Currently, there is an intensive development of bipedal walking robots. The most known solutions are based on the use of the principles of human gait created in nature during evolution. Modernbipedal robots are also based on the locomotion manners of birds. This review presents the current state of the art of bipedal walking robots based on natural bipedal movements (human and bird) as well as on innovative synthetic solutions. Firstly, an overview of the scientific analysis of human gait is provided as a basis for the design of bipedal robots. The full human gait cycle that consists of two main phases is analysed and the attention is paid to the problem of balance and stability, especially in the single support phase when the bipedal movement is unstable. The influences of passive or active gait on energy demand are also discussed. Most studies are explored based on the zero moment. Furthermore, a review of the knowledge on the specific locomotor characteristics of birds, whose kinematics are derived from dinosaurs and provide them with both walking and running abilities, is presented. Secondly, many types of bipedal robot solutions are reviewed, which include nature-inspired robots (human-like and birdlike robots) and innovative robots using new heuristic, synthetic ideas for locomotion. Totally 45 robotic solutions are gathered by thebibliographic search method. Atlas was mentioned as one of the most perfect human-like robots, while the birdlike robot cases were Cassie and Digit. Innovative robots are presented, such asslider robot without knees, robots with rotating feet (3 and 4 degrees of freedom), and the hybrid robot Leo, which can walk on surfaces and fly. In particular, the paper describes in detail the robots’ propulsion systems (electric, hydraulic), the structure of the lower limb (serial, parallel, mixed mechanisms), the types and structures of control and sensor systems, and the energy efficiency of the robots. Terrain roughness recognition systems using different sensor systems based on light detection and ranging or multiple cameras are introduced. A comparison of performance, control and sensor systems, drive systems, and achievements of known human-like and birdlike robots is provided. Thirdly, for the first time, the review comments on the future of bipedal robots in relation to the concepts of conventional (natural bipedal) and synthetic unconventional gait. We critically assess and compare prospective directions for further research that involve the development of navigation systems, artificial intelligence, collaboration with humans, areas for the development of bipedal robot applications in everyday life, therapy, and industry.

While the intensity, complexity, and specificity of robotic exercise may be supported by patient-tailored three-dimensional (3D)-printed solutions, their performance can still be compromised by non-optimal combinations of technological parameters and material features. The main focus of this paper was the computational optimization of the 3D-printing process in terms of features and material selection in order to achieve the maximum tensile force of a hand exoskeleton component, based on artificial neural network (ANN) optimization supported by genetic algorithms (GA). The creation and 3D-printing of the selected component was achieved using Cura 0.1.5 software and 3D-printed using fused filament fabrication (FFF) technology. To optimize the material and process parameters we compared ten selected parameters of the two distinct printing materials (polylactic acid (PLA), PLA+) using ANN supported by GA built and trained in the MATLAB environment. To determine the maximum tensile force of the exoskeleton, samples were tested using an INSTRON 5966 universal testing machine. While the balance between the technical requirements and user safety constraints requires further analysis, the PLA-based 3D-printing parameters have been optimized. Additive manufacturing may support the successful printing of usable/functional exoskeleton components. The network indicated which material should be selected: Namely PLA+. AI-based optimization may play a key role in increasing the performance and safety of the final product and supporting constraint satisfaction in patient-tailored solutions.

