6.2 A Comparative Scale
6.4.4 Statistical Testing
The aim of this section is to examine the statistical significance of the results that have been presented. To achieve this T-tests were used. A T-test involves the postulation of a null hypothesis that is rejected if the T-value is below a certain threshold; in this case a value of 0.05 was chosen
Table 6.3: T-test values examining the statistical significance of the EKF localised RVF experi- ments vs the absolute known position RVF experiments.
Table 6.4: T-test values examining the statistical significance of the Levy random walk experi- ments vs the EKF localised RVF experiments.
to represent 98% significance.
The first set of T-tests conducted were to compare the EKF localised swarms to the APK swarms. For each parameter the null hypothesis was that ‘localisation should not affect the result’. The results for these T-tests are summarised in table 6.3.
These results show that no matter the swarm composition, or speed, the null hypothesis may be accepted. That is to say that localisation does not affect the performance of the RVF framework.
The second set of T-tests were to investigate the Levy walk results when compared to the RVF framework results. For each parameter the null hypothesis was ‘the RVF framework does not explore an environment more efficiently than the Levy random walk’. The results from the Levy walk T-tests are presented in table 6.4.
These results show that in each case the null hypothesis may be rejected. Thus, utilising the RVF framework produces a statistically significant effect on the efficiency of exploration when compared to a Levy random walk.
Overall, the T-tests show that the results gathered in this chapter are statistically significant; the efficiency of exploration is considerably improved by using the RVF framework over the Levy random walk; and the introduction of EKF localisation to the RVF framework does not affect the exploration time.
6.5
Chapter Summary
The aims of this chapter were: to investigate the effectiveness of the RVF framework whilst incorporating localisation using the EKF; and to evaluate the RVF framework based on a comparative scale.
It was found that the efficiency of the RVF framework was not affected by the introduction of localisation. This result was shown to be statistically significant regardless of the speed of the swarm or the swarm composition. Additionally, it was found that the accuracy of the occupancy grid generated by the RVF framework diminished as the speed increased.
A comparative scale was defined so that the effectiveness of the RVF framework could be analysed. At the lower end of the scale is the Levy random walk, whilst the top of the scale is occupied by the perfect raster. The RVF framework was placed on this scale both to assess the exploration time and the accuracy of the map that it produced. It was found that the RVF framework was significantly closer to the perfect raster on both scales. This suggests that it is an efficient and accurate exploration strategy.
Overall, the RVF framework has proven itself to be an efficient method for instigating exploration in an unknown environment. It has been shown that it is possible to use the RVF framework to produce simultaneous localisation and mapping. In addition, the RVF framework performs more efficient exploration when implemented on a heterogeneous swarm. Thus, if a heterogeneous swarm were to be utilised for exploration and mapping of a nuclear cave environment, the RVF framework would serve as a suitable control architecture.
This chapter completes the examination of the RVF framework as a control method. This thesis has examined the three key components that comprise a swarm capable of exploration and mapping of a nuclear cave environment: sensing, locomotion and control. The final chapter of the thesis will conclude by discussing each of these elements and how they might combine, along with providing suggestions for future work.
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his thesis examined three main components of a heterogeneous robot swarm tasked with characterising an unknown nuclear cave environment: sensing, locomotion and control. It is the belief of the author that through study of these three characteristics, this thesis has demonstrated that it is possible to utilise a heterogeneous swarm of autonomous robots to characterise a nuclear cave environment.This chapter will draw together the work from this thesis and discuss its findings in a wider context. The structure of this chapter is as follows: first, a summary of the work from this thesis will be provided; second, a discussion about the key findings of the thesis will be presented; and finally, remarks and suggestions for future work will be given.
7.1
Thesis Summary
In chapter two the literature surrounding nuclear and swarm robotics was reviewed. It was shown that there is a paucity of material that focuses on the use of heterogeneous swarms, especially in the fields of autonomous mapping and nuclear environments. It was also discovered that there exist few implementations of virtual potential fields for use in exploration and mapping; the main applications in the literature were pattern formation, path planning and spatial distribution. This motivated investigation into the physical parameters that could define heterogeneity (sensing and locomotion) and the use of potential fields for control of such a swarm.
