An Improved Heuristic Path Planning Algorithm for Mobile Robot Navigating on Uneven Terrains

In the face of an imperative global call to mitigate carbon emissions, numerous nations and regions have pledged to achieve the ambitious targets of the “dual carbon” initiative. This transformative agenda is marked by a concerted effort to diminish dependence on fossil fuels, particularly coal, while concurrently augmenting the adoption of renewable energy sources such as wind and solar power. Nevertheless, the incremental integration of renewable energy sources into the power grid presents a notable challenge, specifically i n t he r ealm o f m aintaining the rotational inertia essential for system stability. The capacity market has thus become a critical regulatory instrument, essential for safeguarding the dependable functioning of power systems amidst the dynamic shifts in energy infrastructure and the inherent variability in demand. By integrating considerations of rotational inertia into the operational fabric of the market, key stakeholders are equipped to navigate the imperatives of stable frequency regulation, which is pivotal during the transition to a more sustainable energy paradigm. This scholarly inquiry offers a c omprehensive e xamination o f t he c apacity market's development, its operational mechanisms, and its strategic sig-nificance i n f ortifying t he g rid's s tability a nd r esilience. The analysis is conducted within the context of an expanding pen-etration of renewable energy, highlighting the indispensable role of the capacity market in the evolving energy landscape of the contemporary era.

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Published
2024-10-18
DOI
https://doi.org/10.1109/icus61736.2024.10840038
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

An Improved Heuristic Path Planning Algorithm for Mobile Robot Navigating on Uneven Terrains

Shaocong Wang, Ting Wang, Shiliang Shao, Da Liu et al.
Robotic Path Planning Algorithms
article

An Improved Heuristic Path Planning Algorithm for Mobile Robot Navigating on Uneven Terrains

Shaocong Wang, Ting Wang, Shiliang Shao, Da Liu, Y. Liu, Xianyu Shi
article en

Abstract

In the face of an imperative global call to mitigate carbon emissions, numerous nations and regions have pledged to achieve the ambitious targets of the “dual carbon” initiative. This transformative agenda is marked by a concerted effort to diminish dependence on fossil fuels, particularly coal, while concurrently augmenting the adoption of renewable energy sources such as wind and solar power. Nevertheless, the incremental integration of renewable energy sources into the power grid presents a notable challenge, specifically i n t he r ealm o f m aintaining the rotational inertia essential for system stability. The capacity market has thus become a critical regulatory instrument, essential for safeguarding the dependable functioning of power systems amidst the dynamic shifts in energy infrastructure and the inherent variability in demand. By integrating considerations of rotational inertia into the operational fabric of the market, key stakeholders are equipped to navigate the imperatives of stable frequency regulation, which is pivotal during the transition to a more sustainable energy paradigm. This scholarly inquiry offers a c omprehensive e xamination o f t he c apacity market's development, its operational mechanisms, and its strategic sig-nificance i n f ortifying t he g rid's s tability a nd r esilience. The analysis is conducted within the context of an expanding pen-etration of renewable energy, highlighting the indispensable role of the capacity market in the evolving energy landscape of the contemporary era.

Shenyang Institute of Automation (CN), Chinese Academy of Sciences (CN), State Key Laboratory of Robotics
National Natural Science Foundation of China
Openalex Percentile: Top 74%
Robotic Path Planning Algorithms
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An Improved Heuristic Path Planning Algorithm for Mobile Robot Navigating on Uneven Terrains — Shaocong Wang, Ting Wang, et al. · (2024) | TGRS Research Map | TGRS