Modelos de Precios de Vivienda más Recientes y Resultados

On average it was found that the grapple could support a mass of 2.4kg, or thirty times its own weight. This means that the grapple fulfilled specification (3) and can support a robot of mass 2.4kg or less. Having a robot suspend itself from a grapple in nuclear cave would require little energy and allow the robot to attain a bird’s eye view of its environment. This would enable a ground robot to generate a more complete map of a nuclear cave environment.

The minimum height which the grapple was able to secure itself from was found to be 6cm. This means that the minimum speed required to engage the grapple is:

(4.2) _{v =}p_{2gh =}p_{2 ∗ 9.81 ∗ 0.06 = 1.08m/s}

The knowledge of this speed serves as a specification for the design of a future launching mechanism; a launching mechanism should be capable of achieving this speed at the point of contact between the grapple and its target. This would then allow for the grapple to be utilised by a ground robot in a nuclear cave environment.

The results from the consistency tests, from the five varying heights are summarised in table 4.6.3:

Drop Height (cm) Successful Engagements Percentage Success

7.5 26/30 87%

10 19/30 63%

12.5 20/30 67%

15 19/30 63%

17.5 3/30 10%

Table 4.1: Shows the results of the consistency investigation

These provide interesting results. It was found that when the grapple was dropped from a height of 7.5cm it was able to achieve a success rate of 87%. This shows the design of the grapple is capable of grasping pipework. The high rate of success is deemed to be a combination of two factors: the attainment of the minimum activation speed; and the predictable and consistent ballistic stability.

Consistency at heights of 10cm, 12.5cm and 15cm is stable around 65%. The reduced per- formance at these heights when compared to 7.5cm is due to the uneven weight distribution of

the grapple. Attempts were made to remedy this instability using a counter weight. However, ballistic instability remained. In future it is hoped that the locking mechanism of the grapple could be centralised to reduce this effect. This instability was further realised at a height of 17.5cm, where only a 10% success rate was recorded.

The results of the grappling experiments provide proof of concept. It was shown that the grapple was capable of latching onto pipework and supporting the mass of a small robot. In the future, it is hoped that a detachable grapple could aid a heterogeneous swarm of robots in the task of exploration and mapping of a nuclear cave environment through providing a supplementary modality to overcome obstacles and attain a bird’s eye view of the cave.

4.7

Chapter Summary

The focus of this chapter was to provide an analysis of the various locomotion strategies that are offered to robots within a nuclear cave environment and present the novel design of a supplementary modality, in the form a detachable grappling hook [27]. Having reviewed the literature on robotic locomotion, the most promising ground locomotion strategies were compared through experimentation using a reconfigurable LEGO Mindstorm EV3 robot. These strategies were: wheels, whegs, tracks and spherical locomotion. After conducting these experiments and examining the literature it was found that the most promising methods of locomotion within the cave are as follows:

• Ground locomotion - tracks and spherical robots • Wall-climbing - utilising suction for adhesion

• Flying - rotorcraft utilising a gimball to prevent fatal collision

• Supplementary modalities - robotic grappling, to aid ground locomotion by allowing such robotics to extend into three dimensional exploration with low energy requirements compared to flying

It has been shown that there is a paucity of work focussing on the detachable grappling of mobile robots. Grappling is a useful tool in the nuclear cave environment due to the array of complex pipework presented as a target. In this chapter the design and testing of a novel detachable grappling hook was presented. It was found that the grapple could support the weight of a small robot and could be utilised to hoist a robot over obstacles within the nuclear cave. It is inherent in this method of transferring a ground robot into the third dimension of movement that it could be achieved with very low energy requirements compared with any flying methodology, and this could be a very significant advantage in this application domain.

Overall choosing the correct locomotion strategy within a nuclear cave is of paramount importance, due to the complex environment that it presents. The results of this chapter suggest

that it would be prudent to use a combination of locomotion strategies to overcome the obstacles present within a nuclear cave. This motivates the use of a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment.

The design of such a swarm is comprised of three key components: sensing, locomotion and control. So far, chapter three has discussed the various sensing modalities available to robots within a nuclear cave and made suggestions on the most applicable. Chapter four has discussed the potential locomotion strategies that could be utilised to explore the nuclear cave. In addition, it outlined the design of a detachable grapple that could be used to increase the capabilities of ground locomotion strategies. In chapter 5 and 6 the control component will be discussed, with particular focus on the implementation of the ‘reactive virtual forces’ framework used to control a group of heterogeneous robots in the exploration and mapping of the floor of a nuclear cave environment.

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he three key characteristics that a swarm must possess in order to explore and map a nuclear cave environment are: sensing, locomotion and control. Having reviewed the sensory and locomotive modalities available to a heterogeneous swarm within a nuclear cave, it is next necessary to examine the control of such a swarm.

