Hardware Options for Locating and Monitoring a Quadruped.

[lukass]

I am developing an all-round quadruped robot that will be able to support the advancement of this research work through its hardware features which we intend to equip it with. Here’s a list of the hardware I plan to integrate:Here’s a list of the hardware I plan to integrate:

LiDARS using SLAM (Simultaneous Localization and Mapping)

To create the smart footwear with responsive control, force sensors will be implanted in the soles of the shoes.

Radio for Networking or exchanging control.

Camera for Vision Systems

While LiDAR and SLAM will be used for the localization, the thing that’s appealing me most is doing Kalman filtering and state estimation because I have no experience with them. I’m likely to test the performance of a SLAM with that of Kalman filtering, state estimation, or other related to this sphere.

Questions:

Is the Affordability and Precision Offered by Kalman Filtering and State Estimation as Good as a Commercial-Off-the-Shelf (COTS) IMU?

However, I am aware of the fact that upward pitch and downward pitch generally make use of gyroscope compensation. Despite the fact that a ten axis sensor (gyro, barometer, and magnetometer) with sensor fusion which is common in drones would potentially allow for better performance, what would be the solution cost and the power consumption?

Speaking of implementing the GPS and TOF (Time-of-Flight) capabilities can increase the localization precision?

Additional Sensor Recommendations:

I would also like to know, what other sensors or technologies might You suggest, they would help my project.

Thank you!

 

[MikeSXS]

Cost efficiency and accurateness of Kalman filtering compared to COTS IMU:

Kalman filter used in sensor fusion is a well-proven technique of state estimation, making for more accurate results achieved by IMUs. While off-the-shelf IMUs can be easily found at attractive pricing, implementing Kalman filter will dramatically enhance precision and performance, and such kind of improvement is more likely to happen in dynamic situations. Indeed, Kalman filtering cost depends on your creation and the complexity of implementation. If budget is the main issue, then cost benefit analysis of the extra expenses for powerful computers against accuracy improvement using Kalman filtering is very much important.

Gyroscope Compensation and Sensor Fusion:Gyroscope Compensation and Sensor Fusion:

Implementing the ten-axis sensor, consisting of a gyro, barometer and magnetometer, with a sensor fusion is a typical mechanism drones use and translates the 3D motion and the orientation into the orientation and motion data. Aside from this, the aforementioned configuration can optimize your robot's stability and accuracy of orientation. The solution costs will heavily be influenced by the sensor quality and the complexity of the sensor fusion algorithms that are used. For the case of more advanced procures of sensors plce attention that usually has higher energy consumption, so it also may be responsibled decision to choose best performance with energy requirements. The relevant superior sensor fusion modules might be a bit expensive at the beginning, but without using additional sensors and extra processing power, they can be cost-effective in the long run.

GPS and TOF Capabilities for Localization Precision:GPS and TOF Capabilities for Localization Precision:

Combining GPS and TOF sensors is a great combination, and especially outdoors or those larger environments, one can achieve exacting localization precision. GPS contributes to definition of broad locations instead of TOF sensors used for precise distance measurement with these two systems helping LiDAR system and SLAM system to get better results. This twin nature will make the robot to detect the locations with the higher degree of accuracy.

Additional Sensor Recommendations:

Inertial Measurement Units (IMUs): High-precision IMUs can add extraordinary motion tracking capabilities and improved stability in the motion detection and control of smart devices.

Ultrasonic Sensors: Perfect for obstacle detection and sentiment the chances of collision in different environment.

Infrared Sensors: May help to track thermal signatures and overcome obstacle-detecting functions.

Pressure Sensors: Maximize the robot's performances with the ground through sensed feedback on ground contact and surface texture.

Environmental Sensors: These types of instruments are used to track environmental elements such as temperature, humidity, and air quality that may be vital in some applications or conditions.

Cost and Power Considerations:

Solution Cost: The final cost of the project will rely of upon the selection of sensors as well as of the level of sensor data fusion complication. Instead, the investment to acquire high-end sensors and up to date processing units may be big at the beginning, but the output of increased performance and decreased maintenance costs over the term of use can justify the investment.

Power Consumption: Very much real and quite often sensors and more upgraded algorithms will consume more energy. It is important to be careful with the number of sensors installed and their power consumption in respect to the robot's capacity of the robot’s battery.

The sensors you choose and incorporate will greatly contribute to the enhancing of your quadruped robot's abilities, resulting in a more upgraded robot capable of sophisticated research and real life applications.