General Literature Review

From Control Systems Technology Group
Revision as of 13:18, 15 May 2018 by S162286 (talk | contribs)
Jump to navigation Jump to search

Literature Review

The literature review is divided in two branches: one general literature review concerning itself with robotics technology and current methods used for reforestation and an extended literature review. The latter was held to zoom in on the specific case of reforestation methods, their effectiveness and evaluation parameters, with the goal of obtaining clear cut criteria for assessing the need for a robot. And if it turns out such a need arises to obtain insights into what functionalities the robot ought to have to outperform the current methods. This extended literature review can be found in Extended Literature Review. General information about the project can be found in PRE2017 4 Groep6.

Available robotic technologies

Initially our artifact-to-be-designed was envisioned as a mobile (semi-)autonomous mobile robot which would cover terrain in need of reforestation, evaluating the ground using sensors to obtain parameters which give information about the fertility of the ground (e.g. humidity, acidity, bacteria presence, etc.) and a planting mechanism to plant the seeds if the environmental conditions are deemed favorable. Preferably the artifac-to-be-designed would be a modular robot, consisting of a basic chassis upon which modules could be placed to add or interchange functionalities such that it can also be used in other areas besides reforestation. The results of these researched items can be found below.


Modular robotics is a useful tool in the design of robots for in-field applications, as building a functional specialised robot from scratch is a time-consuming and cost-intensive process. If a modular design approach is taken, the costs of designing a robot could be severely reduced as one general robotic platform with some general functionalities would serve as the starting point, upon which modules can be placed to give the end-product the desired capabilities. A drawback of this modular design method, however, is that the design space will expand explosively due to the seemingly limitless possible configurations the robot could have (Farritor & Dubowsky, 2001) [1]. However, this design space can be brought to proportions by severely reducing it, by placing the constraints which arise from the task to be completed by the robot onto the possible configurations (Farritor & Dubowsky, 2001) [1]. By doing so any and all designs with but a singular deviation which would compromise the execution of the task are immediately discarded in the earlier stages of development.

Some examples of robots which implemented a modular design and with similar environmental working conditions as our to-be-designed seeding robot include the Small Robotic Farm Vehicle (Bawden et al., 2014) [2], the 4-wheel steering weed detection robot of Bak and Jakobsen (Back & Jakobsen, 2004) [3], the Amphibious Locomotion Robot of Li, Urbina, Zhang and Gomez (Li et al., 2017) [4] and the Reconfigurable Integrated Multi-Robot Exploration System (RIMRES) [5]. These robots have in common that they are mostly based on a singular platform, suspended by wheels for locomotion, upon which several modules (e.g. sensors, mechatronic arms, pay-loads, other deployable robots, etc.) can be placed to increase functionality.

A special class of modular robots are the so-called self-reconfigurable modular robots which can change their shape to comply with dynamic environmental constraints and task requirements. Some examples of these self-reconfigurable robots include the I(CES) cubes (Unsal, Kiliccote and Khosla, 1999) [6], M-TRAN (Murata et al., 2002) [7], ATRON (Jorgensen, Ostergaard & Lund, 2004) [8], Modular Robot for Exploration and Discovery (ModRED) (Baca et al., 2014) [9], Polybot (Yim et al., 2003) [10]. Albeit this is an interesting topic of research, for our problem at hand it will not be a feasible solution, since most of these systems are on a mesoscale application, whereas the to-be-designed deforestation robot will be a macroscale prototype.

(Semi)-Autonomous Cars

A good description of the working of remote control systems is given by the patent on remote control systems, which is granted to Mitsubishi Electric Corp. by the US government (Hashimoto et al., 1996) [11]. This patent lists all the essential components for a remote-control system such as movement detector sensor, a transmitter and receiver unit, a display device to function as the user interface, etc.

