Energy-Efficient Navigation of Mobile Robotic Systems

Mobile robotic systems have become integral to modern industry, logistics and exploration, yet their operational endurance is constrained by energy capacity and consumption patterns. Energy-efficient navigation seeks to optimise path planning, motion control and hardware selection for wheeled, legged and aerial robots to extend operating time without compromising performance. The field spans analytical energy models that predict consumption from kinematic variables, adaptive algorithms that account for dynamic environmental factors and bio-inspired actuation strategies that reduce actuator power draw. Advances in sensor fusion and machine learning enable real-time estimation of wheel slippage, terrain variability and battery state, supporting predictive control schemes that minimise unnecessary motion and avoid high-resistance trajectories. The integration of soft robotics and neuromorphic control architectures has further expanded the design space, demonstrating that compliant structures and event-driven sensing yield significant energy savings. Multi-objective optimisation frameworks balance competing demands—such as speed, accuracy and energy usage—offering scalable solutions for autonomous vehicles in complex, unstructured environments. Together, these developments mark a shift towards holistic approaches that unify hardware innovations with intelligent software for sustainable robotic navigation.

Research from Nature Portfolio

Recent studies have highlighted the promise of bio-inspired compliance in reducing locomotion energy requirements. Investigations into soft robotic actuators modelled on muscle fibres have demonstrated adaptive gait control that adjusts stiffness in real time, yielding up to 30 percent lower power draw on uneven terrain. Complementary work has applied event-driven vision sensors to indoor mobile platforms, leveraging sparse data streams to update path plans only when significant environmental changes occur, thereby halving the energy spent on perception and computation. Further research has introduced neuromorphic control chips that process proprioceptive and exteroceptive inputs with low power consumption, enabling centimetre-scale drones to execute obstacle avoidance and stable hovering missions with minimal battery drain.

Energy-Efficient Navigation of Mobile Robotic Systems publication trend

The graph below shows the total number of articles in energy-efficient navigation of mobile robotic systems across all publications each year (not limited to Nature Index journals).

Technical terms

Non-holonomic constraint: A motion restriction that depends on the system’s path, common in wheeled robots where lateral slip is not permitted.

Trajectory planning: The process of determining a sequence of positions and orientations that a robot must follow.

Instantaneous Centre of Rotation (ICR): The point in the robot’s motion plane around which the robot is momentarily rotating.

Reinforcement learning: A machine learning paradigm in which agents learn optimal actions through trial-and-error interactions with their environment.

Centre of Mass (CoM): The point at which the mass of a system is concentrated, influencing stability and energy expenditure.

Soft actuator: A compliant device that emulates biological muscle, allowing energy absorption and release during locomotion.

References

1. Energy Modeling and Power Measurement for Mobile Robots. Energies (2018).
2. An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots. Energies (2021).
3. Power-minimization and energy-reduction autonomous navigation of an omnidirectional Mecanum robot via the dynamic window approach local trajectory planning. International Journal of Advanced Robotic Systems (2018).
4. Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter. IEEE Access (2020).
5. Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot. Sensors (2021).
6. Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position. IEEE Access (2022).