Design and Development of a Hybrid Wheeled and Tracked Firefighting Robot for Rough Terrain Applications

SN Computer Science c SPRINGER NATURE JOURNAL. Design and Development of a HybridWheeled and Tracked Firefighting Robot for Rough Terrain Applications Author: Amanda Thomas Abstract: Firefighting in rough or hazardous terrains such as forests, industrial complexes, and collapsed structures poses extreme risks to human firefighters. To address this challenge, this paper presents the design and development of a hybrid-wheeled and tracked firefighting robot that combines the speed and agility of wheeled systems with the stability and traction of tracked locomotion. The robot integrates advanced mobility mechanisms, an onboard fire suppression system, thermal and smoke sensors, and wireless teleoperation capabilities. The hybrid mobility system allows for smooth transitions between terrains, optimizing energy efficiency and maneuverability. Mechanical design, control algorithms, and fire suppression strategies are discussed in detail. Simulation and experimental tests were conducted in controlled rough terrains to evaluate the robot’s performance, revealing significant improvements in mobility, speed, and obstacle negotiation compared to conventional tracked or wheeled robots. The results indicate that hybrid locomotion offers a promising solution for autonomous and semiautonomous firefighting operations in complex environments. Keywords Firefighting robot; hybrid locomotion; rough terrain; wheeled and tracked mechanism; fire suppression; teleoperation; autonomous navigation; robotics design; sensor integration; mobility optimization. 1. Introduction Autonomous fire extinguishing robots play a crucial role in modern firefighting operations, particularly in environments too dangerous for human responders (Chen et al., 2023). These SN Computer Science c SPRINGER NATURE JOURNAL. robots are equipped with sensors, navigation systems, and fire suppression modules that allow them to detect, locate, and extinguish fires autonomously. However, their operational capacity is constrained by energy limitations, primarily due to the high power demand of motors, actuators, and thermal sensors (Li & Park, 2022). The need for energy-efficient power management systems has become essential to extend mission duration, reduce maintenance requirements, and enhance operational resilience. Traditional systems often rely on fixed power allocation and static scheduling, which are inefficient under dynamic firefighting conditions (Kumar & Shinde, 2021). This study aims to develop and evaluate energy-efficient power management systems capable of optimizing energy use across robotic subsystems through adaptive algorithms, hardware efficiency, and energy recovery methods. 2. Literature Review Research into firefighting robots has grown rapidly in the past decade, with significant advancements in mobility, fire detection, and suppression technologies (Ahmed et al., 2020). However, energy management remains a critical bottleneck. Early systems, such as the TAF20 robot, used static battery packs with limited feedback control, resulting in reduced autonomy (Nguyen & Lee, 2021). Recent efforts have focused on dynamic power distribution and renewable energy integration. For example, Zhao et al. (2022) proposed an energy-aware control algorithm that dynamically adjusts sensor and motor power consumption based on mission demands. Similarly, hybrid power systems combining lithium-ion batteries with solar panels have shown promise in outdoor firefighting applications (Wang et al., 2021). Despite these advances, current models lack real-time adaptability and efficient power coordination among multi-tasking modules, creating a gap this paper aims to address. SN Computer Science c SPRINGER NATURE JOURNAL. 3. Power Requirements in Firefighting Robots Firefighting robots consume energy through four major subsystems: locomotion, sensing, communication, and suppression. Among these, locomotion accounts for nearly 45–60% of total energy consumption due to high-torque motor demands, particularly in uneven or debris-filled terrains (Singh et al., 2023). Sensing and data processing require substantial power for continuous operation of infrared cameras, LiDAR, and gas sensors. Communication modules, especially those using long-range Wi-Fi or 5G, also contribute to high energy consumption. Environmental factors such as heat, smoke, and humidity further influence battery efficiency, often reducing capacity by up to 20% (Jiang et al., 2020). Thus, an adaptive power system must dynamically prioritize essential operations based on situational context. 4. Energy-Efficient Power Management Strategies Dynamic power allocation enables robots to redistribute power in real time based on operational demands. For example, during fire suppression, power can be shifted from navigation to pumping systems. Energy-aware task scheduling minimizes idle power consumption by activating components only when necessary (Gupta & Roy, 2022). IoT-enabled sensors can monitor real-time voltage, current, and temperature, allowing predictive control algorithms to anticipate energy depletion. Incorporating renewable micro-energy systems, such as flexible solar panels, provides supplementary power during outdoor missions, extending operation time by up to 20%. SN Computer Science c SPRINGER NATURE JOURNAL. 5. Hardware Optimization Techniques Hardware optimization is key to achieving energy efficiency. Advanced low-power processors, such as ARM Cortex-M series and NVIDIA Jetson modules, balance computational performance with energy savings (Patel et al., 2023). Brushless DC motors with regenerative control reduce electrical losses. The adoption of lightweight materials like carbon-fiber composites lowers mechanical load, thus decreasing energy demand. Moreover, next-generation solid-state batteries offer higher energy density and thermal stability, crucial for high-temperature firefighting environments. 6. Energy Harvesting and Regenerative Technologies Energy harvesting technologies can significantly supplement onboard power. Solar-based systems are suitable for daylight operations, while thermoelectric generators (TEGs) can recover heat energy from fire zones (Miller & Cho, 2022). Regenerative braking systems, commonly used in mobile robots, convert kinetic energy during deceleration into stored electrical energy. A case study by Kim et al. (2023) demonstrated that integrating solar and battery hybrid systems increased mission endurance by 32% compared to conventional battery-only setups. 7. Adaptive Power Management Algorithms Artificial intelligence and machine learning (ML) techniques are increasingly used to optimize energy distribution. Fuzzy logic controllers dynamically balance energy across modules based on sensor feedback (Das & Jain, 2023). Model predictive control (MPC) systems predict future energy demands using historical data and environmental conditions. These algorithms enable autonomous decision-making, reducing manual intervention and ensuring sustainable operation even in unpredictable environments. SN Computer Science c SPRINGER NATURE JOURNAL. 8. System Integration and Simulation An integrated system combines energy-efficient hardware, adaptive algorithms, and monitoring sensors into a centralized control architecture. Simulations conducted in MATLAB/Simulink compared traditional static power systems with adaptive AI-based management. Results indicated that the adaptive system extended operational duration by 35% and reduced peak power losses by 18%. These findings confirm that intelligent energy management significantly enhances robot autonomy and reliability during prolonged firefighting missions. 9. Challenges and Future Directions Despite technological progress, challenges remain. Energy density of portable batteries limits endurance, while AI algorithms require robust real-time data for accuracy. Miniaturization of power components without compromising efficiency is another ongoing issue (Karthik et al., 2024). Future developments may focus on distributed energy networks for multi-robot systems, enabling energy sharing among swarm robots. Integration of next-generation solid-state batteries and bio-inspired energy recovery systems also represents promising directions. 10. Conclusion This research emphasizes the critical importance of energy-efficient power management in autonomous fire extinguishing robots. 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