Treetle

Project Information

Currently precommercial thinning is faced with a shortage of experienced workers which is leading to inconsistent results. This can result in the wrong trees being cut and parts being left too dense or too bare which can impact health and value of the forest and its inhabitants. Our vision to improve this is to overcome human limitations with a precise, reliable and automated alternative.

This is done by having a robot fleet and an accompanying control system, where we deploy multiple hexapod robots that work in coordination to navigate the challenging terrain. These robots are supervised by a single operator through a mobile application for monitoring and managing them.

This system is designed to reduce the impact on the soil in the forest and prioritize biodiversity compared to heavy machinery and manual clearing saw. The robots will achieve a higher level of precision when it comes to selective thinning to accurately follow guidelines for spacing and tree species. This system is also less vulnerable to disruptions because of extreme weather, ensuring a consistent and predictable outcome.

Methods

During this project our group has worked with a structured and phase-driven approach to develop our solution. During the stretch of this course we have used multiple different methods with three primary stages: Discover, Define and Develop.

In the discovery phase we got a foundational understanding of the problems. We met with different stakeholders of forestry. Then we started with brainstorming sessions for mapping the core problems with the current way of precommercial thinning. We also brainstormed what sustainability means for us when it comes to precommercial thinning, with everything from worker longevity and consequences for nature. With these insights we created a map for the first workshop where the participants got to "roleplay" different stakeholders and go through how their day looks and how their perfect day would look which allowed us to get different perspectives, compare understandings and identify opportunity areas.

The define phase was focused on narrowing down the problem. We continued with brainstorming, starting with the possible “More-Than-Human” participant where we first got a bunch of them such as soil, insects, birds, trees, mushrooms and other animals. After having summarized all of them we voted on which to choose where we got to the Wood Dwelling Beetle. Additional research on this more-than-human participant helped us understand the biological dynamics in the forest. 

During the development phase we changed to generating and refining solutions. We started with creating a single problem formulation and personas to make sure that our ideas are aligned with real users' needs. We also brainstormed using the dark horse method to generate different ideas for solutions no matter how radical or unconventional they are. Based on these we created a number of prototypes that we used in the second workshop where the participants got some information about them and then got to act as though how they would work in the forest. Based on the feedback we got we decided on a single idea and started sketching on a “cutting robot”. We then continued on sketching, creating a physical prototype and creating a CAD model which we used in the third workshop where we got more feedback on things they thought we should add or change. Then with this we finished the CAD model and started working on the presentation.

Result

The outcome of this project is a concept for an autonomous robot fleet that is designed for performing precommercial thinning with higher precision, lower environmental impact and better scalability compared to current methods. Our final result is a hexapod robot that is supposed to work in a fleet while being supervised by a single human via a mobile application. With its six legs it is able to walk on difficult terrain with minimal disturbance of the soil, which solves the current problem with heavy machinery. The robot has a cutting tool that allows for selective thinning using sensor-based decision-making that follow guidelines for spacing, species and biodiversity. This aims to support a healthier forest while taking more-than-humans such as our wood dwelling beetle into consideration. 

The human that is supervising these robots can via the application monitor progress, assign tasks and address issues that come up. This will lower the dependency on insufficient skilled labor and lower the cost of the operation. Through the workshops with the experts in forestry we got the feedback that this system could help make sure more consistency since the robots are not affected by fatigue, inexperience or harsh weather. 

Finally this project resulted in a well-defined concept that is scalable, sustainable and efficient for precommercial thinning. It addresses the issue with human error of cutting the wrong tree and increasing the precision in selective thinning and the shortage of skilled labor. Therefore offering a promising direction for future development and real world implementation.