Our project has received a change in use case upon further discussion with faculty and evaluation of parts within our budget. We are switching to delivery from last-mile delivery to delivery of small objects within a building. This removes the need to read and identify signs, and reduces the number of potential obstacles. In addition, the more consistent lighting and surfaces will help our camera and odometry system to function better.
Last week, we brought up the depth-sensing cameras. We were notified that they were mistakenly marked as available on the inventory sheet, so we will not be going farther down that route and will instead be continuing with bounding boxes.
Risk 1: With the RTL pipeline this week a risk that can happen is that right after the last chunk is said to be done on a convolutional layer the next chunk can get processed. This could lead to the Multipliers and adder tree not being done and starting another layer messing up the actual accumulated value.
Risk 2: Proper balance of vehicle with multiple batteries: We identified the need for multiple batteries for the KR260, motor systems and arduino. With limited space on the vehicle chassis, we need to align them so that we do not have an unbalanced vehicle that, while functional, will make accurate movement and turning more difficult to implement.
Part A: Written by Paul
When considering public health and safety in our use case and design, our primary focus is not to be a hazard to people who go through hallways. This is emphasized in two of our primary design requirements of being able to identify and change navigation when a person is within 2 meters under 50ms and to be able to stop within 0.5m. The robot needs to react to and navigate around people quickly enough that the person will not have to alter their path. This is especially important for those with movement impairments or sensory disabilities. For those without them, a robot getting in the way just means stepping out of the way or slightly changing tour path, an inconvenience that rarely would cause a real safety hazard. However, for those with impairments, quickly changing paths or stopping is a much taller order that can risk their safety, and for those with sensory impairments, they may only detect the vehicle as it is crashing into them.
Aside from our design goals, our focus on public health and safety is exemplified in our curation of training data. We are placing special focus on using data that includes those who are holding objects that obscure parts of them and who are in wheelchairs. Focusing on those with impairments and disabilities also helps those who do not have them, as they can temporarily have them. Consider a maintenance worker who is holding a large box in front of them. They are partially vision impaired at that time, and should not have to worry about a small robotic car that is out of their field of vision.
Part B: Written by Sean
Our autonomous indoor delivery robot is designed with careful consideration of social factors, particularly its operation in shared indoor pedestrian spaces where diverse social groups are present. By prioritizing people detection and obstacle avoidance, our system respects the fundamental social principle that indoor corridors, lobbies, and pathways are primarily human spaces. Users including children, elderly individuals, people with disabilities, and those carrying packages or using mobility aids should feel safe and comfortable around our product. Our focus is on maintaining appropriate distance from pedestrians and stopping when people are detected which is critical for settings like university campuses, hospitals, office buildings, and residential complexes. Predictable behavior addresses social concerns about robots operating in confined spaces where surprise encounters could cause discomfort particularly among vulnerable populations like elderly residents or young children. Our design acknowledges the importance of prioritizing human presence over delivery efficiency. Our solution aims to gain community acceptance by operating as a considerate “neighbor” rather than prioritizing speed.
Part C: Written by Justin
When considering economic factors, our product is designed to be a practical and efficient solution for real time video processing on low power hardware. The KR260 based computer vision system delivers fast inference while consuming significantly less power than a traditional GPU and providing far more capability than a typical microcontroller. This balance between performance and energy efficiency reduces operational costs through lower power consumption, longer battery life, and reduced cooling requirements. For delivery robots that must react quickly to obstacles, this efficiency translates directly into both safety improvements and long term cost savings.
From a production and distribution standpoint, the system relies primarily on commercially available, off the shelf components rather than custom manufactured hardware. This reduces upfront development costs, minimizes supply chain risk, and allows for easier scaling. The main bottleneck would likely be shipping and lead times rather than specialized fabrication. Because the design is modular, components can be replaced individually, lowering maintenance costs and extending product lifespan.