Energy autonomy is one of the most critical challenges in the operation of underwater vehicles. Equipped with limited-capacity batteries, these devices must complete monitoring, inspection, or search missions while optimizing every watt consumed. Traditionally, reinforcement learning-based controllers seek to maximize task accuracy, but they often generate oscillating performance patterns that waste energy. In contrast, an emerging approach posits power management as an explicit constraint – a budget – rather than a diffuse reward penalty. This paradigm shift not only improves efficiency, but opens the door to more predictable and transferable control systems between different vehicles and missions.
Reinforcement learning has proven to be effective for positioning and trajectory tracking tasks in underwater environments, where modeling fluid dynamics is extremely complex. However, when the only goal is accuracy, the algorithm learns to make abrupt and continuous corrections, which increases energy expenditure. The classic solution of subtracting an energy term from the reward introduces a hyperparameter without physical units that must be manually adjusted for each combination of vehicle and task. A poorly chosen weight can even increase consumption, contradicting the initial purpose. On the contrary, formulating the problem as a Markov decision process with constraints allows us to set a specific limit of average power, expressed in real units as watts, and solve it using algorithms such as PPO-Lagrangian. This method automatically adjusts a single dual parameter online, without the need for manual search, ensuring that the resulting policy stays within the declared energy budget.
The application of this approach in simulators such as MarineGym, tested on three different vehicles and four tasks, shows consumption reductions of between 14% and 65% compared to a controller that only optimizes the task, maintaining a superior smoothness of action in most cases. Only at extremely low power regimes is some precision sacrificed, which is acceptable if the mission prioritizes duration over performance. These results show that imposing power as a constraint – and not as a punishment – drastically simplifies the controller's design, making it portable without recalibrations.
Beyond the underwater realm, this design philosophy has profound implications for autonomous robotics in general. Any system that operates with limited resources—aerial drones, mobile robots, IoT devices—can benefit from incorporating physical constraints directly into the formulation of the control problem. Artificial intelligence, and in particular restricted reinforcement learning, thus offers a path to more energy-responsible agents. In this context, companies that develop AI solutions for companies can integrate these techniques into fleet management systems, route optimization or industrial process control, reducing operating costs and extending the useful life of equipment.
To implement this type of controller in real environments, careful software engineering is required that combines simulation models, optimization algorithms, and deployment on embedded hardware. This is where applications become relevant as we develop in Q2BSTUDIO. Our team builds software platforms that integrate physically-constrained AI agents, capable of operating on AWS and Azure cloud services to scale telemetry data processing and storage. In addition, cybersecurity is critical to protect communication between vehicles and base stations, especially in critical missions such as offshore exploration or underwater surveillance. Our cybersecurity and pentesting services ensure that control interfaces and data flows are shielded against intrusions.
Efficient energy management depends not only on the control algorithm, but also on the ability to analyze large volumes of historical data to adjust budgets in real-time. This is where business intelligence services and tools such as Power BI come into play, which allow you to visualize the energy consumption of each vehicle and predict the remaining life of the battery. At Q2BSTUDIO we offer AWS and Azure cloud services to host these dashboards, as well as process automation solutions to synchronize missions with available resources. The AI agents we develop can act as autonomous controllers that make split-second decisions, adapting to changes in current or visibility without human oversight.
All in all, the transition from an approach based on weighted rewards to one based on constraints with physical units represents a quantum leap in robotic control engineering. For companies looking to integrate these capabilities into their products or processes, having a technology partner that understands both theory and practice is key. From custom software design to the implementation of advanced AI models, at Q2BSTUDIO we help build systems that save energy, reduce costs, and increase operational autonomy. Whether managing a fleet of underwater drones or a network of oceanographic sensors, the combination of control with constraints and cloud services allows you to achieve levels of efficiency that previously seemed unattainable.
