There is growing interest for manufacturing enterprises to embrace the drivers of the Smart Industry paradigm. Among these drivers, human–robot physical co-manipulation of objects has gained significant interest in the literature on assembly operations. Motivated by the requirement for human dyads between the human and the robot counterpart, the study of task allocation in human-robot collaboration literature shows promising trends for the implementation of ergonomics in collaborative assembly.
1. Task Allocation
Task allocation in manufacturing is the problem of evaluating and assigning operations to existing resources within the most feasible sequence that improves economic performance and social benefits
[1][41]. Traditionally, the division of tasks between the active resources of the production line was based on fixed rules, and both humans and robots performed high-frequency repetitive operations
[2][26]. The allocation of tasks between the human and the robot primarily aims to follow the criteria that satisfy the respective capabilities of the individual resources. Attention must be given to the kind of resources used based on their competence and capabilities
[3][39]. At a deterministic level, HRC deals with the paradigm of shared sequential task execution between the human and the robot. As shown in
Figure 15, a task sequence comprising
n = five operations is distributed between the human (H) and the robot (R). The human performs tasks k
2, k
3 and k
4, while the robot performs tasks J
1, and J
5.
Figure 15. Task allocation between the human and robot.
Current shared industrial workplaces bring numerous uncertainties that cannot be anticipated with rigid automation. Co-operation-based assembly through task sharing using human intelligence for decision making and robots for accurate execution is critical for workload planning in the production environment
[4][5][42,43]. Task allocation problems in assembly, commonly known as the assembly line balancing problem (ALBP), emerge when the assembly process must be redesigned based on optimization criteria for the proper re-assigning of tasks
[3][6][39,44].
Several modeling tools are available for solving the task sequence problem. Difficulty score sheets in design for assembly (DFA) were used in
[7][8][24,45] to provide a good understanding of the attributes that affect the human–robot task assignment. The concept of dynamic function allocation was studied in
[9][46] to resolve the problem of an unbalanced workload by changing the levels of human/machine controls over system functions, which lead into more situational awareness of human factors in automation. The authors in
[10][47] proposed the disassembly sequence planning model that is capable of minimizing the disassembly time without violating the human safety and the resources constraints.
In solving the task allocation problem in the design phase, the authors in
[11][48] used the nominal schedule to best distribute the work among actors. Following an AND/OR graph, the scheduler is capable of allocating the most suitable task for each actor to execute at each point in time, whereby human expertise is exploited to improve the collaboration. While most studies consider the single human–robot collaborative system, Liau and Ryu
[12][49] studied two different HRC modes, namely, multi-station and flexible modes. Through simulation, they proposed a three-level task allocation model to improve the cycle time, human capability and ergonomic factors.
Beyond the suitability of the task distribution strategy, the capability of the HRC system and the optimization of the assembly cycle time and line balancing can also be studied if an appropriate task allocation model demonstrates sufficient levels of situational awareness. This assigns adjustable roles to the active resources in a manner that determines the limits of acceptable physical work requirements. Furthermore, the design of task allocation that ignores the human factors can lead to economic costs associated with health damage and the loss of productivity due to absenteeism
[13][16].
2. Ergonomics in Collaborative Assembly
The requirement of integrating the human factors in operations involving manual material handling has become a growing trend in research. In the effort to involve human analysis in collaborative work design, ergonomics focuses on the human physical and cognitive characteristics and describe the science of designing appropriate working conditions
[6][44]. There are two key aspects considered below: occupational health and safety.
3. Intelligent Controllers
Assembly operations that involve humans can be characterized by the random and uncertain behavior of the agents involved. This leads to unpredictable changes in the occurrence of events over time. In this probabilistic context, the collaborative state must be continuously integrated into the system’s response in terms of both what to execute and when to execute it.
4. Optimization Techniques
Manual operations cannot satisfy the demand for repeated human movements under load in collaborative assembly. Therefore, mathematical models could provide guidelines for making effective decisions within the current insufficient knowledge of the shared assembly tasks. Indeed, assembly task planning can be categorized as a particular optimization problem. One of the challenges in collaborative operations is the minimization of the cycle time, irrespective of the variability of the manual processing time in executing the assembly tasks as compared to automation
[14][96]. When it comes to manufacturing, the first problem is concerned with task allocation and modeling, for which mathematical models and computer languages can provide the quantitative description of tasks to be performed
[1][41]. The optimization of task allocation considering various modalities such as the part geometry, robot model and kinematics, as shown in
Figure 29.
Figure 29. Optimization planning of task allocation for human–robot collaboration. Multi-criteria optimization for minimizing the human fatigue, energy consumption and path and maximizing efficiency.
Adding new constraints to a single-board problem based on constraint programming (CP) is easier to develop and more readable when compared to mixed-integer linear programming (MILP)
[15][97]. The authors’ comparison between MILP and CP revealed that CP offers a superior computational performance for the ALBP of printed circuit boards comprising between 60 and 200 tasks. The authors in
[16][98] proposed an online estimation of the quality of interaction between a human and a robot. Through the computation of fluency metrics, the authors measured the contribution of the human to the interaction. Zhang, Lv
[2][26] used reinforcement learning to optimize the task sequence allocation in the HRC assembly process. A visual interface displays the assembly sequence to the operators to obey the decision of the human agent. The authors in
[17][23] evaluated multiple criteria such as resources availability, suitability and processing time, which they integrated with a modular framework where the individual agents communicate over an ROS-based architecture.
