According to the National Academy of Engineering (NAE), one of the 14 challenges to enhance life on this planet is ‘How can robots assist to explore the cosmos.’ Sending a person to Mars is expected to cost more than $100 billion [1], but deploying a robot costs merely $2.9 billion [2]. Sending an individual additionally increases the hazards to one’s safety. If a robot on an exploratory trip malfunctions, the worst-case scenario is that the robot does not return. However, if a person is involved in a botched operation, the consequences are even direr [3].

Robotic systems can perform tasks that individuals are unable to perform as well as survive severe conditions such as radiation and extreme heat. Robotic systems also do not require a continual supply of water or food and can be recharged using solar panels; some can also be custom-built to perform specific jobs and outfitted with sensing technologies to gather data [4]. Individuals undertaking such jobs may incur extraordinarily high costs since specific equipment must be created to protect them.


Designing a portable, cost-effective robot to explore hazardous environments for the safety of humans.

2Gs (Two end goals):

1. AI navigation system 

  • LiDAR sensor system to detect obstacles and collect data
  • Collision avoidance with reinforcement learning
  • Robot Operating System (ROS) for sensor control
  • SLAM for data mapping and obstacle predictions

2. Optimized structure design 

  • Design and 3D print a feasible chassis to hold all the components
  • Optimize structure to improve durability
  • Assemble all hardware components and test the enclosure to fit them

Our expected outcomes consist of the following:

  1. Maze navigation is accomplished by a machine learning algorithm
  2. An omni-wheels control system
  3. A lightweight chassis (Optimized topology design)
  4. If possible, we would also like to have ROVA navigate at various elevations and steep inclines

Education Objective:

By completing the ‘BEGINNER‘ assignment, high school or college students learn 3D printing and laser cutting skills. They will be required to design, develop and 3D print a stackable storage container for hardware components such as nuts, bolts and screws before proceeding onto the robot structural system; here, they will be required to laser-cut the 2D chassis framework.

Undergraduate students must develop their fundamental programming expertise in either, python or C, while experimenting on the Nvidia Jetson Nano. Additionally, they will also study mechanical structures by creating a three-dimensional robotic systems chassis with CAD software such as AutoCAD, onshape, SOLIDWORKS to house electronics for the robot. Another activity will be to calibrate any types of sensor, which requires basic programming, and integrating it into the microprocessor. This project can be connected to an undergraduate class known as ‘INTRODUCTION TO ROBOTICS‘.

Research Objective:

This project’s area of interests is data gathering and obstacle mapping in hazardous situations. The robotic navigation system will be enhanced via machine learning; ROVA should be capable of autonomously detecting, recording, and avoiding obstacles as ROVA may have to traverse difficult terrain with varying altitudes and recognise impediments that cannot be traversed.


The structure system can be divided into five sub-systems: Motion, Power Supply, Sensor, Microcontroller, and Chassis. The Optimal design focus on the chassis design and this sub-system is also the foundation for all other four sub-systems. The arrows in the picture below show the correlation between the subsystems. There are also several prototyping methods used for designing the structure. The major tools are CAD and 3D printing: use CAD to design or simulate the environment and use 3D printing to produce physical components for testing or assembly. 


The initial ideas comprise two parts in two pictures (Below) respectively: the left picture indicates a general structure of the robot and what potential features may be added; the right picture focuses on how to use the omni-wheels [5], particularly how to coordinate each of the four wheels to determine movements of the robot.

The blue circle in the left picture, for example, shows a design idea of having a drill in the middle bottom of the robot to collect samples or break through obstacles. The yellow circles demonstrate different types of chassis layouts [6]. The green circle in the right picture similarly shows a design of the chassis by adding a damping system to optimize stability. The orange circles illustrate different layouts of omni-wheels based on existing products.


Ideas of base structure design and movement of Omni wheels 


Alternatives are ranked using a point-based system. The original plan is to adopt an existing robot structure while the group focuses on developing the software. Thus, the scoring system evaluates the robot structure through aspects such as simplicity, feasibility, and material usage. 

The first two evaluating criteria are worth ten points each, while the last criterion is worth five points. If a condition is met, the structural alternative receives five points. And if it is not entirely accomplished, fewer points will be given.

1. Simplicity

The structure should be simple to construct while retaining essential characteristics. To reduce time, the production process should be as efficient as possible.

2. Feasibility

The structure should be simple to construct while retaining essential characteristics. The production process should be as efficient as possible to reduce time.

