Aiming at the partial disassembly of discarded products,by considering the increase in operation time caused by disassembly interference between parts during the disassembly process,a partial disassembly line balan-cing problem with disassembly interference between components was studied,and a multi-objective mathematical model was established with the objectives of minimizing the smoothing index and energy consumption while maximi-zing disassembly profits from the perspectives of efficiency,energy consumption,and profitability.To address this problem,an Improved Moth-flame Optimization algorithom(IMFO)based on Pareto dominance theory was pro-posed.A suitable mechanism of encoding and decoding was designed according to the characteristics of the problem,which could save storage space and simplify the optimization operator.To enhance the search depth and breadth of moths in the solution space,the local search and global exploration strategies with sequentially changing neighbor-hood structures were designed.The method based on the independent contribution to hyper volume was introduced to screen the Pareto optimal solution.Finally,the effectiveness of the proposed model and algorithm was validated through various types of numerical examples and case studies.The processes of different disassembly schemes were compared,and the changes in energy consumption and profit with respect to disassembly depth were analyzed.
Aiming at the issue of low path planning efficiency for Automated Guided Vehicle (AGV) facing mixed U-shaped and dynamic obstacles, an adaptive proximal distillation evolutionary reinforcement learning algorithm guided by intrinsic reward policy (IRPA-PDERL). Initially, Random Network Distillation is introduced as intrinsic rewards in the fitness function, which is enhanced the policy of diversity of elite policy evaluation. Secondly, an adaptive intrinsic reward weight factor α is designed to balance the algorithm on the capacity to exploration and exploitation, aiding AGV in selecting the optimal strategy in highly uncertain dynamic obstacle environments. The decreased parameter sensitivity results from optimizing the covariance parameters of the mutation operator by covariance matrix adaption evolution strategy. Comparing with multiple algorithms on three different environments, the experimental results show that the improved algorithm reduces paths by 12.65
Autonomous shoveling of loaders is the key technology to realize automatic and intelligent operation,and the tracking control of the target working trajectory is one of its core parts.The actual trajectory of the bucket in the pile is related to the indicators such as the operation output,so it is of great significance to realize the effective track-ing control of the target working trajectory.The PID and other control methods without system models have prob-lems such as large overshoot amplitude and buffeting under the system constraints.Since the Model Predictive Con-trol(MPC)has the advantage of effectively dealing with system constraints to make the system operate smoothly,it was introduced into the motion control of the loader's working mechanism and a trajectory-tracking control method was proposed for the working mechanism based on the Nonlinear Model Predictive Control(NMPC).A kinematic model of the working mechanism in the drive space was established.Then,the description of the working trajectory was given.Furthermore,a trajectory-tracking controller for the working mechanism was designed based on the NMPC method.Finally,the Simulink/ADAMS co-simulation was carried out with the general PID as the compari-son group.The analysis showed that under the same system constraints,for the different target trajectories,the maximum absolute error of the bucket-tip displacement based on the designed controller didn't exceed±0.052m,which was 71％lower than the PID controller,and the maximum absolute error of the bucket angle didn't exceed±2.58°,which was 16％lower than the PID controller.Moreover,the designed controller had a smoother control effect.The designed controller had better performance than the PID controller in dealing with system constraints and smoothness.
In the short term, a fully autonomous level of machine intelligence cannot be achieved. Humans are still an important part of HCI systems, and intelligent systems should be able to “feel” and “predict” human intentions in order to achieve dynamic coordination between humans and machines. Intent recognition is very important to improve the accuracy and efficiency of the HCI system. However, it is far from enough to focus only on explicit intent. There is a lot of vague and hidden implicit intent in the process of human–computer interaction. Based on passive brain–computer interface (pBCI) technology, this paper proposes a method to integrate humans into HCI systems naturally, which is to establish an intent-based HCI model and automatically recognize the implicit intent according to human EEG signals. In view of the existing problems of few divisible patterns and low efficiency of implicit intent recognition, this paper finally proves that EEG can be used as the basis for judging human implicit intent through extracting multi-task intention, carrying out experiments, and constructing algorithmic models. The CSP + SVM algorithm model can effectively improve the EEG decoding performance of implicit intent in HCI, and the effectiveness of the CSP algorithm on intention feature extraction is further verified by combining 3D space visualization. The translation of implicit intent information is of significance for the study of intent-based HCI models, the development of HCI systems, and the improvement of human–machine collaboration efficiency.
Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.
