Powered prostheses are effective for helping amputees walk on level ground, but these devices are inconvenient to use in complex environments. Prostheses need to understand the motion intent of amputees to help them walk in complex environments. Recently, researchers have found that they can use vision sensors to classify environments and predict the motion intent of amputees. Previous researchers can classify environments accurately in the offline analysis, but they neglect to decrease the corresponding time delay. To increase the accuracy and decrease the time delay of environmental classification, we propose a new decision fusion method in this paper. We fuse sequential decisions of environmental classification by constructing a hidden Markov model and designing a transition probability matrix. We evaluate our method by inviting ablebodied subjects and amputees to implement indoor and outdoor experiments. Experimental results indicate that our method can classify environments more accurately and with less time delay than previous methods. Besides classifying environments, the proposed decision fusion method may also optimize sequential predictions of the human motion intent in the future.
Purpose
This paper aims to realize the automatic assembly process for multiple rigid peg-in-hole components.
Design/methodology/approach
This paper develops fuzzy force control strategies for the rigid dual peg-in-hole assembly. Firstly the fuzzy force control strategies are presented. Secondly the contact states and contact forces are analyzed to prove the availability of the force control strategies.
Findings
The rigid dual peg-in-hole assembly experimental results show the effectiveness of the control strategies.
Originality/value
This paper proposes fuzzy force control strategies for a rigid dual peg-in-hole assembly task.
Purpose
This paper aims to present an optimization algorithm combined with the impedance control strategy to optimize the robotic dual peg-in-hole assembly task, and to reduce the assembly time and smooth the contact forces during assembly process with a small number of experiments.
Design/methodology/approach
Support vector regression is used to predict the fitness of genes in evolutionary algorithm, which can reduce the number of real-world experiments. The control parameters of the impedance control strategy are defined as genes, and the assembly time is defined as the fitness of genes to evaluate the performance of the selected parameters.
Findings
The learning-based evolutionary algorithm is proposed to optimize the dual peg-in-hole assembly process only requiring little prior knowledge instead of modeling for the complex contact states. A virtual simulation and real-world experiments are implemented to demonstrate the effectiveness of the proposed algorithm.
Practical implications
The proposed algorithm is quite useful for the real-world industrial applications, especially the scenarios only allowing a small number of trials.
Originality/value
The paper provides a new solution for applying optimization techniques in real-world tasks. The learning component can solve the data efficiency of the model-free optimization algorithms.
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