Weckenborg, Kieckhäfer
[18][22] developed a genetic algorithm to minimize the assembly lines’ cycle times for a given number of stations with collaborative robots. Stecke and Mokhtarzadeh
[19][61] presented a use case of an assembly task of a base shaft module to demonstrate the impact of robot mobility on the performance of a hybrid assembly line. The authors used an energy expenditure model to analyze the advantages of collaborative robots in assembly lines. A combination of mixed-integer programming, constraint programming and a bender decomposition algorithm reveals that the configuration for equipping an assembly line with a robot is best when the ratio of robots over the station is near 0.7, with 37% of mobile robots.
The implementation of a multi-modal interface for the fusion of different communication methods such as voice and gesture commands is well reported for the robust human–robot control architecture in manufacturing systems
[20][21][22][5,27,93]. However, solutions based on speech recognition face numerous limitations such as the noise in the environment that is characteristic of a real production line. In summary, human and robot characteristics are often considered similar from mathematical and computer modeling perspectives
[23][29]. A summary of the optimization methods reported in the literature for assembly task planning in HRC is captured in
Table 13.
Beyond the computational complexity of mathematical modeling approaches and the use of cumbersome data acquisition methods such as direct EMG signals, alternative solutions to assembly systems design such as the virtualization
[24][87] and visualization
[25][99] of manufacturing processes have emerged in recent years. These tools have received growing interest in improving the product design for collaborative assembly for their non-reliance on physical set-ups and the associated reduction in safety risks.
Table 13. Optimization methods for HRC in the recent literature.
Year |
Ref. |
Description/Title |
ALBP |
AD |
MM |
OT |
Key Feature |
2018 |
[26][25] |
Robot adaptation to human physical fatigue in human–robot co-manipulation |
|
|
✔ |
DMP |
Proposes a new human fatigue model in HRC based on the measurement of EMG signals. |
2019 |
[27][55] |
Sequence Planning Considering Human Fatigue for Human–Robot Collaboration in Disassembly |
✔ |
✔ |
✔ |
DBA |
Solved the sequence planning considering human fatigue in human–robot collaboration using a bee algorithm. |
2019 |
[28][31] |
A selective muscle fatigue management approach to ergonomic human–robot co-manipulation |
|
|
✔ |
ML |
Performed experiments on two different HRC tasks to estimate individual muscle forces to learn the relationship between the given configuration and endpoint force inputs and muscle force outputs. |
2020 |
[29][100] |
Mathematical model and bee algorithms for the mixed-model assembly line balancing problem with physical human–robot collaboration |
✔ |
✔ |
✔ |
MILP BA ABC |
The authors presented a mixed-model assembly line balancing problem using a combination of MILP, BA and ABC algorithms. To this end, the proposed model and algorithm offer a new line design for increasing the assembly line efficiency. |
2020 |
[30][101] |
Bound-guided hybrid estimation of the distribution algorithm for energy-efficient robotic assembly line balancing |
✔ |
✔ |
✔ |
BGS |
The authors proposed a bounded guided sampling method as a multi-objective mathematical model for solving the problem of the energy efficiency of robotic assembly line balancing. |
2020 |
[15][97] |
Scheduling of human–robot collaboration in the assembly of printed circuit boards: a constraint programming approach |
✔ |
✔ |
✔ |
MILP CP |
A comparison between MILP and CP reveals that CP offers a superior computational performance for ALBP, comprising between 60 and 200 task. |
2020 |
[18][22] |
Balancing of assembly lines with collaborative robots |
✔ |
✔ |
✔ |
MILP GA |
The authors developed a genetic algorithm to minimize the assembly lines’ cycle times for a given number of stations with collaborative robots. |
2021 |
[19][61] |
Balancing collaborative human–robot assembly lines to optimize the cycle time and ergonomic risk |
✔ |
✔ |
✔ |
MILP CP BD |
Human–robot collaboration was studied for sensitivity analysis. MILP, CP and BD algorithms were developed to analyze the benefits of human–robot collaboration in assembly lines. To this end, regression lines can help managers determine how many robots should be used for a line. |
2022 |
[2][26] |
A reinforcement learning method for human–robot collaboration in assembly tasks |
✔ |
✔ |
✔ |
RL |
The use of reinforcement learning to optimize the task sequence allocation in the HRC assembly process. A visual interface displays the assembly sequence to the operators to obey the decision of the human agent. |
2022 |
[31][13] |
A dynamic task allocation strategy for mitigating the human physical fatigue in collaborative robotics |
✔ |
✔ |
✔ |
DNN |
A non-intrusive online fatigue algorithm that predicts the joint muscle activation associated with the human motion. The estimation process allocates the task activities based on a sophisticated musculoskeletal model and a 3D vison system that tracks the human motion in real time. |
2022 |
[32][12] |
Development of an integrated virtual reality system with wearable sensors for the ergonomic evaluation of human–robot cooperative workplaces |
|
|
✔ |
|
Ergonomic analysis strategy of humans in the loop virtual reality technology. The system uses a mixed- prototyping strategy involving a VR environment, computer–aided design (CAD) objects, wearable sensors and human subjects. |