Task-Driven Manipulation with Reconfigurable Parallel Robots

ReachBot, a proposed robotic platform, employs extendable booms as limbs for mobility in challenging environments, such as martian caves. When attached to the environment, ReachBot acts as a parallel robot, with reconfiguration driven by the ability to detach and re-place the booms. This ability enables manipulation-focused scientific objectives: for instance, through operating tools, or handling and transporting samples. To achieve these capabilities, we develop a two-part solution, optimizing for robustness against task uncertainty and stochastic failure modes. First, we present a mixed-integer stance planner to determine the positioning of ReachBot's booms to maximize the task wrench space about the nominal point(s). Second, we present a convex tension planner to determine boom tensions for the desired task wrenches, accounting for the probabilistic nature of microspine grasping. We demonstrate improvements in key robustness metrics from the field of dexterous manipulation, and show a large increase in the volume of the manipulation workspace. Finally, we employ Monte-Carlo simulation to validate the robustness of these methods, demonstrating good performance across a range of randomized tasks and environments, and generalization to cable-driven morphologies.

ReachBot is a proposed robotic concept for enhanced mobility in challenging environments, such as martian lava tubes. Using deployable booms as reconfigurable prismatic joints, ReachBot can extend and traverse across large regions, accessing hard-to-reach areas of scientific interest.

However, to truly perform interesting science in these environments, and to set itself apart from more traditional rovers, ReachBot must also have manipulation capabilities.

Consider a sample extraction task: NASA may be interested in taking a geological sample from a hard-to-reach area of the Moon or Mars. This will require a robot like ReachBot to:

• Align a camera to analyze a region while maintaining a steady view
• Apply a force or torque to extract the sample, through grasping or drilling
• Transport the sample to a retrieval location

For each of these to occur, ReachBot must carefully plan its motion and the forces/torques it applies, ensuring that it maintains stability and safely completes the task.

To do this, we create a two-part system which optimizes the ReachBot configuration and the tensions in each boom. We use concepts from dexterous manipulation (task ellipsoids and wrench spaces) to form our optimization problem, which optimizes for the worst-case disturbance wrench (a force and torque) in the task.

This allows ReachBot to safely execute tasks such as the following, while maintaining stability:

Please refer to the paper (linked at the top of the page) for more details