Amirhossein Nourian, University of Turku, amnour@utu.fi
Environmental monitoring is becoming increasingly important in a world facing rapid climate change, biodiversity loss, and environmental risks. Autonomous robots are emerging as powerful tools for monitoring ecosystems, as they can operate in remote, hazardous, and hard-to-reach environments. However, one of the most critical challenges limiting long-term deployment of such systems is energy efficiency. Mobile robots typically rely on finite onboard energy resources, such as batteries, which constrain their operational lifetime. This limitation is particularly significant in environmental monitoring tasks, where robots must function autonomously for extended periods without human intervention. Therefore, improving energy efficiency is not merely a performance optimization problem but a fundamental requirement for sustainable robotic systems.
Robots might look sleek and efficient on the outside, but under the hood, they’re constantly using energy in many ways. From sensing their surroundings and processing data to communicating and physically moving, every action comes with an energy cost. In fact, the biggest drain often comes from motion itself: walking, rolling, lifting, or interacting with the environment can consume most of a robot’s power. At the same time, the “brains” of the robot are its processors, sensors, and communication systems, which also demand a significant share of energy.
That’s why improving energy efficiency isn’t just about tweaking one part of the system. Researchers are increasingly realizing that it requires a holistic perspective. Instead of optimizing components in isolation, the real gains come from designing hardware, control systems, and intelligent decision-making to work seamlessly together. Sustainable robotics research highlights that energy-efficient operation must be integrated with environmental awareness and adaptive intelligence [1]
Internal and External Energy Factors
When it comes to energy use, robots are influenced by two big forces: what’s happening inside them and what’s happening around them.
Internal Environment (Robot-Centric Factors)
Inside every robot, there are different processes and components. These include the battery and how much charge it has left, the processing power needed to make decisions, communication between components (or with other systems), and the overall efficiency of embedded systems. Efficient management of these components allows robots to adapt their behavior based on available energy resources. For example, energy-aware scheduling and computation offloading strategies can significantly reduce power consumption while maintaining system performance [2].
External Environment (Task and Environment Factors)
But robots don’t operate in a vacuum. The outside world plays an equally important role in how much energy they use. Rough terrain, obstacles, and challenging navigation paths can quickly drain power. Even the nature of the task, whether it’s simple monitoring or complex exploration, affects energy demand. Add unpredictable elements like weather or moving objects, and things get even more demanding. Mechanical energy consumption is strongly influenced by these factors. Efficient path planning and motion strategies can significantly reduce energy usage. Advanced approaches, like reinforcement learning, are helping robots learn how to explore and operate more efficiently. even in changing environments [3].
Robot Survivability and Influencing Factors
In Figure 1, the diagram highlights how energy, stability, and time jointly determine long-term operational performance. Internal factors (such as battery state and computational load) and external factors (such as environmental conditions and task complexity) overlap and interact dynamically. The survivability timeline at the bottom emphasizes how robots must continuously balance these factors to maximize operational lifespan. Figure 1 serves as a conceptual bridge between system-level awareness and learning-based optimization, showing how decision-making must account for both immediate (event-based) and long-term (life-long) considerations.
Learning Energy-Aware Behavior with Deep Reinforcement Learning
Traditional robot control methods usually rely on predefined models and assumptions about how the world works. While they are effective in simple settings, these approaches often struggle when environments become complex, unpredictable, or constantly changing. That’s where Deep Reinforcement Learning (DRL) comes in. Instead of being explicitly programmed for every situation, robots can learn what to do by interacting with their environment.
DRL formulates the problem as a Markov Decision Process (MDP), where the robot observes a state, takes an action, and receives a reward. In energy-aware robotics, the state space includes both internal variables (battery level, CPU usage, communication load) and external variables (terrain features, obstacles, task goals). The reward function is typically designed to minimize energy consumption while ensuring task completion and stability.
DRL allows robots to:
- Learn energy-efficient navigation and control strategies
- Adapt to dynamic and uncertain environments
- Balance multiple objectives such as task completion and energy minimization
Recent research shows that reinforcement learning can effectively optimize exploration and sensing tasks in environmental monitoring scenarios [4]. Additionally, RL-based approaches can integrate internal system states with external environmental information, enabling more holistic decision-making. Advanced methods incorporate energy consumption directly into the learning objective. For instance, reinforcement learning frameworks can minimize energy usage while maintaining task performance, achieving substantial reductions in energy consumption without
sacrificing efficiency [5]. Furthermore, decentralized reinforcement learning approaches enable multi-robot systems to coordinate exploration and energy management, improving mission longevity and robustness [6]
Conclusion
Energy efficiency isn’t just a technical detail in robotic environmental monitoring—it’s one of the biggest challenges shaping how effective these systems can be. Addressing this challenge requires a comprehensive understanding of both internal and external energy factors, as well as the integration of advanced learning techniques such as deep reinforcement learning.
As robotics and artificial intelligence continue to advance, energy awareness will become a core part of how robots think and act. The future isn’t just about building machines that can do more, it’s about building machines that can do more with less. Ultimately, this approach will enable robots to adapt to changing environments, work more intelligently, and play a meaningful role in tackling some of the world’s most pressing environmental challenges.
References
- [1] M. Hutter, J. Buchli, and M. A. Hoepflinger, “Towards energy-efficient legged robots,” IEEE Robotics and Automation Magazine, vol. 20, no. 3, pp. 35–44, 2013.
- S. Xu, X. Chen, and Y. Li, “Energy-efficient multi-agent deep reinforcement learning for computation offloading,” Sensors, vol. 25, no. 3, pp. 1–18, 2025.
- T. Lu, Y. Wang, and Z. Liu, “Reinforcement learning-based dynamic exploration for mobile robots,” Frontiers in Robotics and AI, vol. 12, 2025.
- D. Mansfield, P. Corke, and J. Leitner, “Active perception and reinforcement learning for robotic systems,” Frontiers in Robotics and AI, vol. 11, 2024.
- H. Zhang, C. Li, and X. Wang, “Energy-efficient path planning for mobile robots using deep reinforcement learning,” IEEE Access, vol. 8, pp. 123456–123467, 2020.
- A. Faust, T. Hester, and P. Stone, “Learning intelligent energy management for robotic systems,” IEEE Transactions on Robotics, vol. 35, no. 3, pp. 739–752, 2019.
28.3.2026
