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Robotic Sensing and Stimuli Provision for Guided Plant Growth

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An adaptive scheme for robot localization and mapping with dynamically configurable inter-beacon range measurements.

Arturo Torres-González¹, Jose Ramiro Martinez-de Dios², Anibal Ollero³

¹Robotics Vision and Control Group, University of Sevilla, Escuela Superior de Ingenieros, c/Camino de los Descubrimientos s/n, 41092 Seville, Spain. arturotorres@us.es.

Sensors (Basel, Switzerland)

|April 30, 2014

Summary

This study introduces a novel range-only simultaneous localization and mapping (RO-SLAM) system that optimizes sensor measurements for improved accuracy and reduced computational load. The adaptive system enhances robot navigation in sensor networks.

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Area of Science:

Robotics
Sensor Networks
Simultaneous Localization and Mapping (SLAM)

Background:

Robot-sensor network cooperation is crucial for localization and mapping.
Range-only (RO) SLAM faces challenges with landmark (beacon) measurement accuracy and resource management.

Purpose of the Study:

To develop a dynamic RO-SLAM scheme that optimizes measurement gathering for enhanced accuracy and efficiency.
To balance SLAM performance (accuracy) with resource consumption (computational burden, energy).

Main Methods:

Implemented a RO-SLAM scheme with a dynamic measurement gathering module and a supervision module.
Configured measurement collection for robot-beacon and inter-beacon data at varying rates and depths.
Utilized an Extended Kalman Filter SLAM with auxiliary Particle Filters for beacon initialization (PF-EKF SLAM).

Main Results:

Achieved 34% lower map errors and 14% lower robot errors compared to traditional methods.
Reduced computational burden by 16% while maintaining similar beacon energy consumption.
Validated through experiments on the CONET Integrated Testbed.

Conclusions:

The proposed adaptive RO-SLAM scheme effectively improves localization and mapping accuracy.
Dynamic measurement management offers a significant reduction in computational requirements for SLAM systems.
This approach is suitable for resource-constrained robot-sensor network applications.