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As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots.

The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness.

According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented.

EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study.

The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios.

This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.

Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment.

First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively.

Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper.

Only one step climbing motion planning is considered in this method.

With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint.

The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

The purpose of this paper is to describe and analyze the effects of campus sustainability planning to annual campus energy inflows and outflows in California higher education. The paper also offers a preliminary statistical analysis for the evaluation of impact factors on energy outflows and a link between energy outflows and building utilization.

The paper reports two campus examples University of California Merced and California State University Stanislaus, analyzing 36-months’ campus energy outflows data. It uses statistical linear regression analysis to determine the most significant impact factors to energy outflows and what is their relationship. Finally, the paper draws on building utilization data and presents sustainability management strategies for campus energy conservation which make the most of building utilization and contributes to campus sustainability planning efforts.

Statistics analysis considered ten multiple models of linear regression to identify the greatest impact factors on campus energy outflows. Interestingly, the overshadowing positive impact factor is renewable energy credits (RECs) which is expected as is required by California energy law. After removing RECs, cost of RECs and cost of electricity from further statistical analysis, we re-compute linear regression for the remaining variables, and natural gas outflows have the strongest – negative – relationship with energy outflows. In this study, it is demonstrated how sustainability planning applies to campus green building design criteria; how much do sustainable campus buildings cost; how sustainability planning affects the inflows and outflows of energy during the period of one academic year; and what are the direct benefits of campus sustainability planning and design to faculty, students, staff, administrators, environment and society.

The research is focused on two campus examples in California higher education and may have overlooked some campus sustainability plans and energy data from other California campuses. Nevertheless, it is a fairly comprehensive analysis of campus sustainability planning efforts and their effects on energy conservation.

Campus sustainability plans and their effect on campus energy inflows and outflows are very important. Understanding the details and potential effects of impact factors to energy conservation can help broader adoption and implementation of sustainability planning.

As an emerging method for campus sustainability efforts, statistical analysis of multiple linear regression models allows colleges and universities to examine energy conservation and align it with campus sustainability planning operational, academic and administrative functions in an integrated manner. To date, very little scholarly attention has been paid to the effects of sustainability planning on campus-level energy conservation, and no prior attempt has been made to consider how they might be analyzed statistically.

The objective is to describe and evaluate the development of a novel planning tool for end‐use efficiency in the built environment and for infrastructural changes in the energy system.

After describing problems related to further reduce heat demand in the Danish built environment, the geographical nature of the planning task is discussed. The requirements are then translated into concepts for the development of a general method, which is implemented in a practical design of a heat atlas. Typical applications are described and discussed.

It was found that the availability of the extensive public databases in Denmark make feasible the development and application of a highly detailed geographical information base for end use and infrastructure planning and analysis. It was also realised that the development has much higher potentials than explored in this paper. On the other hand, the complex geography of the urban/rural boundaries of cities requires extra care when using this approach.

Unfortunately, the results of this report are only directly applicable for Denmark, which maintains public databases on the built environment and socio‐demography with a very high standard of detail and coverage. The research presented here may require further development of empirical methods of the relation between energy demand and physically and socially mapped data. On the other hand, the research may contribute to better data for analyses in the techno‐economic analyses of future energy systems, which now can be carried out for arbitrary geographical units, independent of administrative boundaries.

The method presented here may be further developed as a practical tool to be used to revive the municipal and regional energy planning, either by technical consultants or by local governments. Even a publicly accessible, web‐based tool is feasible.

The paper describes how existing data in society can be assembled to a novel method to be used within energy planning, and environmental management as a whole. A system of the one developed does not exist as yet. On the other hand it builds upon existing traditions in energy planning and local governance.

