In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guided Vehicle (SGV), we illustrate the direct influence of the motion planning stage on global energy consumption by means of several simulations and experiments. The results indicate that the reaction of the SGV in response to unforeseen obstacles can affect the amount of energy consumed. Hence, energy constraints must be considered when developing the motion planning of AWMRs.

This paper presents the German-inspired education model called Dual, as pioneered by l'Université du Québec à Trois-Rivières (Drummondville Campus) for its mechanical engineering undergraduate degree. Focus is on the program structure itself and the ongoing research project which aims to determine and measure outcomes of this novel approach from the student, industry-supervisor and professor perspectives’, specifically pertaining to their respective experience, impact on academic performance, employability, and the development of work-related skill sets. The methodology is set forth in this paper and results from similar experiences across Europe are presented. Qualitative and quantitative data for this specific project are still being collected and analyzed. The proposed model for this research stems from studies on active, experiential and work-integrated Learning. Motivated by a desire to better prepare engineering students for the ever-changing realities of the profession and as a response to the shortage of qualified personnel faced by the industrial sector, the program combines parallel on- and off-site learning over a consecutive two-year period. This differs from the more commonly known co-op structure where students alternate between study terms and work terms. With the proposed dual model, students complete the program within 4 years earning 24 credits, of a total 120, through work-integrated learning directly at the workplace (off-site). Courses completed jointly at the school and in partnership with the company include a 9-credit capstone design project and two 3-credit complementary courses, where reflective journaling and mentorship are central to both. In addition, three 4-month internships are also completed. Since June 2021, the program is accredited by Engineers Canada.

To protect workers, it is necessary to characterize the noise and vibration emissions value of nailers. Standardized characterization methods exist but require three trained human operators, which leads to a dispersion in the results and a difficult implementation. An automatic test stand (ATS) was developed to characterize those values without the participation of human operators. It is proposed here that we compare the results obtained by the two methods. Preliminary results suggest that further refinement of the ATS is needed to better mimic the biodynamics of the human hand–arm and that a larger number of operators would be required for validation.

In this paper, we propose the use of a neural network to identify lateral skidding events of road vehicles used during winter driving conditions. Firstly, data from a simulation model was used to identify the essential vehicle dynamics variables needed and to create the network structure. Then this network was retrained to classify real-world vehicle skidding events. The final network consists of a 3 layer network with 10, 5 and 1 output neurons 13 inputs, 4 outputs and a 5 step time delay. The retrained network was used on a limited set of real vehicle data and confirmed the effectiveness of the network classifying lateral skidding events.