Elon Musk, CEO of amongst others SpaceX and Tesla, leader in electrical and (semi-)autonomous vehicles, describes in his vision of the autonomous car in 2016, where software updates will dominate the improvement in functionality and degree in autonomy, whereas repairs by an actual mechanic will severely reduce. There is even the potential for turning non autonomous cars into autonomous cars by means of a software update (if the non-autonomous car has software capabilities). However, there may be some legal challenges involved in this method (Kessler, 2015) [12]. Another option for mobility, in the case of failure to implement a fully autonomous vehicle, would be remote control. An operational remote control robot is actually more closely related to a remote controlled toy car than an actual remote control car due to its size. The active patent for this is owned by Matsuhiro and shows the state of the art for these machines, which do not differ much from the state of the art for autonomous cars, considering a transmitter and receiver unit is the main component [13].

An important aspect in autonomous vehicle is the path planning to get from A to B. An ambitious project, albeit one with high potential is to set up a communication network between other (autonomous) vehicles to share information regarding traffic densities, traffic jams and unforeseen obstacles due to accidents to get additional information for optimal path planning [14].

Sensors for prospecting/evaluating ground

Evaluating the soil the robot is on can be the defining factor whether it is worth it to plant new seeds in the ground, since an infertile soil will not create a new healthy forest. The design of the robot would benefit from such sensors, since it can utilize this information to determine where to plant the seeds.

Currently the soil can be read with a multitude of sensors. The most simple, but ineffective for our robot, sensor would be to use a simple plant[15] and determine whether the plant shows sufficient growth. A lot of information can be obtained from the plant, like the salinity, nutrients and available soil moisture.

This is however very inefficient and not desirable for our robot. An alternative would be to use moisture sensors[16] to determine the amount of water in the ground, since water is a critical component for a plant to grow. Further sensors include NIR reflectance sensors. These sensors can accurately measure the organic matter within the soil. This leads to an accurate picture whether the soil is fertile enough to plant seeds.

Vis-NIR sensors can also determine the amount of nitrogen and moisture in the soil. Which leads to an even more complete picture of the soil.

Humidity in the air can also help determine whether the area is suitable. An RH sensor[17] based on a Bragg grating can determine the relative humidity accurately. The optical fiber used to determine this can also house temperature, pH, pressure and more sensors. This results in a quite complete picture of the environment above the soil and can help determine the suitability for planting the seeds.

The robot can also be used in predetermined areas. Forest fires[18], for example, increase the nitrogen in the soil and in most cases the amount of carbon is also increased. This results in a soil that is suitable and fertile enough to deploy our robot on.

Drilling/plowing/seeding mechanism

A thing to keep in mind is the cost-effectiveness of the planting method. this article analyses the usage of an auger against the usage of spades.[19] While the article concludes that spades are more cost-efficient, the easier development and the lower priority of manhours would still make the auger a better option for this project.

This article shows how direct seeding is viable and what parameters have effect.[20] Using the appropriate sensors to measure these parameters would greatly benefit the project.

A kinematic analyses of an auger system[21] can be of great help when developing the seeding system for this project.

Development of a mobile powered hole digger for orchard tree cultivation using a slider-crank feed mechanism[22] gives another example of the design of an auger design, which doesn't straight up work for this case but gives some insights and can be used in this design.

An auger experiences certain loads during drilling. A mechanical analysis of the auger[23] could help in selecting the right parts for the job. This analysis has been done for bigger scale work on the moon, but is still relevant due to the use of variables which can be evaluated for their earth counterpart.

Contemporary considerations regarding reforestation

Next a branch of the general literature review focused on the problem (reforestation) instead of the possibilities for the product (robot). Inquiries were made into the scale for the need for reforestation, the involved methodologies and conditions, and lastly the employment of robotics technology in reforestation practices to assess whether there is potential for improvement of the current technologies or if there even are any current technologies at all. The results of this research can be found below.

