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The Future of Robotics

Written by James English

Dealing with Challenges and Tradeoffs in Motion Control

The global motion control industry saw trends similar to the machine vision market with an estimated 8% growth in 2018. Within this broader market stability, robot motion control has continued a focus on innovation to drive growth in new industries and applications. Robot motion control—especially for complex articulated arms—is expected to transcend the overall motion control market in coming years.

Motion control software moves robot arms through the action of rotation and sliding joints, and mobile robots through locomotion and steering. This controlled motion enables robot tasks, which are done with tools (end effectors) on the robot. Task can be manipulative, as when using a gripper, or sensory, as when positioning and capturing data with a camera. These two concepts—movement and tasks—are defining components for using a robot.

As illustrated in Figure 1, the tools/end effectors do the work while the joints are controlled, and the relationship between the two is complex. An equation describing the placement of a probe held by a robot arm can take pages and pages of trigonometric functions. This is the easy direction. Going the other way—calculating the control solution of how to place the joints to achieve a desired tool position—may not even have an equation. It may only be solvable iteratively.

The motion control challenge: The joints are controlled while the tool (or end effector) does the work, and the relationship between the two is complex.

Robots like the one shown in Figure 2 have more actuators than the minimum needed for grasping a screwdriver. This actuator redundancy empowers a robot but complicates motion control. If you consider the human body, our extra joints enable many ways—an infinite number, in fact—to take the leftover sandwich out of the refrigerator, and exploiting the redundancy lets us reach around milk cartons, balance, and move smoothly to reduce joint stress and avoid joint limits. This takes lots of brainpower, however. Robots with redundancy benefit from having the potential to move with the same smooth, efficient control, but that takes lots of computer processing power.

Some robots have more degrees of freedom than the minimum required to do a task. These kinematically redundant robots are powerful but can be hard to control.

When there are multiple ways for a robot to accomplish a task, the chosen way should have special qualities, such as maximizing distance from a collision, improving strength, minimizing time, avoiding workspace limits, reducing power consumption and improving accuracy. The best motion will usually be a combination of these—and other—pure qualities.

Motion control must also incorporate constraints. Robot joints have speed and acceleration limits. Actuators have maximum torque or force. Physical parts of the robot cannot overlap in space, and joint limits cannot be exceeded. These are constraints imposed by the physical reality of the robot and the world. The desired tasks, constraints and optimizations combine to make robot motion control a challenge.

A variety of mathematical techniques have been developed to address the challenge. While special-purpose equations can be used, a common technique applies the so-called manipulator Jacobian matrix. The Jacobian is a mathematical object that describes tool velocity as a function of joint velocity in a simplified way. It sidesteps the complicated direct calculation of positions. It has a simplified form, so it is easier to invert to solve the control problem. The only drawback is that it works with velocities rather than positions. Positioning using the Jacobian requires algorithmic feedback techniques.

Though the Jacobian can almost always be defined, calculated and inverted for control, challenges remain for a complete solution. Jacobian-based control is local in nature, and some problems have to be solved globally. Global control relates to large movements with flexibility in the path so long as the endpoints are correct, while local control relates to precisely defined—and usually small—movements. Many robot tasks are performed using a combination of global and local control, and how the optimizations are selected and implemented depend on qualities of the robot, its task and its environment.

Managing higher time derivatives of joint position is also important. Many robots today generate full paths offline before motion starts. Offline path generation allows the use of the future states of the robot in calculations about earlier states. This helps in limiting the higher derivatives of motion that can cause vibration, such as jerk, the derivative of acceleration. The drawback of this approach, however, is that knowledge is incomplete before motion begins. Once the robot starts on a pre-calculated path, it cannot respond to environmental and user-input changes. Sometimes a tradeoff must be made, using slower and more cautious motion to control higher derivatives in real time.

Playing into this tradeoff is the speed of calculation of the control algorithms. When applied in real time, speed is critical. Exploring multiple alternatives—time step by time step—and choosing the best is a powerful algorithmic approach. Faster implementations allow more alternatives and improved control.

There is a chasm between algorithm existence on paper or in demonstration and its practical use because making an algorithm usable is itself difficult. Implementations need to be robust and even rare problems need to be addressed. The implementation must accommodate inevitable deviations in the robot type, the environment, and tasks. And it must easily integrate with other software.

Addressing these needs today are multiple free-open-source and commercial software packages. One commercial example is Energid Technologies’ Actin, which controls arms in areas such as manufacturing, medical, and energy applications. A prominent example of the use of Actin is in bin picking, where one part at a time must be removed from a random pile of parts. Bin picking requires motion control that is fast and smooth while avoiding collisions with the bin holding the parts and with other parts in the bin. Advanced motion control enables this robotic bin picking to be practical, just one example of the way motion control advancement will help robotics across many industries.


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James English is the president and chief technical officer of Energid Technologies. Specializing in automatic remote robotics, James leads project teams in the development of complex robotic, machine vision and simulation systems. Prior to founding Energid, his background was in software development in the engineering and aerospace industries where he held key R&D positions with Raytheon and MAK Technologies. He has authored many journal and conference papers and multiple patents related to the control and simulation of robotic systems.

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The Future of Robotics

by James English time to read: 4 min