How to Estimate Mechanical Energy Losses in Robotics Applications

Mechanical energy losses in robotics applications can be estimated by considering various factors that contribute to the overall energy consumption of the system. These factors include the energy consumed during forward and backward movements, angular turns, and shape shifting. To estimate these losses, a novel energy model can be developed using Newton-Raphson (NR) estimation to calculate the successive energy approximation values from each individual gaits in the system.

Understanding Mechanical Energy Losses in Robotics

Mechanical energy losses in robotics applications can be attributed to several factors, including:

1. Forward and Backward Movements: The energy consumed during forward and backward movements can be calculated by taking the index position (starting point) as a reference along with angular displacement. The cost can be calculated using equations (2) and (3) in the study by Manimuthu et al.

2. Angular Turns: The power exerting elements like speed, angle, position, weight, inertia, and inductance are necessary to be incorporated in the system, especially during turnings. The inertia of the DC motors ‘I’ is one of the critical parameters that affects the output response, including rising time, settling time, overshoot, and steady-state error of the system.

3. Shape Shifting: The power exerting elements like speed, angle, position, weight, inertia, and inductance are also necessary to be incorporated in the system during shape shifting. The inertial movement affects the output response, including rising time, settling time, overshoot, and steady-state error of the system.

Estimating Mechanical Energy Losses using Newton-Raphson (NR) Estimation

To estimate the mechanical energy losses in robotics applications, a novel energy model can be developed using the Newton-Raphson (NR) estimation technique. This method calculates the successive energy approximation values from each individual gaits in the system. The steps involved in this process are as follows:

1. Identify the Energy Consumption Factors: Determine the energy consumed during forward and backward movements, angular turns, and shape shifting.

2. Develop the Energy Consumption Model: Formulate the energy consumption model using the identified factors. This model should include the necessary power exerting elements, such as speed, angle, position, weight, inertia, and inductance.

3. Apply Newton-Raphson (NR) Estimation: Use the NR estimation technique to calculate the successive energy approximation values from each individual gaits in the system. This will help in accurately estimating the mechanical energy losses.

4. Incorporate Inertial Effects: Consider the inertia of the DC motors ‘I’ as one of the critical parameters that affects the output response, including rising time, settling time, overshoot, and steady-state error of the system.

5. Validate the Model: Test the developed energy consumption model with experimental data or simulations to ensure its accuracy in estimating the mechanical energy losses.

Example Calculation of Mechanical Energy Losses

Let’s consider a scenario where a robot is powered by a 6V battery rated at 2000mAh. The total energy available in the battery is:

Energy = Voltage × Capacity
Energy = 6V × 2000mAh = 43200 Joules

As a robot builder, it is important to minimize the number of energy conversions, as no conversion is 100% efficient. Typically, most conversions have an efficiency between 30% and 70%, which can result in significant energy losses.

For example, if the robot’s motors have an efficiency of 50%, the actual energy available for the robot’s movement would be:

Actual Energy = 43200 Joules × 0.5 = 21600 Joules

This means that the mechanical energy losses in this scenario would be around 50% of the total energy available.

Estimating Mechanical Energy Losses using the Energy Consumption Estimation Model

The Energy Consumption Estimation Model for Complete Coverage of a Tetromino Inspired Reconfigurable Surface Tiling Robot can be used to estimate the mechanical energy losses in robotics applications. This model predicts the robot’s movement for a span of 180 degrees, enabling one half cycle of free-wheel movement for every step input function.

The key features of this model include:

1. Consideration of Forward and Backward Movements: The model takes into account the energy consumed during forward and backward movements, as well as the angular displacement.
2. Incorporation of Power Exerting Elements: The model includes the power exerting elements, such as speed, angle, position, weight, inertia, and inductance, to accurately estimate the energy losses during turnings and shape shifting.
3. Utilization of Newton-Raphson (NR) Estimation: The model employs the NR estimation technique to calculate the successive energy approximation values from each individual gaits in the system.
4. Inclusion of Inertial Effects: The model considers the inertia of the DC motors ‘I’ as a critical parameter that affects the output response, including rising time, settling time, overshoot, and steady-state error of the system.

By using this Energy Consumption Estimation Model, you can accurately estimate the mechanical energy losses in your robotics applications and optimize the system’s energy efficiency.

Conclusion

Estimating mechanical energy losses in robotics applications is crucial for improving the overall energy efficiency of the system. By considering the energy consumed during forward and backward movements, angular turns, and shape shifting, and using the Newton-Raphson (NR) estimation technique, you can develop a comprehensive energy consumption model to accurately estimate the mechanical energy losses.

Remember to incorporate the power exerting elements, such as speed, angle, position, weight, inertia, and inductance, and to consider the inertial effects of the DC motors. By understanding and minimizing the mechanical energy losses, you can optimize the performance and battery life of your robotic systems.

References

1. Manimuthu, A.; Le, A.V.; Mohan, R.E.; Veerajagadeshwar, P.; Huu Khanh Nhan, N.; Ping Cheng, K. Energy Consumption Estimation Model for Complete Coverage of a Tetromino Inspired Reconfigurable Surface Tiling Robot. Energies 2019, 12, 2257.
2. Society of Robots. Energy – How to Build a Robot Tutorials. https://www.societyofrobots.com/mechanics_energy.shtml
3. Society of Robots. Robot Energy Calculator. https://www.societyofrobots.com/energy_calculator.shtml