Editorial

Pandemic and climate change: two events that are mixed and, often, seem to be the same. The events of recent years, and especially 2021, have jeopardized the certainties which up until now, were grounded on that scientific knowledge could lead humanity along a safe path. For a while, we thought that the results of multiple theses, and the accumulated efforts to understand phenomena and solutions based on the concept of sustainability were in vain, given the threats of a ferocious pandemic, along with the frustrated attempts to contain climate change. The goals sought up until now have been greatly challenged.

Quali-quantitative synthesis of the global scenario of patent families about leprosy

The work presents the analysis and synthesis of patent families about leprosy worldwide in the last 20 years. Three technological aspects were defined for the study of both portfolios, thus defined: diagnostic, drug/treatment and vaccine. Three thousand and three hundred twenty-three (3323) patent families were identified (790 for diagnostic, 1515 for drug and 1018 for vaccine). Fifty-eight (58) patent holders were identified and analyzed, with the most expressive portfolio of patent families, distributed in 13 countries. For the recovery of patent families, the ORBIT Intelligence® system from QUESTEL® was used. Using quantitative and qualitative analysis and synthesis, the patterns of five strategic indicators on the global patent scenario about leprosy were observed, namely: holders of patent families; the years of filing for unionist priority for patent families; countries of unionist priority for patent families; the legal status of filed patent families; and the legal status of patent families by country of protection. It is suggested that new public policies be created to encourage synergy between patent families and the social determinants of neglected populations, in order to induce a safe path to technological independence and better performance in the fight against leprosy in the Brazil.

Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

2019冠狀病毒病疫情下的疫苗猶疑與強制疫苗接種：初步的道德評估

LANGUAGE NOTE | Document text in Chinese; abstract also in English. 疫苗猶豫，亦即延遲甚至拒絕接受疫苗接種，不只是人們對科學有多少了解的問題，亦混雜着不同信念和對權威的不信任。另一方面，有支持強制接種疫苗人士認為，因為疫苗是相對安全的方法令得社會達至群體免疫；權衡輕重之下，強制接種是道德上可以容許的做法。社會能否要求民眾強制接種新冠疫苗，抑或是疫苗猶豫有其合理性，應予尊重，是一個值得我們探討的道德課題。 本文會用以下的方式探究此項課題。歷來有不少學者提出各類支持強制接種麻疹疫苗的理由。另一方面，我們亦必須考慮各類支持疫苗猶疑的理由。正反的道德論證，皆涉及不能約化，但是在不同情境需要比較和排序的價值。我們會論證應以阿馬蒂亞•森(Amartya Sen)提出的後果評價作為道德推論和基礎去評估這些多元價值。在後果評價的基礎上，才可以比較接種新冠疫苗與麻疹疫苗在道德上的異同，並由此建立一套框架去評估強制疫苗接種的道德議題。 Vaccine hesitancy, a delay in acceptance or even refusal of vaccination, is a problem not only linked to public knowledge of science but also caused by complex beliefs and a lack of confidence in authority. People who support coercive vaccination argue that vaccination is a comparatively safe path for people in a community to reach herd immunity. Weighing the benefits and costs, coercive vaccination is morally permissible. However, whether we should enact it for Covid-19 vaccines or respect people who have vaccine hesitancy is a moral issue worthy of detailed investigation. Similar debates have also been around coercive use of the measles vaccine, which will serve as a point of comparison in this evaluation. There are different kinds of arguments for and against policies of coercive vaccination, but whether positive or negative, they involve values that are incommensurable but should be compared and ranked accordingly in different situations. We argue that consequential evaluation, as suggested by Amartya Sen, forms the moral reasoning and foundation to evaluate these plural values. Using consequential evaluation, we can compare the moral similarities and differences between Covid-19 vaccines and measles vaccines and develop a framework to evaluate the moral issue of coercive vaccination.

Aircraft Parking Trajectory Planning in Semistructured Environment Based on Kinodynamic Safety RRT ∗

To improve the safety and effectiveness of autonomous towing aircraft aboard the carrier deck, this study proposes a velocity-restricted path planner algorithm named as kinodynamic safety optimal rapidly exploring random tree (KS-RRT ∗ ) to plan a near time-optimal path. First, a speed map is introduced to assign different maximum allowable velocity for the sampling points in the workspace, and the traverse time is calculated along the kinodynamic connection of two sampling points. Then the near time-optimal path in the tree-structured search map can be obtained by the rewiring procedures, instead of a distance-optimal path in the original RRT ∗ algorithm. In order to enhance the planner’s performance, goal biasing scheme and fast collision checking technique are adopted in the algorithm. Since the sampling-based methods are sensitive to their parameters, simulation experiments are first conducted to determine the optimal input settings for the specific problem. The effectiveness of the proposed algorithm is validated in several common aircraft parking scenarios. Comparing with standard RRT ∗ and human heuristic driving, KS-RRT ∗ demonstrates a higher success rate, as well as shorter computation and trajectory time. In conclusion, KS-RRT ∗ algorithm is suitable to generate a near time-optimal safe path for autonomous high density parking in semistructured environment.

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm

Both emergency braking and active steering are possible choices for collision avoidance manoeuvres, and any obstacle avoidance strategy aims to design a control algorithm preventing accidents. However, the real-time path needs to consider the motion state of surrounding participants on the road. This work presents a collision avoidance algorithm containing the path-planning and the tracking controller. Firstly, the lateral lane-changing spacing model and the longitudinal braking distance model are presented, describing the vehicle to reactively process dynamic scenarios in real environments. Then, we introduce the safety distance into the artificial potential field algorithm (APF), thereby generating a safe path in a simulated traffic scene. Redesigning the influence range of obstacles based on the collision areas and corresponding safety distance compared with the classic APF. Besides, based on the threat level, the repulsion is divided into the force of the position repulsion and the speed repulsion. The former is related to the relative position and prevents the vehicle from approaching the obstacle. The latter is opposite to the relative speed vector and decelerates the ego vehicle. Simultaneously, the attraction is improved to apply a dynamic environment. Finally, we design a model predictive control (MPC) to track the lateral motion through steering angle and a Fuzzy-PID control to track the longitudinal speed, turning the planned path into an actual trajectory with stable vehicle dynamics. To verify the performance of the proposed method, three cases are simulated to obtain the vehicle responding curves. The simulation results prove that the active collision avoidance algorithm can generate a safe path with comfort and stability.