3. Efficiency of Material Usage

ROVA is required to be lightweight, and its dimensions should not be too large or it cannot be fit into small spaces. If it is compact and lightweight, this will also help to conserve resources.


In conclusion, the third alternative scored the highest of 20 points and was chosen as the final design. This robot design also comes with rudimentary code to help prevent obstacles, which further helps with programming. Nevertheless, after a review of the electronic components for this project, it is clear that the design structure cannot be directly used due to the unique combination of these components. The chassis must be specially designed to fit the components such as the motor driver, power supplies, LiDAR, ultrasonic sensor, line tracking sensor, and a microcontroller.



A lean canvas breaks down an aim into several business segments. All the segments are tailored to meet customer demands and achieve company growth. Starting from problem and solution statements, the lean canvas shows a basic financial analysis in the cost structure and revenue stream segments. The lean canvas also demonstrates the uniqueness of the product in the unfair advantage.  The Customer Segments shows the targeted end-users of the product and the Channels aim to deliver products to end-users in time.


Humans cannot perform certain exploratory tasks and safety risks and costs are too high for humans to work in severe conditions


A wheeled, remotely controlled robot adaptable for different terrains

Unique Value Proposition

‘Mission impossible, not for robots’

Unfair Advantage

Multiple patentable structure design and sensor technology

Customer Segments

•High-tech companies developing products to work in severe conditions (E.g., High pressure)

•Governments that sponsor exploratory projects in unknown chartered

Key Metrics

•Achieve break-even point within 5 years

•50% profit margin

•P/E ratio of 30


•TV, radio, and magazine ads

•Endorsement by senior advocacy groups

•Showroom and trade show

Cost Structure

•Manufacturing and material ~30%

•R&D ~40%

•Marketing and sales ~25%

Revenue Stream

•Recurring revenue in maintenance via contracts with high-tech companies

•Transaction revenue from retailers & internet

•Licensing revenue (Patents)


The pictures below demonstrate different layers of the robot. They also demonstrate the location of various components on each layer. There are in total three layers, with the top layer having an extra layer for the LiDAR. Each layer is designed based on the electronic components. The structure of this robot is special as a robot with such a combination of components is unique and existing chassis cannot be used. (Click the images below for an expanded view)


This project may be separated into four sections based on its intricacy. Each section is interconnected with the others, and the individual may select which section to begin with depending on their skillset. Our recommendation is that High school and College students should begin with the “BEGINNER” project; Undergraduates should begin with the “INTERMEDIATE” project; and Master’s students may begin with the “ADVANCED” project but should consider the “INTERMEDIATE” project as it covers robotic hardware. The “EXPERT” project is primarily intended for PhD students.


Click the following images to view tutorials. Each tutorial is followed by a Youtube video in the end. The tutorials aim to be precise and help students to quickly develop their hand-on skills. It is strongly suggested that students practice the skill immediately after reading the tutorials to solidify their knowledge. And if students find anything confusing, they can go back and reread the parts of the tutorials for solutions. There are four tutorials:  1) Robot structure prototyping 2) Robot components 3) Laser cutting tutorial 4) 3D printing tutorial


Here is our final presentation! In this video, we introduced how we decide to design this robot, what we did, and what future directions can be. The tasks are divided into two parts for two people: Optimal Design and Smart AI System. Future works can be based on either of these two parts, or a combination of these two. The major tasks for future works include redesigning the chassis to improve space efficiency and strength, programming the robot to avoid obstacles at a slope and the robot can memorize the location of the obstacles, and integrating Arduino, LiDAR and Jetson Nano for the robot (Using two microcontrollers). 


[1]  NAE GRAND CHALLENGES (2019). Engineer the Tools of Scientific Discovery. Grand Challenges – Engineer the Tools of Scientific Discovery.

[2] Swanson, S. (2019). Are astronauts worth tens of billions of dollars in extra costs to go to Mars? THE CONVERSATION.

[3] NASA Science (2021). Why Do We Send Robots To Space?  NASA Science space place.

[4] Anderson, G. T., Tunstel, E. W., & Wilson, E. W. (2007). Robot system to search for signs of life on mars. IEEE Aerospace and Electronic Systems Magazine22(12), 23-30.

[5] Babjak, J., Novák, P., Kot, T., Moczulski, W., Adamczyk, M., & Panfil, W. (2016, May). Control system of a mobile robot for coal mines. In 2016 17th International Carpathian Control Conference (ICCC) (pp. 17-20). IEEE.

[6] Tzafestas, S. G. (2013). Introduction to mobile robot control. Elsevier.