CARACTERÍSTICAS DE DISEÑO

Two experiments are designed to test the ability of CAM-SLAM to share navigation information between heterogeneous robotic platforms, Own- bot and the Pioneer. In these experiments each of the robots will be placed

in the opposite environment and be given the CAM learned by the other robot.

The first experiment will test whether navigation information learned by a lesser sensory capable robot (Ownbot) can be useful for a relatively more sensory capable robot (Pioneer) and vice versa. This experiment will run both of the robots with LIDAR sensors, with each LIDAR having different sensing capabilities and positions on their respective platforms. This means the robots can match sensor data to match decision points dur- ing navigation, potentially providing a more robust classifier matching cri- terion.

The second experiment will test the performance of CAM-SLAM shar- ing navigation information learned from two robotic platforms utilising different sensor types and SLAM techniques. The novel part of this ex- periment is sharing information between a LIDAR based platform using GMapping and a camera based platform using RatSLAM. Each respective SLAM technique is designed to operate with each sensor type, and shar- ing information between them would currently require human interaction. This is because it is not possible to directly compare 2D distance data from the LIDAR, with 2D colour data from the camera. Furthermore, GMap- ping generates a metric grid based map of the environment, and RatSLAM generates a Cartesian point map of the path the robot takes within the en- vironment. This requires a means of converting between each perspective, which is a non-trivial problem. CAM-SLAM seeks to solve this problem through learning a map of decision points (CAM) using actions as a com- mon perspective between the robots.

Cognitive Action Mapping SLAM

Results

The previous chapter detailed the CAM-SLAM algorithm, which is de- signed to learn a map of decision points within an environment. It is hypothesised that by learning a map of decision points CAM-SLAM can: reduce the amount of unnecessary mapping information, reduce the mem- ory costs of maps, and allow navigation information to be shared between heterogeneous robotic platforms. This chapter presents the results of train- ing CAM-SLAM on real-world robotic platforms and evaluates the perfor- mance of CAM-SLAM based on these criteria.

The experiments in this chapter have trained the system on two hetero- geneous robotic platforms (Ownbot and Pioneer in section 3.4.1) on levels two and three of the Cotton building (see section 3.4.2). This is to provide a comparison on the performance of CAM-SLAM learning on robots with different capabilities, and on robots utilising different SLAM approaches. Each experiment has been run through 10 trials for repeatability testing and statistical analysis using the Student’s T test. Ideally, a minimum of 30 trials would be required for statistical analysis, but these experiments are being run on real-world robots and 30 trials requires an impractical amount of hours to complete. This is due to the time constraints of run-

ning autonomous robots in an active work space, the limited battery life of the robots, and requiring personnel to be present to observe the robot in case of failures or potentially hazardous malfunctions. However, the Stu- dent’s T test is designed for small sample sizes. A small sample size will not differentiate the trials if there are small differences but will indicate large differences between trials.

Each trial is a run of 10 learning iterations. A learning iteration is a single traversal of the robot from the start location to the goal location, one itera- tion of the ACS process. This is based on previous work which has shown localisation improvements occurring within 10 learning iterations in similar work [103]. Between each learning iteration only the CAM (classifier pop- ulations) is kept, the full SLAM map (mental map) from each respective SLAM approach is discarded at the end of each iteration. It is expected that only the learned navigation information in the CAM is required for effective navigation.

To judge the performance of CAM-SLAM, the CAM for each experi- ment is compared to a human determined ground truth of the environ- ment. Ground truth is determined by judging regions within the environ- ment where the robot will be required to make a decision about the action it will follow. The ground truth for the levels two and three of the Cotton building were presented in figures 3.5 on page 68 and 3.6 on page 69.

4.1

Learning a CAM with Ownbot

The aim of this experiment was to evaluate CAM-SLAM’s ability to gen- erate useful CAMs. This was done individually on two heterogeneous robotic platforms to test the robustness of the system to varying degrees in measurement uncertainty, in both the sensors and movement of the robots. The second part of the experiment was to evaluate CAM-SLAM’s ability to learn with both a range and vision based mental map.

platforms on levels three and two of the Cotton building respectively. First, both robots were trained with their respective LIDARs and utilised GMapping for the mental mapping stage. Second, the Pioneer was trained again, on level two, this time using its camera and RatSLAM for the men- tal mapping stage. Note, both Pioneer experiments were started from the beginning with no information shared between experiments.