The development of modern building planning has become inseparable from the concept of green energy saving, which has gradually become an important method for energy-saving design of building planning and has become the research direction of planners. Based on this, the status quo of research on green building at home and abroad was investigated and the principle of energy saving for green building planning was put forward; then the building shape coefficient, eco-strategy design of green building energy, building energy-saving layout and other aspects were analyzed in detail; finally, the Amsterdam large CBD complex Valley green building was analyzed. The results show that the building has fully considered the energy-saving design in the process of planning and design, which not only ensures the commercial demand, but also reflects the residential use and finally achieves the goal of green energy saving.

Energy use in urban areas has turned a subject of local and worldwide interest over the last few years, especially emphasized by the correlated greenhouse gases emissions. The purpose of this paper is to analyse the overall energy efficiency potential and emissions resulting from integrated solutions in urban energy planning, in the scale of districts and neighbourhoods in Brazil.

The approach is based on the description and the application of a method to analyse energy performance of urban areas and support their planning. It is a quantitative bottom-up method and involves urban morphology, urban mobility, buildings and energy supply systems. Procedures are applied to the case study of Agua Branca urban development area, located in Sao Paulo, Brazil.

In the case of Agua Branca area, energy efficiency measures in buildings have shown to be very important mostly for the buildings economies themselves. For the area as a whole, strategies in promoting public transport are more effective in terms of energy efficiency and also to decrease pollutant emissions.

Literature review has shown there is a lack of approaches and procedures able to support urban energy planning at a community scale. The bottom-up method presented in this paper integrates a plenty of disaggregated and multisectoral parameters at the same stage in urban planning and shows that is possible to identify the most promising actions by building overall performance indexes.

Obviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach.

To the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

As seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor.

To the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

This contribution aims to demonstrate that the adoption of a holistic approach to the development of infrastructures supporting the growth of cruise tourism can represent a trigger for a more sustainable urban and port planning in the Moroccan context. Although, along with this growth, there are global-local issues pertaining to sustainability and resilience that need to be addressed to support sustainable urban and social development of tourist cities.

Documents and literature review were used to develop an in-depth analysis related to the different fields involved in the proposed theoretical reflection. Scientific publications, data and international statistical reports have been used to validate the approach.

The work provides empirical insights to show the strengths of the country related to the sustainability, suggesting that they deserve to be integrated, involving urban planning, green energy transition, as well as the economic and social sustainability of tourism industry.

This paper aims to stimulate a debate among stakeholders and scholars involved on tourist port cities urban planning to avoid a “silos” approach and to favour a more sustainable infrastructure development process. In that sense, the paper fulfils the need to provide a basis to encourage the integration of different strategic topics and agenda concerning social and economic growth of tourist port cities.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

Pumped-storage hydroelectricity (PSH) is considered as an effective method to moderate the difference in demand and supply of electricity. This study aims to understanding of the high capacity of energy production, storage and permanent exploitation has been the prominent feature of pumped-storage hydroelectricity.

In this paper, the optimization of energy production and maintenance costs in one of the large Iranian PSH has been discussed. Hence, a mathematical model mixed integer nonlinear programming developed in this area. Minimizing the difference in supply and demand in the energy production network to multiple energies has been exploited to optimal attainment scheme. To evaluate the model, exact solution CPLEX and to solve the proposed programming model, the efficient metaheuristics are utilized by the tuned parameters achieved from the Taguchi approach. Further analysis of the parameters of the problem is conducted to verify the model behavior in various test problems.

The results of this paper have shown that the meta-heuristic algorithm has been done in a suitable time, despite the approximation of the optimal answer, and the consequences of research indicate that the model proposed in the studied power plant is applicable.

In pumped-storage hydroelectricity plants, one of the main challenges in energy production issues is the development of production, maintenance and repair scheduling concepts that improves plant efficiency. To evaluate the mathematical model presented, exact solution CPLEX and to solve the proposed bi-objective mixed-integer linear programming model, set of efficient metaheuristics are used. Therefore, according to the level of optimization performed in the case study, it has caused the improvement of planning by 7%–12% and effective optimization processes.