Robot-Assisted Disassembly Sequence Planning With Real-Time Human Motion Prediction

Disassembly planning and sequencing play an important role in recycling a fast-growing number of end-of-life products. Optimal sequences can effectively reduce carbon emissions and save natural resources in the remanufacturing industry. Considering the development of intelligent manufacturing technology, this work deals with the optimization problem of selective disassembly sequences with an objective of maximizing disassembly profit. Disassembly sequences are generated based on AND/OR graphs. After setting up an environment matrix based on such graphs, this proposes a Q-learning technique to find an selective optimal disassembly sequence. The algorithm is applied to real-life disassembly cases. Experimental results show that the algorithm is superior a popularly-used genetic algorithm (GA) in both computing speed and solution quality through their various comparisons.

Herein, we present a real-time multi-agent deep reinforcement learning model as a disassembly planning framework for human–robot collaboration. This disassembly plan optimises sequences to minimise operation time and the disassembling costs of end-of-life (EoL) products. Combining different data-driven decision-making tools, the plan aims to handle the complexities and uncertainties of disassembly tasks. Based on the physical features and geometric limitations of EoL product components, we calculate product disassembly difficulty scores. Subsequently, the deep reinforcement learning model integrates these scores into planning process. The model allocates tasks in real time according to the online conditions of the human operator, cobot, and product, enabling the model to cope with uncertainties that may change the process routine. We also present different scenarios wherein a cobot collaborates with human operators with different skill levels. To evaluate model performance, we compare it with baseline models in terms of the convergence time and incorporated disassembly features. The analysis indicates that our model converges three times faster than a baseline model applied to the same case study. Moreover, our model includes more features of the disassembly problem in its decision-making process than any other baseline model.

Disassembly is an essential step for remanufacturing end-of-life (EOL) products. Optimization of disassembly sequences and the utilization of robotic technology could alleviate the labor-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human–robot collaboration. The proposed framework combines three attributes: disassembly cost, safety, and complexity of disassembly, namely disassembleability, to identify the optimal disassembly path and allocate operations between human and robot. A multi-attribute utility function is used to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly which is assumed to be an uncertain parameter with a Beta distribution; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the protection of human workers in the work environment. An example of dismantling a desktop computer is used to show the application. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations among human and robot. A sensitivity analysis is conducted to show the model's performance when changing the disassembly cost for the robot.

This paper addresses the problem of creating intelligent, green, and financially-beneficial disassembly sequences for end-of-life (EOL) electronic products. These complex EOL products contain a broad spectrum of materials including precious metals. Therefore, one would have to process these products to retrieve the value buried in them. EOL processing options include, reuse, remanufacturing, recycling or proper disposal. Each of this option requires a certain level of disassembly. Hence, obtaining an optimal or near optimal disassembly sequence is crucial to increasing the efficiency of EOL processing. Since the complexity of determining the best disassembly sequence increases as the number of parts in a product grows, an efficient methodology is required for disassembly sequencing. In this paper, we present an evolutionary algorithm for generating near-optimal and/or optimal sequences for selective disassembly of EOL products. A numerical example is provided to demonstrate the functionality of the algorithm.

The representation of the disassembly sequence is a key issue in maintenance planning. It involves a highly constrained combinatory problem, which is coupled with varying start and end nodes. These start and end nodes are dependent upon the nature of a maintenance task. In this paper, different representation and modelling schemes for disassembly sequence planning are first reviewed. Then, a novel representation scheme for disassembly sequence, which is generic and can be used to represent both the geometrical and precedence constraints dynamically in product disassembly, is proposed. Based on such a representation scheme, the process for the determination of possible disassembly sequences can be simplifled. By taking into consideration disassembly constraints, the optimal disassembly sequence in relation to the component to be maintained (target component) can be quickly derived. This is achieved by pruning the search space of disassembly sequences, grouping related components into subassemblies, and identifying free components to facilitate disassembly operations. Subsequently, the optimal disassembly sequence in relation to the target component can be obtained using genetic algorithms. In this manner, the disassembly sequences for a complex product comprising a relatively large number of components can be derived within a short time. A case study is used to illustrate the effectiveness of the representation scheme. Comparisons are made using the same case study with the AND/OR graph representation and the Petri net approach for disassembly sequence planning. The results show that the proposed representation scheme is simpler and is more efficient than the rest.

A sound disassembly Petri net model for the effective planning of disassembly processes and tasks is outlined. Owing to the unmanageable complexity associated with modelling of the disassembly processes and tasks, it becomes essential to have a more powerful Petri net model developed by incorporating the concepts of expert system, knowledge representation techniques, etc. Disassembly task planning at high and low levels can easily be represented by proposed high- and low-level expert Petri net. An algorithmic approach is also suggested for evaluating the end-of-life values of a product. These values are used to determine an optimal disassembly sequence and it is incorporated in the expert disassembly Petri net. A proposed expert enhanced high-level coloured disassembly Petri net is empowered to express such details vividly. The application of the proposed expert enhanced high-level coloured disassembly Petri net model is demonstrated through the sample disassembly of a flashlight.

Future automation systems are likely to include devices with a varying degree of autonomy, as well as advanced algorithms for perception and control. Human operators will be expected to work side by side with both collaborative robots performing assembly tasks and roaming robots that handle material transport. To maintain the flexibility provided by human operators when introducing such robots, these autonomous robots need to be intelligently coordinated, i.e., they need to be supported by an intelligent automation system. One challenge in developing intelligent automation systems is handling the large amount of possible error situations that can arise due to the volatile and sometimes unpredictable nature of the environment. Sequence Planner is a control framework that supports the development of intelligent automation systems. This paper describes Sequence Planner and tests its ability to handle errors that arise during execution of an intelligent automation system. An automation system, developed using Sequence Planner, is subjected to a number of scenarios where errors occur. The error scenarios and experimental results are presented along with a discussion of the experience gained in trying to achieve robust intelligent automation.

