cobots don’t require safety fences or complex isolation facilities. They have a small footprint, are easy to install, and can be quickly deployed on existing production lines. Programming and training costs are lower as well; cobots use graphical programming or drag-and-teach methods, allowing regular workers to operate them after short training sessions.
| High Safety: Cobots achieve high safety through various sensors (e.g., force/torque sensors, vision sensors, proximity sensors), precise force control algorithms, and lightweight structural designs. Even if contact with a person occurs, the impact force is limited. It’s worth mentioning that the design and manufacturing of cobots strictly adhere to international safety standards (such as ISO/TS 15066). Compliance with these standards ensures their safety and reliability globally, providing users with dependable safety assurance.
Disadvantages of Cobots (Collaborative Robots)
| Lower Speed: Cobots must ensure safety during close collaboration with humans, imposing strict limits on speed and force. To prevent injury to people or the environment in case of collision, their operating speed is typically limited to a lower level, generally 1/3 to 1/2 that of traditional industrial robots. Additionally, due to their lightweight design, speeds must be limited during high-speed operation to prevent deformation that could affect accuracy and ensure reliability.
| Lower Stiffness: To achieve safe human-robot collaboration, cobots often use lightweight materials and simplified structures. While this reduces weight and inertia, it also leads to insufficient overall rigidity. During motion, cobots with low stiffness are prone to vibration caused by inertial forces or external disturbances. This can affect trajectory accuracy and increase end-effector positioning errors. For example, noticeable shaking may occur during high-speed motion or rapid start/stop actions.
| Lower Payload: Low stiffness also makes cobots more susceptible to deformation and displacement under load. Consequently, their payload capacity is typically in the 5-20kg range, significantly lower than that of traditional industrial robots. In applications requiring the handling of heavier workpieces or applying significant force, cobots may struggle to maintain a stable posture and accuracy. In tasks demanding high force and precision, such as certain assembly or welding operations, cobots may underperform compared to traditional industrial robots.
| Lower Precision: Due to their lightweight structure and lack of rigidity, cobots have lower absolute position accuracy, making it difficult to meet the demands of tasks requiring extremely high positional precision. During motion, factors like inertia can lead to relatively poorer dynamic accuracy (e.g., trajectory tracking accuracy), potentially causing deviations in the end-effector’s path and affecting operational accuracy. For instance, cobots typically have a repeat positioning accuracy in the range of ±0.03-0.3mm, whereas traditional industrial robots can achieve ±0.01-0.1mm. This means cobots have a relatively larger positional deviation when repeatedly performing the same action.
How Do Cobots Differ From Traditional Industrial Robots
Cobots and traditional industrial robots differ in their advantages, disadvantages, and application scenarios. As robotics technology advances, the convergence and integration of cobots and traditional robots will deepen. However, distinctions in their usage still exist today.
For mass production tasks with high demands on work efficiency and reliability, traditional industrial robots are the primary choice.
For customized, small-to-medium batch production tasks or human-robot hybrid tasks with high demands on human safety and programmability, collaborative robots are often the better choice.
Recommended In-depth Reading from AI Robots Eidos
Cobots and traditional industrial robots are two important directions in the development of robotic technology, and they complement each other and develop synergistically.
Cobots focus on human-robot collaboration, flexible production, and low-threshold deployment, making them suitable for scenarios that require high flexibility and safety. Traditional industrial robots, on the other hand, dominate in high-load, high-precision, and large-scale production.
Interested readers can read this in-depth article about industrial robots.
Basic Workflow of Cobot Motion Control
| Task Decision: The user issues relatively specific commands, for example, instructing the cobot to move an object from point A to point B.
| Trajectory Planning: Based on the specific task command, the controller uses trajectory planning algorithms to generate the motion path for the robot’s end-effector. This path is discretized into several points in space, each mapped with corresponding position information and the robot’s velocity at that point, also involving interpolation between points.
| Inverse Kinematics Solution: The position and velocity of points in Cartesian space from trajectory planning are fed into the inverse kinematics solver, which calculates the joint angles for each robot axis. This process requires establishing the robot’s Denavit-Hartenberg (DH) parameter model beforehand. (Note: DH parameters use four parameters to express the position and angle relationship between two consecutive joint links within a specifically designed coordinate system.)
| Joint PID Control and Motor FOC: The angular velocity (or acceleration) information for each axis, output by the inverse kinematics solver, is sent to the driver of each servo joint. The driver uses PID control algorithms to accurately track these angular velocity/acceleration commands. Simultaneously, it uses Field-Oriented Control (FOC) algorithms to decompose the angular velocity commands into current vector commands for the three-phase motor, controlling the motor’s output speed and torque to support the robot’s motion. (Note: FOC is a technique for controlling three-phase motors using a variable frequency drive, adjusting the output frequency, voltage magnitude, and angle to control the motor’s output.)
Collaborative Robot Applications
| Industrial Field
Automotive Manufacturing: Cobots are used for parts handling, welding, and assembly. On engine assembly lines, cobots precisely install various components onto engines, ensuring accuracy and quality while increasing assembly speed.
