This paper deals with the problem of navigating semi-autonomous mobile robots without global localization systems in unknown environments. We propose a planning-based obstacle avoidance strategy that relies on local maps and a series of short-time coordinate frames. With this approach, simple odometry and range information are sufficient to make the robot to safely follow the user commands. Different from reactive obstacle avoidance strategies, the proposed approach chooses a good and smooth local path for the robot. The methodology is evaluated using a mobile service robot moving in an unknown corridor environment populated with obstacles and people.

This paper presents a fleet model explained through a complex configuration of load sharing that considers overcapacity and is based on a life cycle cost (LCC) approach for cost-related decision-making. By analyzing the variables needed to optimize the fleet size, which must be evaluated in combination with the event space method (ESM), the solution to this problem would normally require high computing performance and long computing times. Considering this, the combined use of an integer genetic algorithm (GA) and the ant colony optimization (ACO) method was proposed in order to determine the optimal solution. In order to analyze and highlight the added value of this proposal, several empirical simulations were performed. The results showed the potential strengths of the proposal related to its flexibility and capacity in solving large problems with a near optimal solution for large fleet size and potential real-world applications. Even larger problems can be solved this way than by using the complete enumeration approach and a non-family fleet approach. Thus, this allows for a more real solution to fleet design that also considers overcapacity, availability, and an LCC approach. The simulations showed that the model can be solved in much less time compared with the base model and allows for the resolution of a fleet of at least 64 trucks using GA and 130 using ACO, respectively. Thus, the proposed framework can solve real-world problems, such as the fleet design of mining companies, by offering a more realistic approach.

The use of decision-making models in the early stages of the development of complex products and technologies is a well-established practice in industry. Engineers rely on well-established statistical and mathematical models to explore the feasible design space and make early decisions on future design configurations. At the same time, researchers in both value-driven design and sustainable product development areas have stressed the need to expand the design space exploration by encompassing value and sustainability-related considerations. A portfolio of methods and tools for decision support regarding value and sustainability integration has been proposed in literature, but very few have seen an integration in engineering practices. This paper proposes an approach, developed and tested in collaboration with an aerospace subsystem manufacturer, featuring the integration of value-driven design and sustainable product development models in the established practices for design space exploration. The proposed approach uses early simulation results as input for value and sustainability models, automatically computing value and sustainability criteria as an integral part of the design space exploration. Machine learning is applied to deal with the different levels of granularity and maturity of information among early simulations, value models, and sustainability models, as well as for the creation of reliable surrogate models for multidimensional design analysis. The paper describes the logic and rationale of the proposed approach and its application to the case of a turbine rear structure for commercial aircraft engines. Finally, the paper discusses the challenges of the approach implementation and highlights relevant research directions across the value-driven design, sustainable product development, and machine learning research fields.

The sample squared Sharpe ratio (SSR) is a critical statistic of the risk-return tradeoff. We show that sensitive upper-tail probabilities arise when the sample SSR is employed to test the mean-variance efficiency under different test statistics. Assuming the error's normality with a nonzero mean, we integrate the sample SSR and the arbitrage regression into a noncentral chi-square (χ2) test. We find that the distribution of the sample SSR based on the regression error is to the left of the F-distribution when assuming the returns' normality. Compared to two benchmarks that use the noncentral F-distribution and the central F-statistic, the χ2-statistic is more effective, competitive, significant, and locally robust when used to reject the upper-tailed mean-variance efficiency test using the usual parameters (sample size, portfolio size, and SSR).

In order to reduce the time spent on tolerance analysis, it is necessary to correctly identify and prioritize the key characteristics of the product. For multiple-state mechanisms, a systematic procedure for doing this is lacking. We present a new complexity metric for multiple-state mechanisms based on the product behavior, describing the impact of geometrical variation. The sequence of the structural state transitions is linked to the product composition, enabling a clear prioritization of variation-critical states and interfaces. The approach is applied on an industrial case and verified based on a comparison with the company-specified priority tolerance calculations.

Constrained motion is essential for varying robotics tasks, especially in surgical robotics, for instance, the case of minimally invasive interventions. This article proposes generic formulations of the classical bilateral constrained motion (i.e., when the incision hole has almost the same diameter as that of the tool) as well as unilaterally constrained motion (i.e., when the hole incision has a larger diameter compared to the tool diameter). One of the latter constraints is combined with another surgical task such as incision/ablation or suturing a wound (modeled here by 3D geometric paths). The developed control methods based on the hierarchical task approach are able to manage simultaneously the constrained motion (depending on the configuration case, i.e., bilateral or unilateral constraint) and a 3D path following. In addition, the proposed methods can operate with both straight or curved surgical tools. The proposed methods were successfully validated in various scenarios. Foremost, a simulation framework was proposed to access the performances of each proposed controller. Thereafter, several experimental validations were carried out. Both the simulation and experimental results have demonstrated the relevance of the proposed approach, as well as promising performances in terms of behavior as well as accuracy.

Many students complete PhDs in functional programming each year. As a service to the community, twice per year the Journal of Functional Programming publishes the abstracts from PhD dissertations completed during the previous year.