# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 2 Related Work  
This section considers the related literature to effectively tackle path planning in a WSN. The limitations of batteries necessitate the development of alternatives to communication methods that conserve energy. [PERSON] et al. studied network topology-based energy efficient WSN MAC techniques [14]. In [15], energy-efficient and reliable WSN MAC and routing algorithms are examined. Ref. [16] introduces a packet-sending method to improve channel quality and decrease redundancy. [PERSON] et al. [17] have created a hybrid-coding-aware routing solution that is applicable to the realm of underwater acoustic sensor networks (UASNs). This strategy reduces the amount of transmission overhead while also enhancing dependability. Clustering improves lifespan, resource management, data aggregation, and energy efficiency in wireless sensor networks [18]. The network is divided into clusters, each with a cluster head (CH) relaying information to reduce redundant transfers [19]. Energy and bandwidth savings exist in challenging fields with limited communication resources [20]. In the work [21], a clustering-based communication protocol lowered sensor node energy usage. [PERSON] et al. 's topology management solution improves coverage and longevity, ensuring reliable connectivity [22]. [PERSON]. [23] presented a virtual force-based distributed node deployment strategy to improve WSN network coverage. A network topology control model in [24] optimizes data transmission and extends network lifespan for features such as robustness, energy consumption balance, and topology. AGVs charge, collecting data as the UAV gathers data. Sensor-equipped AGVs can collect geology, water quality, and marine life data. In [25], AGV-assisted communication was tested, with the AGV gathering energy-saving data as a mobile node. In [26], AGVs were proposed for data collecting and K-means pathway planning [27]. AGVs are used for data collection and multi-hop detection [28]. AGVs or central stations can receive data from mobile or stationary sensors. We can coordinate the activities live. [PERSON] et al. 's [29] field-deployable three-phase wireless charging system charges AGVs quickly and easily. According to the work in [30], using dynamic system theory for AGV navigation in 0-100 m depths led to faster battery life. Building autonomous-docking, battery-charging AGVs allows long-term operation without human intervention. The dock charges sensor nodes and AGV batteries. Their efficiency and independence increase without retrieval and recharge. Efficiency is improved via AGV path design. [PERSON] et al. apply kinematic and dynamical models to plan AGV routes, avoid obstacles, and assess energy usage for energy savings and network longevity [31]. [PERSON] and colleagues [32] propose a hybrid underwater AGV exploration technique that considerably restricts their range. In [33], the exploring region is separated into smaller parts with data collecting points. Prepared pathways conserve AGV energy when gathering data. A rechargeable method [34] extends network life. A path planning and energy-saving technique for using AUVs can handle the network energy limits and battery replacement issues ([35]). While many AUVs charge sensor network nodes, a genetic algorithm selects the optimal path to maximize network size and transmission reliability. The simulation demonstrates that while energy balancing node density and network size, the AUV path planning method converges more quickly than the conventional approaches and lengthens the lifetime of WSNs. In high-density networks, our path planning saves 15% of the energy used by exploratory AUVs. Autonomous ground vehicles have attracted a lot of attention as workable solutions for a variety of military and civilian applications. However, in challenging circumstances (e.g., border enforcement and planetary exploration) where they must operate autonomously with limited onboard power, energy consumption plays a crucial role in autonomous ground vehicle navigation.