The development of intelligent algorithms for controlling autonom- ous mobile robots in real-time activities has increased dramatically in recent years. However, conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories. The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory (PPART) neural network for effectively managing the touring process of autonomous mobile robots in real-time. The proposed system is implemented using the AlphaBot platform, and the performance of the system is evaluated according to the obstacle prediction accuracy, path detection accuracy, time-lapse, tour length, and the overall accuracy of the system. The proposed system provide a very high obstacle prediction accuracy of 99.61%. Accordingly, the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
Fixed robotics have been widely applied for many years in numerous settings where environmental conditions are known with a very high degree of certainty. However, mobile robots have the capacity to perform a much wider range of activities, such as explore terrestrial, underwater, aerial, and outer space environments, transport cargo, complete complex tasks, perform surgery, assist in warehouse distribution centers, support security, act as a personal assistants, aid in space and ocean exploration, and provide guidance for navigation [
More recent efforts to improve the predictive capabilities of autonomous mobile robots have been based upon the development of increasingly sophisticated artificial neural networks (ANNs) [
The present work addresses these issues by proposing a potential field integrated pruned adaptive resonance theory (PPART) neural network for effectively managing the touring process of autonomous mobile robots in real-time based on a very high accuracy for predicting unexpected obstacles in the environment. The excellent obstacle prediction accuracy then facilitates the development of highly efficient robot trajectories in real time. Specifically, the potential field method is employed to conduct path exploration according to a given destination and the presence of obstacles in the path exploration space based on the Laplace equation and an energy field representation of the path exploration space, including the destination and obstacles within that space. The adaptive resonance theory (ART) neural network is then employed in conjunction with the determined obstacles to obtain the optimal navigation path that avoids all obstacles and is the shortest possible path to achieve operational objectives. Here, the optimal navigation pathways are identified by fuzzy and ART neural networks based on building maps that consist of several geometric primitives. The remainder of this manuscript is organized as follows. Section 2 presents the PPART neural network in detail. The obstacle prediction and path detection performance, and the tour efficiency obtained by the network are evaluated in Section 3. Finally, Section 4 concludes the manuscript.
The following assumptions and notations are applied to identify the obstacles, ideal travel paths, and navigation process of an autonomous mobile robot [
The tour planning working environment with in which the mobile robot is placed is defined as
A specific area to be analyzed and accessed by the robot in
The
The shape of the mobile robot is approximately a circle with a radius defined as
The mobile robot path configuration space in
The robot sensing data captured with in
The path exploration problem and any obstacles within the defined environment
Once the observation points related to the path exploration process are detected, the detected robot path must adhere to the mapping relation
During the analysis process,
The attractive potential of the destination is defined as follows:
Here,
Here,
The successful identification of
The output of this process generates vector information
The proposed ART neural network architecture and corresponding processing are illustrated in
The specific navigation choice function is defined as follows:
Here,
Furthermore, a matching process is performed for every incoming input, where upon the exact navigation path is identified successfully. Otherwise, network training is continued by updating the weight values as follows:
Here, the ART network uses the learning parameter
The complete training process is illustrated in
In the category pruning process, a fuzzy ART rectangular map is identified for every obstacle present in
For layer F2, the weight values are computed as follows.
This process is repeated for all removed obstacles in the touring environment.
Then, direct category updating is applied to the ART network to resize the rectangular map categories. In addition, the corresponding weight values are also updated as
Finally, direct category creation is applied whenever the incoming input is not matched with the trained features. Moreover, categories are created only when obstacles are present in the environment. A new rectangular map is created with the respective weight values defined above, and a new category is created (
According to the above discussion, each incoming input feature is processed by a pruned ART network that completely recognizes the obstacles present in the environment. Then, the effective navigation path is detected from source to destination by eliminating unwanted categories from the list. This process is repeated, and the mobile robot efficiently moves in the tour environment until reaching the destination.
The proposed PPART approach was developed using the AlphaBot robotic development platform. The development platform is compatible with Ardunio and Raspberry Pi, and includes several components, such as a mobile chassis and a main control board for providing motion within a test environment boundary. The effective utilization of the components and compatibility helps to predict the obstacles, line tracking, infrared remote control, Bluetooth, ZigBee process, and video monitoring. The mobile robot path exploration and navigation process performance provided by the PPART neural network is evaluated according to its obstacle prediction accuracy, path detection accuracy, error rate, and overall system accuracy based on different evaluation metrics. The accuracy of obstacle prediction was compared with those obtained using three existing machine learning techniques, including ANN-,CNN-, and SNN-based methods.
