This Zurich startup built a four-armed robot for space stations. Each astronaut hour it saves is worth $140,000.

Introduction

In the realm of space exploration, automation and robotics play crucial roles in maintaining and operating space stations. Orbit Robotics' Helios robot represents a significant advancement in this field, featuring a unique four-armed design that allows it to navigate and work in microgravity environments. This tutorial will guide you through creating a simulation environment for a four-armed robotic system using Python and PyBullet, which is a physics engine that allows for realistic simulation of robotics and physics-based interactions.

By the end of this tutorial, you'll have a working simulation of a four-armed robot that can navigate and manipulate objects in a simulated microgravity environment, similar to the concepts behind Helios.

Prerequisites

• Basic understanding of Python programming
• Python 3.6 or higher installed
• PyBullet installed (can be installed via pip)
• Basic knowledge of robotics concepts and physics simulation

Step-by-Step Instructions

1. Install Required Dependencies

First, we need to install the required Python libraries. PyBullet is the primary library for physics simulation in this tutorial.

pip install pybullet

This command installs PyBullet, which provides a physics engine that allows for realistic simulation of robotics and physics-based interactions. We'll use it to simulate the microgravity environment and robot movements.

2. Initialize the Simulation Environment

We start by creating a basic simulation environment with a space station and a four-armed robot. This involves setting up the physics world, adding the space station structure, and initializing our robot.

import pybullet as p
import pybullet_data
import time

# Connect to the physics engine
p.connect(p.GUI)
p.setAdditionalSearchPath(pybullet_data.getDataPath())

# Set the gravity to simulate microgravity (very low gravity)
p.setGravity(0, 0, -0.01)

# Load the space station
station = p.loadURDF("station.urdf", basePosition=[0, 0, 0])

# Load the four-armed robot
robot = p.loadURDF("robot.urdf", basePosition=[0, 0, 1])

Here, we connect to PyBullet's physics engine and set the gravity to simulate microgravity. The low gravity value (0, 0, -0.01) mimics the environment in space where objects float. We then load the space station and the robot using URDF files, which define the physical structure of the objects.

3. Define the Robot's Arms and Joints

Next, we need to define the robot's four arms and their joints. This involves identifying each joint and setting up the control parameters for each arm.

# Get the number of joints in the robot
numJoints = p.getNumJoints(robot)

# Define the arm joints
armJoints = []
for i in range(numJoints):
    jointInfo = p.getJointInfo(robot, i)
    if "arm" in jointInfo[1].decode('utf-8').lower():
        armJoints.append(i)

# Print the arm joints for verification
print("Arm joints:", armJoints)

This code snippet identifies all the joints in the robot and filters out those related to the arms. By knowing the joint indices, we can control each arm individually, which is essential for simulating the robot's movement in microgravity.

4. Implement Arm Movement Control

With the joints identified, we can now implement movement control for each arm. This involves setting up joint control parameters and defining movement patterns for the arms.

# Set up joint control for each arm
for joint in armJoints:
    p.setJointMotorControl2(robot, joint, p.POSITION_CONTROL, targetPosition=0)

# Define a simple movement pattern
def moveArms():
    # Move arms in a coordinated pattern
    p.setJointMotorControl2(robot, armJoints[0], p.POSITION_CONTROL, targetPosition=1.57)
    p.setJointMotorControl2(robot, armJoints[1], p.POSITION_CONTROL, targetPosition=-1.57)
    p.setJointMotorControl2(robot, armJoints[2], p.POSITION_CONTROL, targetPosition=0.78)
    p.setJointMotorControl2(robot, armJoints[3], p.POSITION_CONTROL, targetPosition=-0.78)

# Run the movement
moveArms()

This code sets up position control for each arm joint, allowing us to move the arms in specific patterns. In microgravity, coordinated movement is essential for the robot to navigate and manipulate objects effectively. The movement pattern simulates how the robot might move its arms to maintain balance and perform tasks.

5. Simulate Object Manipulation

Now, we'll simulate how the robot might interact with objects in the space station. This involves loading objects and using the robot's arms to manipulate them.

# Load a tool or object to manipulate
object = p.loadURDF("tool.urdf", basePosition=[0.5, 0, 1])

# Define a function to grasp and move an object
def graspAndMove():
    # Move the robot to the object
    p.setJointMotorControl2(robot, armJoints[0], p.POSITION_CONTROL, targetPosition=0.5)
    p.setJointMotorControl2(robot, armJoints[1], p.POSITION_CONTROL, targetPosition=-0.5)
    
    # Simulate grasping the object
    p.createConstraint(robot, armJoints[0], object, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], [0, 0, 0])
    
    # Move the object to a new location
    p.setJointMotorControl2(robot, armJoints[2], p.POSITION_CONTROL, targetPosition=1.0)
    p.setJointMotorControl2(robot, armJoints[3], p.POSITION_CONTROL, targetPosition=-1.0)

graspAndMove()

This simulation shows how the robot's arms might grasp and move an object. The constraint function simulates the gripping action, and the movement of the arms shows how the robot might reposition the object. This is a simplified version of how the robot would interact with tools and components in a real space station.

6. Run the Simulation Loop

Finally, we'll run a simulation loop to continuously update the robot's state and observe its behavior in the microgravity environment.

# Run the simulation loop
for i in range(1000):
    p.stepSimulation()
    time.sleep(1./240.)
    
    # Update the robot's arm positions periodically
    if i % 100 == 0:
        moveArms()
        graspAndMove()

The simulation loop is crucial for observing how the robot behaves over time. It updates the physics simulation at a consistent rate (240 frames per second) and periodically updates the robot's arm positions to simulate continuous operation. This is how the robot would operate in a real space station, constantly moving and performing tasks.

Summary

In this tutorial, we've created a simulation environment for a four-armed robot similar to Orbit Robotics' Helios. We've covered setting up the physics environment with microgravity simulation, defining robot arms and joints, implementing arm movement control, simulating object manipulation, and running a continuous simulation loop. This simulation provides a foundation for understanding how such robots might operate in real space environments, where traditional leg-based locomotion is ineffective.

While this is a simplified simulation, it demonstrates the core concepts behind four-armed robotics in microgravity environments. The principles learned here can be extended to more complex scenarios, including real-time control systems, path planning, and advanced manipulation tasks that would be essential for a robot like Helios to function effectively in a real space station.