Workspace analysis is crucial for understanding a robot's capabilities and limitations. It involves visualizing reachable and dexterous workspaces using analytical and numerical methods, and examining how design parameters affect workspace characteristics.

Singularities are critical points where a robot loses degrees of freedom, causing control issues. Understanding different types of singularities and implementing strategies for their management is essential for safe and efficient robot operation.

Workspace Analysis

Workspace visualization methods

• Reachable workspace encompasses all points end-effector can reach regardless of orientation
• Dexterous workspace includes points end-effector reaches with arbitrary orientation (subset of reachable workspace)
• Analytical methods utilize forward kinematics equations, geometric approaches (vector analysis), and algebraic techniques (constraint equations)
• Numerical methods employ Monte Carlo simulation (random joint configurations) and discretization of joint space (systematic sampling)
• Visualization techniques include 2D cross-sections (slices of workspace), 3D volume rendering (voxel-based representation), and contour plots (iso-surfaces of manipulability)

Design parameters vs workspace characteristics

• Link lengths directly impact workspace size and shape (longer links increase reach but may reduce dexterity)
• Joint types and limits affect workspace boundaries (prismatic joints create linear motion, revolute joints create arcs)
• Manipulator configuration influences workspace volume (serial robots: larger workspace, parallel robots: higher precision)
• Base placement and orientation affect workspace symmetry and accessibility (floor-mounted vs ceiling-mounted)
• End-effector design impacts dexterous workspace (tool orientation capabilities, gripper size)

Singularities

Types of manipulator singularities

• Boundary singularities occur at workspace limits (arm fully extended or retracted)
• Interior singularities happen within workspace (e.g., elbow singularity in anthropomorphic arms)
• Algorithmic singularities arise from mathematical formulations (not physical robot configurations)
• Mathematical representation: Jacobian matrix becomes rank-deficient, determinant equals zero
• Impact: loss of manipulability, potential infinite joint velocities, control system instability

Strategies for singularity management

• Singularity avoidance:
  1. Analyze workspace and plan paths to avoid singular regions
  2. Implement joint limit avoidance algorithms
  3. Utilize redundancy resolution techniques for extra degrees of freedom
• Robust control methods: damped least squares (adds stability near singularities), SVD-based methods (decompose Jacobian)
• Trajectory optimization minimizes proximity to singular configurations (cost function penalizes near-singular poses)
• Redundant manipulator strategies: null space motion (self-motion without affecting end-effector), task prioritization
• Real-time handling: monitor condition number of Jacobian, implement adaptive control strategies (adjust gains near singularities)