A TRIZ-Integrated Conceptual Design Process of a Smart Lawnmower for Uneven Grassland

Abstract

Existing smart lawnmowers, while convenient to use, have significant limitations, such as a lack of manoeuvrability on uneven agricultural grassland (constraint 1), high charging frequency (constraint 2) and low local market penetration (constraint 3). Although the effectiveness of the theory of inventive problem solving (TRIZ) has been demonstrated in several design studies, there also seems to be a lack of research addressing the design difficulties of smart lawnmowers using this method. With the use of the TRIZ method, this study seeks to conceptually design a smart lawnmower for uneven grassland. Tools from TRIZ were used, including cause-effect chain analysis, technical contradictions, physical contradictions, and substance field modelling. In developing a design concept, constraints were solved by inventive principles, separation strategies and standard inventive solutions. For constraint 1, the following solutions were chosen with the appropriate principles: using larger wheels (#17, another dimension: using a second or third dimension), a pivot design (#30, flexible shell: replacing rigidity with flexibility and movability) and replacing the motor with one that has more power or torque. For constraint 2, the following solutions were chosen: to reduce weight, add holes in the mower housing (#31, porous materials: making an object porous or adding porous elements) and attach a solar panel to recharge batteries with solar energy (#28, mechanical substitution: using electric, magnetic or other fields to interact with object). Using other materials or technologies to minimise costs (#13, the other way around: using the opposite way) and a modular design concept to reduce maintenance costs (#1, segmentation: dividing an object into independent parts) were the chosen ways to solve constraint 3. Conceptualisation and design analysis were also performed. Although the effectiveness of the concept is unclear, these suggestions are supported by previous research and could potentially solve some of the problems with smart lawnmowers.