Having found that utilisation of a heterogeneous swarm for exploration and mapping of a nuclear cave environment was a novel concept, it was decided that the physical traits defining heterogeneity should be investigated, these were sensing and locomotion. In chapter three the sensing modalities that are most desirable in a nuclear cave were explored, relying on the
expertise of an industrial expert from the Nuclear National Laboratory; these modalities are: distance, radiation, pressure, humidity, image capturing, temperature, chemical and tactile perception. In each case the applicable sensor technologies were reviewed and compared, with the chapter concluding with suggestions for the most suitable sensor for each modality.
Following a review of the sensory capabilities of a heterogeneous swarm, it seemed prudent to examine the second feature that could define heterogeneity, locomotion. The first part of chapter four reviewed the locomotion strategies applicable to traversing a nuclear cave environment: ground locomotion, wall climbing robots, flying robots and supplementary modalities. Subse- quently, the most promising ground locomotion strategies were compared experimentally using a LEGO mindstorm EV3. It was found that a spherical robot, coupled with a tracked robot would give the greatest locomotive benefits whilst exploring a nuclear cave. Finally, chapter four described the design of a novel detachable grappling hook that could allow for ground robots to surmount obstacles and attain a bird’s eye view of the environment.
Chapters three and four represent a study into the first two elements comprising a hetero- geneous swarm: sensing and locomotion. The remaining component was control. Chapter five introduced the ‘Reactive Virtual Forces’ framework, designed to control a heterogeneous swarm of robots in the exploration and mapping of a nuclear cave environment. This control architecture utilises virtual analogues of the fundamental forces of nature to guide robots to unexplored regions of an occupancy grid. The ‘Reactive Virtual Forces’ framework was tested in MATLAB and embodied simulations and found to operate more efficiently with a heterogeneous swarm, when compared to a homogeneous swarm.
Chapter six extended the work on the ‘Reactive Virtual Forces’ framework conducted in chapter five. It was found that it is possible to utilise the ‘Reactive Virtual Forces’ framework in conjunction with an extended Kalman filter to produce simultaneous localisation and mapping. The ‘Reactive Virtual Forces’ framework was then placed on a comparative scale. The lower end of this scale was occupied by a Levy random walk, while the top end was defined by the perfect raster. It was found that the ‘Reactive Virtual Forces’ framework was close to the top of this scale, showing that it is an efficient exploration algorithm.
Overall, the key contributions of the thesis are:
• The consolidation of knowledge relating to the characterization of a nuclear cave environ- ment utilizing a heterogeneous swarm of autonomous robots
• A review of sensors for use in a nuclear cave environment, carried by a heterogeneous swarm of mobile robots. This lead to the finding that a heterogeneous swarm that explores a nuclear cave is the most likely to benefit from using: a LIDAR sensor for range finding; a scintillator detector to determine the presence of radiation; a piezoresistive sensor to acquire information about pressure; a capacitative sensor to measure humidity; a cheap disposable camera to attain images of the cave; a thermistor to determine temperature; an
electrical transducer to examine the presence of chemicals; and finally, whiskers to enable tactile perception.
• The review of, and experimentation comparing, locomotion strategies that are of benefit in a nuclear cave environment. This lead to the discovery that a swarm exploring a nuclear cave should utilise multiple locomotion strategies including: tracked and spherical locomotion for ground robots; suction to enable adhesion of wall climbing robots; rotorcraft as flying robots, implementing a gimball to prevent fatal collision; and finally, a robotic grappling device as a supplementary modality.
• The novel design of a detachable grappling hook that could be used to supplement ground locomotion strategies.
• The design of the ’Reactive Virtual Forces’ framework, capable of efficiently controlling a heterogeneous or homogeneous swarm for exploration and mapping. Within this framework novelty lies in the sole use of potential fields for mapping and organization, without the use of a shared map, along with the examination of performance on both a heterogeneous and homogeneous swarm.
• The utility of simultaneous localisation and mapping of an unknown environment through the combination of an extended Kalman filter and the ’Reactive Virtual Forces’ framework. Previously, SLAM has not been achieved with virtual fields alone. The design of an explo- ration performance scale that can be used to assess the quality of exploration strategies and it is applied to the ‘Reactive Virtual Forces’ framework.
The implications of these finding are discussed in the subsequent section.