Size reconfigurable robots have been introduced for coverage applications to improve performance. The size reconfiguration ability allows a robot to access narrow areas in a smaller size while covering open spaces in a larger size, improving productivity. This paper proposes a novel CPP method consisting of an Overlapping Reduction Criterion (ORC) and a Reconfiguration Reduction Criterion (RRC) for a size-reconfigurable robot to improve performance in dynamic workspaces. A Glasius Bio-inspired Neural Network (GBNN) is adapted to guide the robot toward unvisited cells considering neural activity variation. The size variation is managed by utilizing a collection of grid maps generated for various size configurations of the robot. The RRC and ORC penalize the movements requiring size reconfigurations or creating isolated unvisited regions in the decision-making process of next movement selection yielding to reduce reconfigurations and overlapping. According to the results, the proposed CPP method surpasses state of the art in terms of performance indexes reconfiguration count, overlapping, path distance, and coverage time by significant margins.

This paper introduces EDeformNet, a novel method for real-time 3D reconstruction of fishing nets using sparse positional measurements. Currently, net deployment during large-scale fishing operations is challenging as the submerged lattice deformations that occur in response to the various environmental factors are not visible to the vessel operator. EDeformNet extends Embedded Deformation Graphs (EDGs), a commonly used technique in template-based nonrigid 3D reconstruction that allows control of embedded spaces through sparse control point correspondences. These can be suitably derived from acoustic tracking beacons attached to the net. EDeformNet enhances the standard EDG optimization scheme by including constraints that preserve surface normals at control points and guard distances between vertices in the template mesh. These improvements are proven to enable an accurate representation of the complex deformations and movements typical in purse seine nets, the fishing technique where the algorithm has been tested, which standard EDG is unable to attain. Moreover, EDeformNet also proposes a tailored strategy that dynamically adjusts the net template according to the known length of the deployed portion of the fishing net. This approach reconstructs exclusively the submerged portion of the fishing net, avoiding extraneous data from above-water sections and enhancing accuracy under realistic fishing conditions. The proposed method is validated using realistic 3D physics simulations in Blender, where quantifiable comparisons demonstrate that EDeformNet effectively captures the spatial dynamics of purse-seining. Compared to standard EDG, EDeformNet achieves superior performance, resulting in at least a 25% improvement across the array of challenging temporal scenarios studied.

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.

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.