Reforestation and Forest Fires

Fires in the Yellowstone National Park cause burn severities around the Park. Fires of different sizes cause different ecological responses. The location of the fire has the biggest influence on the biotic response of the ecosystem. Severely burned areas mainly know pine seedlings while having less vascular species than before the fire. The bigger the burned down area, the more tree seedlings sprout, and the lower the general species diversity. (Turner, M.G. et al. 1997) [24]

In recent years a lot of deforestation has occurred in Latin America and the Caribbean. But a lot of forest recovery has also sprouted, partly caused by demographic and socio-economic change. This is the main factor influencing change in wood growth. Woody vegetation change was dominated by deforestation in 2001-2010 (-542 thousand km^2), but 362 thousand km^2 was recovered. As woody vegetation depends so heavily on deforestation and reforestation these need to be controlled more extensively. (Aide, T.M. et al. 2013) [25]

It is also possible for invasive species to become the dominant factor in forests after a wildfire, this results in a new kind of forest that has a less healthy ecosystem that might spread to unaffected areas in its vicinity. In general, invasive species have a higher survival rate then the original species in the area. Invasive species reproduce faster and their seeds are carried to areas less affected by wildfires. Since the survival rate is relatively high, it is beneficial to remove the leftover seeds that survived the wildfire. [26]

Current deforestation and combat methods

Deforestation is clearing Earth’s forests on a massive scale, often resulting in damage to the quality of land. The world’s rain forests could completely vanish in a hundred years at current rate of deforestation. Consequences of deforestation are the loss of habitat for millions of species and climate changes. The most feasible solution to deforestation is to carefully manage forest resources by eliminating clear-cutting to make sure forest environments remain intact. The cutting that does occur should be balanced by planting young trees to replace older trees felled. The number of new tree plantations is growing each year, but their total still equals a tiny fraction of the Earth’s forested land. (Geographic, 2015) [27]

Rehabilitation of deforestation areas can have different steps. It can include anti-erosion works, projects for slope formation and protection and reforestation. The prototype will focus on reforestation. The forest service takes into account the type of vegetation that has been burned, the success potential of natural regeneration of trees and the general conditions, and, accordingly, shall proceed, or not, to artificial reforestation of burnt areas using native species. The purpose of reforestation is the creation of new forests, the renewal of mature forests and the recovery of degraded forest ecosystems while ensuring natural regeneration or artificial intervention (seeding or planting) for production purposes and the protection of soils. The cost of reforestation in the last 8 years was enormous due to many manhours. (Christopoulou, 2011) [28]

This website reviews many different ways for reforestation. Almost all methods are based on man work, people are physically present and are planting the seeds themselves: direct seeding. One method that is currently used that does not involve a person physically being where the seed is planted is called aerial seeding. This method plants new seeds using planes and helicopters. This method is much more efficient than being physically present on the ground but is generally outside the budget of most reforestation projects. (David, 2015)[29]

Seeds of different species have different optimal depths for sowing, with some growing best if they are buried a few inches deep in the soil, while others, including many grasses and herbs, need exposure to light to germinate and so need to be on the surface. A rule of thumb when growing vegetables and grains is to sow the seed at a depth of one to two times the width of the seed. If seeds of one species, or a mixture of seeds of different species with different needs are randomly mixed in a larger seed ball, at least some of the seeds should be in the optimal position for germination. This optimizes reforestation. (Goosem & Tucker, 2013)[30]

Reforestation also allows for augmenting the composition of the forest, species can be either suppressed or promoted in the new area. This can result in a healthier forest and allow for a more beneficial ecosystem for animals. This requires some degree of precision when replanting the forest, a new composition might result in a new dominant species. Hence precision is needed to assure certain plants might dominate the forest in certain areas. [31]

Current use of Robotics Technology in seeding/reforestation activities

The use of machinery in agriculture, the logging industry and nature upkeep is commonplace, however the application of autonomous robotic technology is still rather in its infancy. Some robotics solutions exist in these field, which are primarily categorised in 2 classes: a mobile robotic class and a drone class. Examples in the mobile robotic class include the R-Stepps project to combat desertification (Mohamed, Flavien & Pierre, 2015) [32] and the Agribot to plant seeds on farming land (Pavan et al., 2017) [33]. Examples in the drone class include the Treek'lam (Sinalkar & Phade, 2016) [34] and the quadcopter designed by Fortes (Fortes, 2017) [35]. Overall this leaves us with almost countless possibilities for either designing a new robot or improving the existing version of the mobile robot and/or drone.


