Robotics

Robots operating in contact-rich environments, such as grinding, polishing, surface finishing, cleaning, and assembly, require advanced force control strategies to interact safely and accurately with their surroundings. Traditional motion control approaches are often insufficient for these applications, as variations in environmental properties can lead to force tracking errors, excessive contact forces, or damage to the robot and workpiece.

This project investigates an adaptive variable impedance control framework that enables a robot to dynamically regulate interaction forces under uncertain environmental conditions. Unlike conventional impedance controllers that rely on fixed stiffness and damping parameters, the proposed approach adapts the impedance parameters based on the interaction state, allowing the robot to maintain stable force tracking despite changes in surface properties.

The controller models the robot–environment interaction using a virtual mass–spring–damper system and adjusts the desired motion response according to the external contact forces. This enables accurate force regulation without requiring complete knowledge of the environment dynamics.

Singularity Avoidance for Redundant Robots

For redundant robotic systems, additional degrees of freedom provide flexibility to avoid singular configurations and improve motion capability. This project integrates a singularity avoidance strategy into the inverse kinematics framework, allowing the robot to maintain high manipulability while executing complex contact trajectories.

The inverse kinematics solution is obtained using the Damped Least Squares method, combined with a manipulability-based optimisation strategy to prevent singular configurations during task execution.

Key Results

• Developed an adaptive variable impedance controller for force tracking under uncertain environmental conditions.
• Reduced force-error overshoot during environmental stiffness transitions by up to 56.13% compared with constant impedance control.
• Improved robot manipulability by 5.95% through an integrated singularity avoidance strategy.
• Demonstrated stable multi-axis force tracking for redundant robotic manipulators performing complex contact trajectories.

Citation

Muhammad Bilal, M. Nadeem Akram, Mohsin Rizwan. "Adaptive Variable Impedance Control for Multi-Axis Force Tracking in Uncertain Environment Stiffness with Redundancy Exploitation." Journal of Control Engineering and Applied Informatics, vol. 24, no. 2, pp. 35–45, 2022.

Collaborative robots (or cobots) are widely used in research laboratories for a range of manipulation tasks. Most cobots, such as Swayer and Franka Emika 7-DoF robots, have more degrees of freedom (DoF) than are required to perform a given task. This redundancy means that the same end-effector pose can often be achieved using multiple, and in many cases infinitely many, joint configurations. In such cases, the robot could use its redundant DoF to satisfy additional objectives while still completing the main task. The primary objective is typically to achieve the desired end-effector motion or pose, while the remaining DoF could be exploited for secondary objectives such as avoiding singularities, maintaining a safe distance from obstacles, avoiding joint limits, or reducing unnecessary joint motions. Redundancy resolution is therefore concerned with determining an appropriate joint configuration or motion from the available solutions while satisfying the primary task and optimizing one or more secondary objectives. In other words, it provides a way to exploit the robot’s additional DoF to select a solution that is not only task-feasible but also desirable according to desired criteria. This simulation-based study investigates how redundancy resolution can be used to avoid singular configurations and dynamic obstacles while maintaining the primary objective of task completion. A singular configuration occurs when the robot loses one or more independent directions of end-effector motion, limiting its ability to move freely in certain directions. Simulation Environment The algorithm is implemented on a 3R planar robot operating in a 2D workspace. The robot has three joint DoFs, while the end-effector position requires only two DoFs (x and y). Therefore, the robot has one redundant DoF, allowing multiple joint configurations to achieve the same end-effector position. Singularity Avoidance To evaluate singularity avoidance, the robot is commanded to follow a straight-line trajectory in the workspace. Two conditions are considered. In the first condition, the robot only optimizes the primary objective of following the desired trajectory. In the second condition, the robot follows the same trajectory while also optimising a secondary objective for singularity avoidance. The difference between the two conditions can be visualised using the manipulability ellipsoid. The ellipsoid represents the robot's ability to generate end-effector motion in different directions. A larger ellipsoid volume indicates higher overall manipulability and therefore greater distance from a singular configuration. In this experiment, the ellipsoid is larger when the singularity-avoidance objective is included, demonstrating how the redundant DoF can be used to improve the robot's manipulability while maintaining the desired end-effector trajectory. The results are visualised using a manipulability ellipsoid. As the robot approaches a singular configuration, the ellipsoid area shrinks. A larger ellipsoid indicates higher manipulability, meaning the robot is far from a singularity. At a fully extended position, a hard singularity cannot be avoided, and the ellipsoid collapses into a straight line. Dynamic Obstacle Avoidance A dynamic obstacle avoidance algorithm is implemented to investigate how the redundant DoF can be used to avoid moving obstacles while maintaining the primary task. The robot is required to follow a predefined trajectory while a dynamic obstacle moves through its workspace. Two objectives are considered: the primary objective of following the desired trajectory and the secondary objective of avoiding collisions with the moving obstacle. When the obstacle approaches the robot, the redundancy resolution algorithm adjusts the robot's joint configuration to move the robot away from the obstacle while continuing to follow the desired end-effector trajectory. The simulation demonstrates that the robot can adapt its joint configuration in real time to avoid the moving obstacle while maintaining the primary task. This illustrates how the redundant DoF can be exploited to satisfy a secondary safety objective without significantly compromising the desired task trajectory.