Mobile robots can perform tasks on the move, including exploring terrain, discovering landmark features, or moving a load from one place to another. This group of robots is characterized by a certain level of intelligence, allowing the making of decisions and responding to stimuli received from the environment. As part of Industry 5.0, such mobile robots and humans are expected to co-exist and work together in a shared environment to make human work less tiring, quicker, and safer. This can only be realized when clean, dense, and economical energy sources are available. The aim of the study is to analyze the state of the art and to identify the most important directions for future developments in energy sources of robotic power systems based mainly on batteries. The efficiency and performance of the battery depends on the design using different materials. Work environments and performance requirements are considered in this systematic review to classify solutions that help developers choose the best-suited power system for specific application. Indirectly, the aim of the work is to generate discussion within the scientific and engineering community. A narrative review of publications from six major bibliographic databases according to preset inclusion criteria is combined with a critical analysis of current and future technologies. The main findings of the review allow answering the question of what is the role of modern power source technologies, artificial intelligence, and ground-breaking research work in global policies related to energy saving, green policies, and sustainable development. The main opportunities and threats are discussed, and a brief feasibility analysis is carried out. The novelty of the article relates not only to the analysis of technologies, but also to approaches and their use under conditions of limited resource availability, when resource usage must be minimized. The article provides an overview of batteries, their specifications, classifications, and their advantages and disadvantages. In addition, we propose (1) an algorithm for selecting the main energy source for robot application, and (2) an algorithm for selecting an electrical system power supply. Current mobile robot batteries are, in most cases, the robot’s biggest limitation. Progress in battery development is currently too slow to catch up with the demand for robot autonomy and range requirements, limiting the development of mobile robots. Further intensive research and implementation work is needed to avoid years of delay in this area.

Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing energy bills and maximizing profits. Problem: This article aims to check how predictable the operation of a household PV system is in the short term—such predictions are usually made 24 h in advance. Methods: We made a comparative study of different energy management strategies based on a real household profile (selected energy storage installation) based on both traditional methods and various artificial intelligence (AI) tools, which is a new approach, so far rarely used and underutilized, and may inspire further research, including those based on the paradigm of Industry 4.0 and, increasingly, Industry 5.0. Results: This paper discusses the results for different operational scenarios, considering two prosumer billing systems in Poland (net metering and net billing). Conclusions: Insights into future research directions and their limitations due to legal status, etc., are presented. The novelty and contribution lies in the demonstration that, in the case of domestic PV grids, even simple AI solutions can prove effective in inference and forecasting to support energy flow management and make it more predictable and efficient.

IoT applications for building energy management, enhanced by artificial intelligence (AI), have the potential to transform how energy is consumed, monitored, and optimized, especially in distributed energy systems. By using IoT sensors and smart meters, buildings can collect real-time data on energy usage patterns, occupancy, temperature, and lighting conditions.AI algorithms then analyze this data to identify inefficiencies, predict energy demand, and suggest or automate adjustments to optimize energy use. Integrating renewable energy sources, such as solar panels and wind turbines, into distributed systems uses IoT-based monitoring to ensure maximum efficiency in energy generation and use. These systems also enable dynamic energy pricing and load balancing, allowing buildings to participate in smart grids by storing or selling excess energy.AI-based predictive maintenance ensures that renewable energy systems, such as inverters and batteries, operate efficiently, minimizing downtime. The case studies show how IoT and AI are driving sustainable development by reducing energy consumption and carbon footprints in residential, commercial, and industrial buildings. Blockchain and IoT can further secure transactions and data in distributed systems, increasing trust, sustainability, and scalability. The combination of IoT, AI, and renewable energy sources is in line with global energy trends, promoting decentralized and greener energy systems. The case study highlights that adopting IoT and AI for energy management offers not only environmental benefits but also economic benefits, such as cost savings and energy independence. The best achieved accuracy was 0.8179 (RMSE 0.01). The overall effectiveness rating was 9/10; thus, AI-based IoT solutions are a feasible, cost-effective, and sustainable approach to office energy management.

The ability of artificial intelligence (AI) to process large amounts of data, analyze complex patterns, and make predictions is driving innovation in the energy sector and transformation of energy markets. It helps optimize operations, improve efficiency, reduce costs, and accelerate the transition to cleaner and more sustainable energy sources. AI is playing an increasingly important role in transforming energy markets in various aspects of the industry in different ways, including smart grids and energy management, renewable energy integration, energy forecasting and trading, demand response and load management, energy efficiency and conservation, maintenance and asset management, energy storage optimization, carbon emission reduction, market analytics and risk management, exploration and production, regulatory compliance, and safety. The aim of this article is to discuss our own AI-based computational model in sustainable transformation of energy markets and to lay the foundations for further harmonious development based on a computational (AI/ML-based) models, with particular reference to current limitations and priority directions for further research. Such an approach may encourage new research for the practical application of AI algorithms in critical domains of the energy sector.