  1. 1.0 1.1 Farritor, S. & Dubowsky, S.. Autonomous Robots (2001) Volume 10, pp57-65. “On Modular Design of Field Robotic Systems”.
  2. Bawden, O., Ball, D., Kulk, J., Perez, T., & Russell, R.. Australian Conference on Robotics and Automation (2014). “A lightweight, modular robotic vehicle for the sustainable intensification of agriculture.”
  3. Bak, T., & Jakobsen, H.. Biosystems Engineering (2004), Volume 87, pp 125-136. "Agricultural robotic platform with four wheel steering for weed detection.".
  4. Li, G., Urbina, R., Zhang, H., & Gomez, J. G.. International Conference on Advanced Mechatronic Systems (ICAMechS) (2017), pp 145-150. “Concept design and simulation of a water proofing modular robot for amphibious locomotion.”. IEEE.
  5. Cordes, F., Bindel, D., Lange, C., & Kirchner, F.. Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (iSAIRAS’10) (2010), pp. 38-45. “Towards a modular reconfigurable heterogenous multi-robot exploration system.”
  6. Unsal, C., Kiliccote, H., & Khosla, P. K. (1999, August). “I (CES)-cubes: a modular self-reconfigurable bipartite robotic system.”. In Sensor Fusion and Decentralized Control in Robotic Systems II (Vol. 3839, pp. 258-270). International Society for Optics and Photonics.
  7. Murata, S., Yoshida, E., Kamimura, A., Kurokawa, H., Tomita, K., & Kokaji, S. (2002). “M-TRAN: Self-reconfigurable modular robotic system.” IEEE/ASME transactions on mechatronics, Volume 7, pp431-441.
  8. Jorgensen, M. W., Ostergaard, E. H., & Lund, H. H. (2004, September). “Modular ATRON: Modules for a self-reconfigurable robot.”. Intelligent Robots and Systems, 2004.(IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on (Vol. 2, pp. 2068-2073). IEEE.
  9. Baca, J., Hossain, S. G. M., Dasgupta, P., Nelson, C. A., & Dutta, A. (2014). “Modred: Hardware design and reconfiguration planning for a high dexterity modular self-reconfigurable robot for extra-terrestrial exploration.” Robotics and Autonomous Systems, Volume 62, pp 1002-1015.
  10. Yim, M., Roufas, K., Duff, D., Zhang, Y., Eldershaw, C., & Homans, S. (2003). “Modular reconfigurable robots in space applications.”. Autonomous Robots, Volume 14, pp 225-237.
  11. Hashimoto et al. (1996). United States Patent 5554980 Retrieved from:
  12. Kessler, A.M. (2015) Elon Musk Says Self-Driving Tesla Cars Will Be in the U.S. by Summer, Retrieved from:
  13. Matsushiro. (1984). United States Patent 4457101 Retrieved from:
  14. Bagloee, S.A. et al. (2016). Autonomous vehicles: challenges, oppurtunities and future implications for transportation policies. Journal of Modern Transportation, Vol 24, Issue 4, page 283-303 section 6 Retrieved from:
  15. Edward M. Barnes, Kenneth A. Sudduth, John W. Hummel, Scott M. Lesch, Dennis L. Corwin, Chenghai Yang, Craig S.T. Daughtry, and Walter C. Bausch, “Remote- and Ground-Based Sensor Techniques to Map Soil Properties”,
  16. Boyan Kuang, “On-line Measurement of Some Selected Soil Properties for Controlled Input Crop Management Systems” (2012),
  17. Sandra F. H. Correia, Paulo Antunes, Edison Pecoraro, Patrícia P. Lima, Humberto Varum, Luis D. Carlos, Rute A. S. Ferreira, and Paulo S. André, “Optical Fiber Relative Humidity Sensor Based on a FBG with a Di-Ureasil Coating” (2012),
