quadbot or soft robot for indoor exploration, climbing stairs -- 2

I am looking for a skilled freelancer to develop a quadbot or soft robot for indoor exploration and specifically designed to climb stairs. Here are the project requirements:

Size and Weight: I am not sure about specific size and weight requirements, so I would appreciate recommendations from the freelancer.

Terrain: The robot will be primarily used for indoor exploration, so it needs to be able to navigate through indoor environments effectively.

Climbing Capabilities: The robot should have the ability to climb stairs effortlessly.

Ideal Skills and Experience:

1. Robotics: Experience in designing and developing robots, especially those with climbing capabilities, would be highly preferred.

2. Mechanical Engineering: Knowledge of mechanical design and engineering principles to ensure the robot's stability and climbing capabilities.

3. Programming: Proficiency in programming languages such as C++ or Python to program the robot's movements and navigation.

4. Sensors and Actuators: Familiarity with sensors and actuators to enable the robot to detect and respond to its environment.

5. Problem-Solving: Strong problem-solving skills to overcome challenges in developing a robot with climbing capabilities and indoor exploration.

If you have the expertise and skills required for this project, please provide your recommendations for the robot's size and weight, along with your relevant experience in the field of robotics

My scenario;
Frame 1: (Interior - Staircase): The robot arrives at the foot of the stairs, a potential challenge to its SLAM (Simultaneous Localization and Mapping) algorithm. Its LiDAR sensor emits rapid-fire laser pulses, generating a dense point cloud of the environment in real time. It's this active sensing method that forms the basis of its perception, navigation, and mapping capabilities.

Frame 2: (Interior - Robot POV): The robot's on-board computer performs real-time calculations to develop a 3D reconstruction of the environment from the point cloud data. It uses a variant of the ICP (Iterative Closest Point) algorithm to merge consecutive point clouds, creating a consistent global model of the environment. The staircase emerges in exquisite detail in the robot's perception.

Frame 3: (Interior - Staircase): Using the high-resolution environmental map, the robot calculates an optimal path up the stairs using the D* Lite path planning algorithm. This algorithm is an incremental heuristic search which is highly efficient for dynamic environments, enabling the robot to re-plan the path in real time if obstacles are detected.

Frame 4: (Interior - Close Up): The robot's articulated legs adjust their position, with joint angles precisely calculated using inverse kinematics, allowing the robot to maintain a stable center of gravity while tackling the stairs. The motor torque and power requirements are calculated using the Newton-Euler method and executed through the robot's servo control system.

Frame 5: (Interior - Staircase): As the robot begins to ascend, each foot placement is carefully chosen to ensure stability and forward progress. If a foot slips or doesn't land as expected, the robot's proprioceptive sensors alert the control system which quickly recalculates joint torques and foot placements to recover balance.

Frame 6: (Interior - Staircase): To confirm its progress, the robot uses Monte Carlo Localization (or particle filter localization) to estimate its position within the global map. The LiDAR data is compared with the 3D map to validate and update the robot's pose (position and orientation) in real time.

Frame 7: (Interior - Top of the Stairs): Upon reaching the top of the staircase, the robot uses its LiDAR and cameras to update the global 3D map with this new vantage point, ready to plan and execute the next leg of its mission.

Frame 8: (Interior): As the robot moves deeper into the building, it comes across a closed door. It carefully opens the door using a mechanical arm or manipulator and proceeds.

Frame 9: (Interior): Inside the room, the robot identifies a uniformed DPRK soldier. The soldier is unaware of the robot's presence. The robot captures images and transmits this information back to the control team.

Frame 10: (Interior): Continuing its mission, the robot navigates up a staircase. It moves quietly, avoiding detection while constantly updating its 3D map.

Frame 11: (Interior): On the upper floor, the robot encounters another soldier. It quickly identifies the soldier and hides before it can be detected, again transmitting this information to the control team.

Frame 12: (Interior): The robot successfully explores the rest of the building, avoiding soldiers and obstacles, collecting more data.

Frame 13: (Exterior): Having completed its mission, the robot exits the building and retreats to a safe location for retrieval, leaving the soldiers none the wiser.

Frame 14: (Command Center): Back at the command center, the team reviews the data collected by the robot. The 3D map of the building's interior is complete, and the locations and identities of the soldiers within have been confirmed."

The use of SLAM, ICP, D* Lite, A* algorithms, inverse kinematics, Newton-Euler method, proprioception, and Monte Carlo Localization demonstrate the integration of advanced robotics concepts required for this complex task. This not only illustrates the intricate dance of software and hardware in robotics, but also the importance of real-time processing and control systems in navigating complex environments.