PurposeThe purpose of this paper is to propose a framework to identify all the feasible disassembly sequences for a multi‐component product and to find an optimal disassembly sequence, according to specific criteria such as cost, duration, profit, etc.Design/methodology/approachTaking into account topological and geometrical constraints of a product structure, an AND/OR disassembly graph is built. Each graph node represents a feasible subassembly. Two nodes i and j are connected by an arc (i, j), called a transition, if the subassembly j can be obtained from the subassembly i by removing one or several connectors. Constraint programming approach is used to generate the feasible subassemblies and related transitions.FindingsIf a cost zij is incurred to perform a transition (i, j), an optimal disassembly sequence can be generated for a given subassembly, using the shortest path algorithm or a linear programming model.Research limitations/implicationsThe proposed approach performs very well compared to other approaches published in the literature, even when applied to products requiring parallel disassembly and including a large number of parts.Practical implicationsThis approach has been successfully applied to assess the wheelchair maintainability at the design stage and will be implemented in CAD systems. One other application, regarding the disassembly process and total revenue maximization for product recycling, is now under consideration.Originality/valueApplying constraint programming to efficiently generate the set of the feasible subassemblies constitutes the main contribution in this paper. This process is the hardest step in the disassembly sequencing problem.

Selection of an optimal disassembly sequence is essential for the efficient processing of a product at the end of its life. Disassembly sequences are listings of disassembly actions (such as the separation of an assembly into two or more subassemblies, or removing one or more connections between components). Disassembly takes place in remanufacturing, recycling, and disposal with a disassembly line being the best choice for automation. In this paper, the disassembly sequencing problem is solved for a cell phone case on a disassembly line, seeking a sequence which is feasible, minimizes the number of workstations (and hence idle times), provides for early removal of high demand/value parts, provides the removal of parts that lead to the access of greatest number of still-installed parts, and early removal of hazardous parts as well as for the grouping of parts for removal having identical part removal directions. Since finding the optimal sequence is computationally intensive due to factorial growth, a heuristic method is used taking into account various disassembly-specific matters. Using the experimentally determined precedence relationships and task times of a real-world cell phone, a MATLAB program is designed and a sequencing solution is generated. Finally, Design for Disassembly (DFD) improvements are recommended with respect to environmentally conscious manufacturing.

Using humans and robots working together to disassemble end-of-life (EoL) products so that they can be remanufactured enables catering for the effects of uncertainties in the condition of those products and unpredictability in remanufacturing operations. Collaborative robots are one of the key elements of Industry 4.0. This paper presents a strategy for human-robot collaborative disassembly that exploits the active compliance control capability of collaborative robots. The paper outlines the proposed human-robot collaboration strategy and active compliance control. It then presents a case study of robotic disassembly designed to show a practical implementation of the proposed strategy to take products apart. The study involved the dismantling of an automotive water pump by a collaborative industrial robot working with a human operator and by two collaborative robots and a human operator. Active compliance control was implemented to separate press-fitted components from the water pump. The results demonstrate the feasibility of the strategy and its effectiveness at increasing flexibility and adaptability by reducing setup efforts.

Humans exhibit outstanding learning, planning and adaptation capabilities while performing different types of industrial tasks. Given some knowledge about the task requirements, humans are able to plan their limbs motion in anticipation of the execution of specific skills. For example, when an operator needs to drill a hole on a surface, the posture of her limbs varies to guarantee a stable configuration that is compatible with the drilling task specifications, e.g. exerting a force orthogonal to the surface. Therefore, we are interested in analyzing the human arms motion patterns in industrial activities. To do so, we build our analysis on the so-called manipulability ellipsoid, which captures a posture-dependent ability to perform motion and exert forces along different task directions. Through thorough analysis of the human movement manipulability, we found that the ellipsoid shape is task dependent and often provides more information about the human motion than classical manipulability indices. Moreover, we show how manipulability patterns can be transferred to robots by learning a probabilistic model and employing a manipulability tracking controller that acts on the task planning and execution according to predefined control hierarchies.

Human-robot collaboration (HRC) on assembly lines enhances productivity and human operators' well-being by enabling humans and robots to perform tasks independently or collaboratively in shared workspaces. Aligning with the human-centric vision of Industry 5.0, this study addresses the human-centered assembly line balancing problem with human-robot collaboration (HCALBP-HRC). A multi-objective mixed-integer linear programming (MILP) model is proposed to minimize cycle time, cognitive load, and ergonomic risk through effective task allocation and resource management. Experiment results of three groups of instances with different production scenarios verified the feasibility of the MILP model. The analysis of trade-offs among the three objectives under varying weight configurations highlights the conflicts between production efficiency and human-centered considerations, underscoring the importance of balanced decision-making in HRC-based assembly systems.

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation.

This work presents a constraint satisfaction problem (CSP) model for the planning and scheduling of disassembly and assembly tasks when repairing or substituting faulty parts. The problem involves not only the ordering of assembly and disassembly tasks, but also the selection of them from a set of alternatives. The goal of the plan is the minimization of the total repairing time, and the model considers, apart from the durations and resources used for the assembly and disassembly tasks, the necessary delays due to the change of configuration in the machines, and to the transportation of intermediate subassemblies between different machines. The problem considers that sub-assemblies that do not contain the faulty part are nor further disassembled, but allows non-reversible and parallel repair plans. The set of all feasible repair plans are represented by an extended And/Or graph. This extended representation embodies all of the constraints of the problem, such as temporal and resource constraints and those related to the selection of tasks for obtaining a correct plan.