What are the most useful robotic control systems and algorithms for Mechanical Engineering?

1 Feedback Control

Feedback control is a basic and widely used technique for robotic control, where the output of a system is measured and compared to a desired reference, and the error is used to adjust the input accordingly. Feedback control can be applied to various aspects of robot motion, such as position, velocity, force, torque, and impedance. Feedback control can also be implemented in different ways, such as proportional-integral-derivative (PID) control, linear-quadratic-regulator (LQR) control, and adaptive control. Feedback control can help you achieve stability, accuracy, and robustness in your robotic system, but it also requires careful tuning, modeling, and sensing.

2 Feedforward Control

Feedforward control is a complementary technique to feedback control, where the input of a system is determined by the desired output and the known dynamics of the system, without relying on measurements or error correction. Feedforward control can be used to improve the performance of feedback control, by compensating for the effects of disturbances, nonlinearities, and delays. Feedforward control can also be used to achieve fast and precise tracking of complex trajectories, such as in robot manipulators, mobile robots, and aerial robots. Feedforward control can help you reduce the sensitivity, error, and bandwidth requirements of your robotic system, but it also requires accurate knowledge of the system dynamics and the environment.

3 Model Predictive Control

Model predictive control (MPC) is an advanced technique for robotic control, where the input of a system is optimized over a finite horizon, based on a model of the system and the environment, and subject to various constraints. MPC can be used to handle complex and nonlinear systems, such as robot manipulators with flexible joints, mobile robots with nonholonomic constraints, and aerial robots with aerodynamic effects. MPC can also be used to incorporate various objectives and criteria, such as energy efficiency, collision avoidance, and obstacle negotiation. MPC can help you achieve optimal and feasible performance in your robotic system, but it also requires high computational power and reliable models and predictions.

4 Reinforcement Learning

Reinforcement learning (RL) is a machine learning technique for robotic control, where the input of a system is learned from trial and error, based on a reward function that evaluates the outcome of the actions. RL can be used to handle systems and environments that are unknown, uncertain, or changing, such as robot navigation in unstructured terrains, robot manipulation in cluttered scenes, and robot cooperation in multi-agent scenarios. RL can also be used to discover novel and creative behaviors, such as robot locomotion in challenging conditions, robot grasping of diverse objects, and robot adaptation to faults and failures. RL can help you achieve adaptive and intelligent performance in your robotic system, but it also requires careful design of the reward function, exploration strategy, and learning algorithm.

5 Motion Planning

Motion planning is a fundamental problem for robotic control, where the input of a system is determined by finding a feasible and optimal path from an initial state to a goal state, while avoiding obstacles and satisfying constraints. Motion planning can be applied to various types of robotic systems, such as robot manipulators, mobile robots, aerial robots, and humanoid robots. Motion planning can also be solved by various methods and algorithms, such as graph-based methods, sampling-based methods, optimization-based methods, and learning-based methods. Motion planning can help you achieve efficient and safe navigation in your robotic system, but it also requires efficient representation, computation, and execution of the paths.

6 Sensor Fusion

Sensor fusion is a crucial technique for robotic control, where the output of a system is estimated by combining the information from multiple sensors, such as cameras, lidars, radars, inertial measurement units, and encoders. Sensor fusion can be used to improve the accuracy, reliability, and robustness of the state estimation, such as the pose, velocity, orientation, and environment of the robot. Sensor fusion can also be used to enhance the perception, cognition, and decision making of the robot, such as the detection, recognition, and tracking of objects, features, and events. Sensor fusion can help you achieve comprehensive and consistent awareness in your robotic system, but it also requires effective integration, calibration, and synchronization of the sensors.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?