  18. L.M. Zavara, R. De Celis, A. Jordán, “How wildfires affect soil properties. A brief review”(2014),
  19. Preece, N. D., van Oosterzee, P., & Lawes, M. J. (2013). Planting methods matter for cost-effective rainforest restoration. Ecological Management and Restoration, 14(1), 63-66. doi:10.1111/emr.12017
  20. Atondo-Bueno, E. J., López-Barrera, F., Bonilla-Moheno, M., Williams-Linera, G., & Ramírez-Marcial, N. (2016). Direct seeding of oreomunnea mexicana, a threatened tree species from southeastern mexico. New Forests, 47(6), 845-860. doi:10.1007/s11056-016-9548-2
  21. Bogdanof, G. C., Moise, V., Visan, A. L., & Ciobanu, G. V. (2017). Kinematic analysis of soil drilling mechanism used in afforestation. Paper presented at the Engineering for Rural Development, , 16 653-658. doi:10.22616/ERDev2017.16.N131 Retrieved from
  22. Zong, W. Y., Wang, J. L., Huang, X. M., Yu, D., Zhao, Y. B., & Graham, S. (2016). Development of a mobile powered hole digger for orchard tree cultivation using a slider-crank feed mechanism. International Journal of Agricultural and Biological Engineering, 9(3), 48-56. doi:10.3965/j.ijabe.20160903.1784
  23. Cheng, Wei & Wang, Hongliu & Liu, Tianxi. (2013). Mechanical model of hollow-external-screw drill rod for lunar soil particle vertical conveying. IEEE International Conference on Control and Automation, ICCA. 1240-1245. 10.1109/ICCA.2013.6565063.
  24. Turner, M.G. et al. (1997). Effects of fire size and pattern on early succession in Yellowstone National Park, Ecological Monographs 67(4) pp. 411-433 Retrieved from:[0411:EOFSAP]2.0.CO;2
  25. Aide, T.M. et al. (2013), Deforestation and Reforestation of Latin America and the Caribbean (2001-2010) BIOTROPICA 45(2): 262-271 Retrieved from: 10.1111/j.1744-7429.2012.00908.x
  26. Kristin Zouhar, Jane Kapler Smith, Steve Sutherland, Effects of Fire on Nonnative Invasive Plants and Invasibility of Wildland Ecosystems, 2008.
  27. National Geographic. (2015, April). Deforestation. Retrieved from National Geographic:
  28. Christopoulou, O. (2011). Deforestation/ reforestation in Mediterranean Europe: The Case of Greece. Soil Erosion Studies, 3-30.
  29. David. (2015, January ). Reforestation Methods Reforestation Projects. Retrieved from Reforestation:
  30. Goosem, S., & Tucker, N. (2013). Repairing the Rainforest . Cairns: Wet Tropics Management Authority and Biotropica Australia Pty.
  31. JingYao, Xingyuan He, Hongshi He, WeiChen, Limin Dai, Bernard J. Lewis & LizhongYu, The long-term effects of planting and harvesting on secondary forest dynamics under climate change in northeastern China, 2016.
  32. Mohamed, Z., Flavien, V., & Pierre, B. (2015, October). Mobile robotics for restoring degraded ecosystems. In Global Humanitarian Technology Conference (GHTC), 2015 IEEE (pp. 273-278). IEEE.
  33. Pavan, T. V., Suresh, R., Prakash, K. R., & Mallikarjuna, C. (2017). Design and Development of Agribot for Seeding.
  34. Sinalkar, S., & Phade, G. (2016, December). Treek'lam. In Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016 International Conference on (pp. 611-614). IEEE.
  35. Fortes, E. P. (2017). Seed Plant Drone for Reforestation. The Graduate Review, 2(1), 13-26.