In a highly competitive market, robot designers must deliver a product that provides the highest possible level of speed, accuracy, durability, and other performance parameters at the lowest possible cost. When a 5% speed advantage is often the difference between making and losing a sale, robot designers need to push the limits of the design process. For example, robot designers can use higher torque motors or they can use lighter arms to reduce the time required for the robot to reach its final position. As robot designers push the performance envelope, arms and other components bend to a degree such that this deflection becomes important in calculating the joint motion that be must be undertaken to achieve a given robot position. Higher torque and lighter arms also make the robot more prone to vibration so it becomes more critical than ever before to determine the natural frequency of the robot and ensure that it is far from any operating frequency of the robot. More powerful robots also increase the demands on the gear train of the robot, increasing the importance of effects such as gear rattle and backlash and also making the bearing design more critical. But most robot designers are still using the same design tools they have used for decades.
Trajectory planners that incorporate kinematic models or simple dynamic models are typically used to establish the path followed the by the end effector to reach the desired spatial position while tracing a smooth and continuous motion during the motor acceleration and deceleration stages. But even when encoders with very high levels of accuracy are used, the ability of robots to move to an absolute XYZ position and ABC orientation is limited by effects such as deflection, gear backlash, thermal expansion and manufacturing variation. The link flexibility problem can be solved analytically with equations but this approach requires a high level of mathematical skills and a considerable amount of time. The complexity of the analytical method increases exponentially as the degrees of freedom and geometrical complexity of the robot increase.
Another challenge for today’s robot designers is understanding the value of the forces applied to the various joints of the robot. As lightweighting is increasingly used to improve robot components, these forces need to be accurately estimated at an early stage in the design process in order to size robot components to deliver a competitive product. Joint forces depend to a large degree on the stiffness of the bearings and beams of the robots so traditional design tools are not able to provide accurate predictions. There is also a growing trend towards the development of collaborative robots that can be operated safely in the presence of human operators. A critical factor in the design of these robots is determining the amount of forces that would be applied by the robot to an operator in the event of a collision in order to avoid operator injury.
The design of the robot’s control algorithm also has become much more critical and difficult in today’s more competitive environment. In order to deliver high speeds while maintaining or increasing positional accuracy, the control algorithm often needs to take dynamic effects, such as the flexibility of the robot and bearings and the stiffness of the geartrain, into account. Meeting the delivery schedule often requires that the control algorithm be designed simultaneously with the robot itself, yet current design methods provide little information on the dynamics effects needed to make sound design decisions. One more example of dynamic effects that need to be considered in robot design is the management and guiding of cables. A group of systems integrators recently cited cable management as the number one reason for downtime in robot cells. Traditional design methods provide no way to know the deformed shape of the cables until the prototype testing stage.
Adams enables robot designers to evaluate the transient dynamic behavior of a proposed robot design in much less time, at a lower cost and at an earlier stage in the design process than would be required to obtain the same information from prototype testing. Using Adams, robot designers can increase operating speed while maintaining precision positioning and avoiding vibration, validate and optimize the control algorithm and evaluate the ability of a proposed design to perform a wide range of applications.
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