Automobile engineering often demands creative and innovative concepts to achieve their performance and efficiency targets. However, the strategies used to create these concepts are evolving with time. Recently, heightened attention has been noticed to using biomimetic concepts in the field of automobile design. The studies that apply biomimicry to automobile design often exhibit improvements in vehicle performance and fuel efficiency. Consequently, biomimetic concepts have extended an opportunity to the automobile industry to generate futuristic designs with advanced technology. However, the innovations and inventions in biology are usually published with more complex terminology that is not convenient for the research community working on automobile engineering. Hence, a notable delay is present in transferring the recent discoveries in biology to engineering researches so that they can develop strategies to mimic them. The purpose of this paper is to provide a brief overview of the biomimetic design approaches that have been and can be used in automobile design. In addition, the paper includes a classification of biomimetic concepts, which reveals the current research interests and highlights the areas with a deficit of research that is required to be addressed extensively in the future.

This paper proposes a robotic exoskeleton chair, named ChairX, for providing sitting assistance for the industrial workers who need to carry out tasks at different crouched postures repetitively. It can act as a customizable chair to support bodyweight for relieving the lower extremity and provide freedom for ambulation when moving between workstations. In comparison to the predecessors, the ChairX is designed to assist the user for assuming crouched positions with forward- as well as backward-inclined leg angles. Moreover, it is flexible to provide sitting assistance at different heights to ensure ergonomics at work The ChairX includes a knee-centered locking mechanism on the lateral side and an active linkage mechanism on the posterior of the leg to support the weight of the user at various seated positions. A mathematical model was formulated to optimize the system parameters and a prototype was built to carry out the physical tests. The results revealed that ChairX can maintain stability and reduce the musculoskeletal strains effectively, thus has the potential to improve the quality of work.

Many work-related tasks demand able-bodied employees to assume uncomfortable postures for long periods. This can lead to work-related musculoskeletal disorders. The situation demands the employees to be physically empowered while ensuring their agility and flexibility. In that context, wearable bodyweight support systems have come to the forefront as a futuristic solution to reduce the stress on the human body when assuming sitting and/or crouched postures. Recently, heightened attention on these posture assistive devices was evident with an increasing number of products, patents, and research articles. However, reviews of such wearable posture assistive devices for the lower limbs are lacking in the literature. Thus, this article specifically focuses on reviewing existing methods and technologies to assist workers in the industrial or service sectors to work in uncomfortable or crouched postures. Systematic reviews and meta-analyses extension for scoping reviews method was used to identify and analyze 43 unique designs from research and patent databases. The critical review of the technologies was performed after classifying these devices into commercial phase, lab/research phase, and conceptual design phase. This review has comprehensively covered the trends and patterns of the modern developments in posture assistance and includes future directions for the research community. Holistically, this article serves as a reference to set up a benchmarking system for wearable posture assistive devices.

Complete coverage path planning (CCPP) is a trending research area in floor cleaning robotics. CCPP is often approached as an optimization problem, typically solved by considering factors, such as power consumption and time as key objectives. In recent years, the safety of cleaning robots has become a major concern, which can critically limit the performance and lifetime of the robots. However, so far, optimizing safety has rarely been addressed in CCPP. Most of the path-planning algorithms in literature tend to identify and avoid the hazards detected by the robot’s perception. However, these systems can limit the area coverage of the robot or pose a risk of failing when the robot is near a hazard. Therefore, this article proposes a novel CCPP method with the awareness of risk levels for a robot to minimize possible hazards to the robot during a coverage task. The proposed CCPP strategy uses reinforcement learning (RL) to obtain a safety-ensured path plan that evaluates and when necessary, avoid the hazardous components in their environment in real time. Furthermore, the failure mode and effect analysis (FMEA) method has been adopted to classify the hazards identified in the environment of the robot and suitably modified to evaluate the risk levels. These risk levels are used in the reward architecture of the RL. Thus, the robot can cross the low-risk hazardous environments if it is necessary to obtain complete coverage. Experimental results showed a noticeable reduction in overall risk faced by a robot compared to the existing methods, while also effectively achieving complete coverage.

Floor‐cleaning robots play a crucial role in both industrial and domestic spaces. However, these robots often face challenges due to hazardous components in their environment, which can cause them to fail and prevent them from performing at their best. This situation necessitates continuous research in the field of floor‐cleaning robots. However, most of these efforts focus on improving the robots’ perception capabilities by incorporating additional sensors. Nevertheless, incorporating more sensors is an expensive solution for most cleaning robots. Alternatively, in this research, the feasibility of introducing a safe path on a predefined hazard map is explored. The proposed method aims to trade‐off between area coverage and the safety of the cleaning robot. Herein, the failure mode and effect analysis (FMEA) method is introduced as a tool to classify the hazards and implement a safety‐ensured coverage path‐planning process. In this approach, the risk factor defined for a point in the environment serves as the key parameter to assess the safety of the algorithm's suggested path. To validate and evaluate the proposed method, this article utilizes the hTetro mobile robot. In the experimental results, it is demonstrated that the proposed method can reduce high‐risk movements of the robot compared to existing methods.