Electronics Manufacturing: Cobots are used for inserting electronic components, inspection, and packaging. In mobile phone production, cobots quickly and accurately place tiny electronic components onto circuit boards, improving efficiency and yield.
Metalworking: Cobots are used for grinding, polishing, and cutting parts, performing precise machining based on part shape and surface requirements to improve accuracy and surface quality.
| Commercial Field
Logistics and Warehousing: Cobots are used for goods handling, sorting, and palletizing (such as cobot palletizers). In large warehouses, cobots quickly move goods from storage to sorting areas and perform accurate sorting and palletizing based on order information, greatly improving efficiency.
Retail: Cobots are used for restocking, displaying, and inventorying goods. In supermarkets, cobots can automatically restock and adjust displays based on sales data, improving operational efficiency.
| Scientific Research and Education
Research: Cobots are used in various scientific experiments, such as material testing and biological experiments, precisely controlling experimental conditions and operations to improve accuracy and repeatability.
Education: Cobots are used in courses like robotics programming and engineering practice. By allowing students to operate cobots hands-on, they cultivate innovative thinking and practical skills. In university robotics labs, students use cobots for programming and control experiments, learning about robot principles and applications.
Global Collaborative Robots Market
Globally, the manufacturing industry is undergoing a transformation driven by innovation and energy efficiency. To reduce costs, improve productivity and product consistency, and meet the demands of flexible production, companies are increasingly adopting collaborative robots.
Data from the International Federation of Robotics (IFR) shows that the global collaborative robot market grew from approximately $1.0 billion in 2015 to $10.11 billion in 2024, achieving a compound annual growth rate (CAGR) of 29.4%. This growth trend is mainly attributed to the urgent need for automation upgrades in global manufacturing and the continuous advancement of cobot technology.
According to the latest report “Global Collaborative Robot Market Size, Major Manufacturers, Major Regions, Product and Application Segmentation Report 2026” by Global Info Research, based on revenue, the global collaborative robot market was approximately $1.322 billion in 2025 and is projected to reach $2.778 billion by 2032, with a CAGR of 10.5% from 2026 to 2032. (Note: These data are institutional forecasts and are for reader reference only.)
Future Development Directions for Collaborative Robots
| AI, Machine Learning, and Digital Twins: A key trend is the deep integration of advanced technologies like Artificial Intelligence (AI), Machine Learning, and digital twin simulation. AI-powered cobots show enhanced adaptability to dynamic environments, learning tasks from historical and real-time data to perform more complex actions with less human intervention. Digital twin technology allows manufacturers to simulate robot performance and optimize system behavior before deployment, reducing implementation risks and improving efficiency. These capabilities are enhancing the intelligence, flexibility, and operational efficiency of cobots in smart factories.
| Modularity: Cobot design is increasingly emphasizing modularity and operational flexibility, enabling companies to quickly reconfigure robotic systems for new tasks and changing production needs. This trend includes plug-and-play designs, easily replaceable modules/components, and more user-friendly programming and teaching interfaces. This shortens deployment cycles and lowers the entry barrier for Small and Medium-sized Enterprises (SMEs). Consequently, companies can scale automation faster and at lower cost, even in space-constrained or budget-limited facilities.
| Cloud Connectivity: Cloud-connected cobots enable remote monitoring, predictive maintenance, and performance analysis, helping manufacturers maintain high equipment uptime and proactively respond to potential issues. By integrating cobot data into Manufacturing Execution Systems (MES) or Industrial Internet of Things (IIoT) platforms, companies can further optimize processes, extend equipment life, and enhance operational resilience. This trend aligns with the digital transformation paths across industries and reinforces the role of cobots as key equipment in the smart factory ecosystem.
Insight from AI Robots Eidos about Cobots (Collaborative Robots)
| The future collaborative robots will no longer be mere tools executing commands passively; they will become ‘equal teammates’ to humans. Through deep learning and knowledge sharing, robots will form a ‘co-intelligence’ relationship with humans—humans provide creativity and complex judgments, while robots offer precise execution and real-time data feedback. Both parties will learn from each other and collaboratively optimize workflows.
| Multiple collaborative robots will not operate independently from one another; instead, they will form a ‘robot community’ through the cloud. When one robot learns a new skill (such as a new grasping method), it can immediately share that skill with all other robots in the community, achieving collective evolution that significantly enhances the adaptability of the entire factory.
| The future trend is that robotic functions will be ’embedded’ within the entire smart factory environment—robotic arms may be hidden under workbenches and rise when needed; sensors will be integrated into lighting systems; and control systems will be incorporated into building facilities. Robots will become ‘invisible,’ yet human-robot collaboration will be ubiquitous. This ‘environmental intelligence’ will make collaboration more natural, and humans may not even realize they are working alongside robots, but their work efficiency will be comprehensively enhanced.
Image Credits: Researchgate & Fanucamerica & Robotsdoneright & Zetagroupengineering & Linkedin & Evolutionmotion & Automationmag
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