Methods | Number of touring navigation attempts | ||||||||
---|---|---|---|---|---|---|---|---|---|
50 | 75 | 100 | 125 | 150 | 175 | 200 | 225 | 250 | |
ANN | 95.11 | 95.53 | 95.04 | 97.13 | 95.59 | 96.02 | 96.07 | 96.22 | 96.27 |
CNN | 97.32 | 95.59 | 97.09 | 97.32 | 96.87 | 98.02 | 96.99 | 97.96 | 97.09 |
SNN | 97.9 | 98.36 | 97.383 | 97.993 | 98.443 | 97.823 | 98.013 | 98.253 | 98.023 |
PPART | 98.21 | 98.48 | 98.92 | 98.23 | 98.7 | 98.92 | 98.78 | 98.82 | 98.97 |
In addition, the efficiency of the obstacle detection process was analyzed, the results of which are listed in
Methods | Time (s) | ||||||||
---|---|---|---|---|---|---|---|---|---|
0.5 | 1.0 | 1.5 | 2.0 | 2.5 | 3.0 | 3.5 | 4.0 | 4.5 | |
ANN | 96.34 | 96.76 | 96.27 | 98.36 | 96.82 | 97.25 | 97.3 | 97.45 | 97.5 |
CNN | 98.55 | 96.82 | 98.32 | 98.55 | 98.1 | 98.56 | 98.22 | 98.34 | 98.32 |
SNN | 98.78 | 98.92 | 98.613 | 98.63 | 98.73 | 98.83 | 98.43 | 98.83 | 98.53 |
PPART | 99.44 | 99.31 | 99.15 | 99.36 | 99.63 | 99.15 | 99.01 | 99.35 | 99.32 |
The path navigation accuracy values are listed in
Methods | Number of touring navigation attempts | ||||||||
---|---|---|---|---|---|---|---|---|---|
50 | 75 | 100 | 125 | 150 | 175 | 200 | 225 | 250 | |
ANN | 97.43 | 97.85 | 97.36 | 99.45 | 97.91 | 98.34 | 98.39 | 98.54 | 98.59 |
CNN | 98.343 | 96.613 | 98.113 | 98.343 | 97.893 | 99.043 | 98.013 | 98.983 | 98.113 |
SNN | 98.923 | 99.383 | 98.406 | 99.016 | 99.466 | 98.846 | 99.036 | 99.276 | 99.046 |
PPART | 99.233 | 99.503 | 99.43 | 99.253 | 99.723 | 99.43 | 99.53 | 99.843 | 99.23 |
The efficiency of the navigation path identification process was analyzed. The results, listed in
Methods | Time (s) | ||||||||
---|---|---|---|---|---|---|---|---|---|
0.5 | 1.0 | 1.5 | 2.0 | 2.5 | 3.0 | 3.5 | 4.0 | 4.5 | |
ANN | 96.68 | 97.1 | 96.61 | 98.7 | 97.16 | 97.59 | 97.64 | 97.79 | 97.84 |
CNN | 98.89 | 97.16 | 98.66 | 98.89 | 98.44 | 98.9 | 98.56 | 98.68 | 98.66 |
SNN | 99.12 | 99.26 | 98.953 | 98.97 | 99.07 | 99.17 | 98.77 | 99.17 | 98.87 |
PPART | 99.78 | 99.65 | 99.49 | 99.7 | 99.97 | 99.49 | 99.35 | 99.69 | 99.66 |
The error rates of the three different classifiers considered in comparison with that of the PPART approach are illustrated in
The present work addressed the generally inefficient tour trajectories obtained by conventional intelligent algorithms due to the poor prediction of unexpected obstacles in the environment by proposing the PPART neural network. The potential field method was employed to conduct path exploration according to a given destination and the presence of obstacles in the path exploration space based on an energy field representation of the path exploration space. An ART neural network was then employed in conjunction with the determined obstacles to obtain the optimal navigation path that avoids all obstacles and is the shortest possible path to achieve operational objectives. The proposed system was implemented using the AlphaBot platform, and the performance of the system was evaluated according to the obstacle prediction accuracy, path detection accuracy, and the overall accuracy of the system. These results demonstrated that the proposed system provides a very high obstacle prediction accuracy of 99.61%. Accordingly, the proposed tour planning design effectively predicts unexpected obstacles in the environment, and thereby increases the overall efficiency of tour navigation. In future work, we will seek to improve the efficiency of the mobile robot navigation process by applying optimized techniques.
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