As was discussed in chapter one, potential fields have largely been used for spatial distribution, pattern formation and path planning [251] [19] [274] [223] [199] [124]. These applications are not unlike exploration: robots must move to specific areas of an environment whilst avoiding collision. These similarities give rise to a question – if spatial distribution, pattern formation and path planning may be dictated by potential fields, is it possible to employ the same method for exploration using a heterogeneous swarm? Answering this question is the focus of this chapter.

Some work has sought to answer this question with regards to a homogeneous team of robots [155]. However, heterogeneity appears to be an under-investigated topic within exploration and mapping. As discussed previously, a heterogeneous swarm is likely to be the best solution to the exploration and mapping problem presented by a nuclear cave due to the need for diverse sensing and locomotive capabilities. This diversity motivates the design of a control strategy capable of commanding a heterogeneous swarm in the exploration and mapping of a nuclear cave environment: this gave rise to the ‘Reactive Virtual Forces’ (RVF) framework described in this chapter.

The RVF framework treats robots as particles under the influence of virtual analogues of three of the four fundamental forces of nature: the electromagnetic force, the gravitational force and the strong nuclear force. These forces guide the exploration and mapping behaviour of a swarm of robots in conjunction with an occupancy grid representation for geometric mapping.

of robots and compare its performance on a heterogeneous and homogeneous group of robots. This section is organised as follows: first, the occupancy grid representation used to define the map will be outlined; second, the forces utilised to guide robots in the RVF framework will be explained; third, the simulation design and experimental methodology will be described; fourth, the embodied simulations and associated experimental methodology will be highlighted; subsequently the results from these experiments will be presented; and finally, conclusions will be drawn.

Much of the work described in this chapter was presented at the TAROS 2017 conference, where it was shortlisted for best student paper [28].

5.1

Occupancy Grid Representation

The representation used to define a geometric map for the RVF framework was the occupancy grid. An occupancy grid divides an environment into discrete sub-spaces and stores the likelihood that each sub-space is occupied by an obstacle [77]. As a robot moves through the environment this likelihood is updated based on sensor data. This representation was chosen for two reasons, the first is that it is a prevalent representation in the robotics literature and therefore straightforward to implement. The second reason is that the output of an occupancy grid is easily interpreted, which could be important for use by plant workers who are required to plan decommissioning of a nuclear cave environment.

The implementation used in this work splits the environment into squares, requiring only knowledge of the perimeter of the nuclear cave. This is a reasonable assumption of knowledge, as plans can show the size of the room being mapped. Having split the environment into these squares, each is represented by an element in a matrix. Elements in the matrix are searched through recursively and updated based on a robot’s sensor data. The possible updates are as follows:

• Obstacle detected – corresponding grid value increased by 3 • No obstacle detected – corresponding grid value decreased by 1 • Robot occupies grid square – corresponding grid value decreased by 5

The discrepancy in update value between an obstacle being detected and not detected is because the presence of an obstacle is more accurately detected than the lack of an obstacle; this was important to reflect in the update of the occupancy grid. If a robot lies within a grid square then the likelihood that it contains an obstacle is severely reduced, hence the larger decrease in occupancy value associated with this scenario. The maximum likelihood of occupancy is 100 and the minimum is 0, cells are initialised with a value of 50. The update values of 1, 3 and 5 were chosen as they allowed the map to tend towards an accurate representation at an acceptable rate. An example of an occupancy grid can be seen in figure 5.1.

Figure 5.1: Shows a robot navigating an obstacle filled environment with an example occupancy grid.

The update process leads to three classifications of cell. These will later be utilised in the RVF framework to aid in exploration and mapping. The classifications are:

• Known cell:ν≥ 60 orν≤ 40 • Unknown cell: 40 <_ν< 60

• Frontier cell: an unknown cell next to a known cell, hence on the frontier of exploration and thus also with a value in the range 40 <ν< 60

Whereνis the occupancy value. The values of 40 and 60 were chosen as this meant that repeat observations of a cell were necessary in order to classify it as known or unknown. These cells will later be used to determine attractive regions for the RVF framework.

Robots communicate their maps to one another when they are in sensor range. When a map is received it is compared cell by cell to the map the robot currently holds. The average between the communicating robots’ cell values are then found. Subsequently, these values replace the corresponding cell value in each robots’ map. This method allows robots to correct false occupancy measurements over time. In addition, this allows the speed of the mapping process to be increased. This is because, if maps are shared, a single robot does not need to visit all areas of the map.

This section has described the implementation of occupancy grid mapping that is utilised in the RVF framework. In the next section the forces used in the RVF framework will be outlined.

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