Industrial robots are increasingly deployed in applications requiring an end effector tool to closely track a specified path, such as in spraying and welding. Performance and productivity present possibly conflicting objectives: tracking accuracy, path speed, and motion uniformity. Industrial robots are programmed through motion primitives consisting of waypoints connected by pre-defined motion segments, with specified parameters such as path speed and blending zone. The actual executed robot motion depends on the robot joint servo controller and joint motion constraints (e.g., velocity, acceleration limits) which are largely unknown to the users. Programming a robot to achieve the desired performance today is time-consuming and mostly manual, requiring tuning a large number of coupled parameters in the motion primitives. The performance also depends on the choice of additional param-eters: possible redundant degrees of freedom, location of the target curve, and the robot configuration. This paper presents a systematic approach to optimize robot motion parameters. The approach first selects the static parameters, then chooses the motion primitives, and finally iteratively updates the waypoints to minimize the tracking error. The ultimate performance objective is to maximize the path speed subject to the tracking accuracy and speed uniformity constraints over the entire path. We have demonstrated the effectiveness of this approach both in simulation and on physical systems for ABB and FANUC robots applied to two challenging example curves. Comparing with the baseline using the current industry practice, the optimized performance shows over 100% performance improvement.

This paper describes the algorithm development and experimental results of a vision-guided multiprobe microassembly system. The key focus is to develop the capabilities required for the construction of 3-D structures using only planar microfabricated parts. Instead of using grippers, multiple sharp-tipped probes are coordinated to manipulate parts by using vision feedback. This novel probe-based approach offers both stable part grasping and dexterous part manipulation. The light weight of the part and relatively slow motion means that only kinematics-based control is required. However, probe motions need to be carefully coordinated to ensure reliable and repeatable part grasping and manipulation. Machine vision with multiple cameras is used to guide the motion. No contact force sensor is used; instead, vision sensing of the probe bending is used for the grasp force control. By combining preplanned manipulation sequences and vision-based manipulation, repeatable spatial (in contrast with planar) manipulation and insertion of a submillimeter part have been demonstrated with an experimental testbed consisting of two actuated probes, a passive probe, an actuated die stage, and two cameras for vision feedback.

In our recent work, we have demonstrated effectiveness of the concept of multi-probe microassembly for manipulating and inserting microscale, sub-millimeter, parts to create three-dimensional microstructures. However, the approach has been based on trial-and-error manual teaching of grasp points to ensure a stable grasp during motion. As a result, the part orientation is restricted (nearly aligned with the world reference frame and lying flat) to ensure successful grasping and manipulation. In this paper, we developed a kinematics based hybrid motion and force control based only on vision feedback. We first conduct a systematic analysis of the bending of the probes while they are in contact with the part, to estimate the grasp force based on the vision feedback of the probe configuration. A Jacobian based controller is then used for position manipulation while maintaining the desired squeeze force. Experimental results with two probes and two camera are included to demonstrate the effectiveness of the controller to move the part to specified position and orientation while maintaining sufficient squeeze force to prevent part slippage.

This paper describes the operator assisted automated assembly of a 3-legged spatial platform by using a vision guided multi-probe assembly process. This is the first step towards the ultimate goal of building a microscale active spatial platform. Two issues are highlighted in this paper: contact management and vision feedback. Using multiple probes for part grasping and manipulation has the advantage of robustness and versatility as compared to micro-grippers. However, the contacts between the probes and the part need to be carefully managed to ensure a grasp that is stable for part pick-up and yet manipulable to allow part motion in a controlled fashion. By using vision guidance, the probes can be coordinated to grasp the parts and lift them off the die securely and reliably. We show that the contacts act as point contacts with friction, so when a part is pressed against a stationary probe, the part rotates about the axis between the contacts, changing its orientation so it may be inserted into a slot in the substrate. We have demonstrated that the three legs can be assembled in a fully automated fashion via multiple-camera vision feedback. The platform is at present assembled via tele-operation. The assembled microstructure measures 450 µm×600 µm. We are now working on the full automation of the assembly onto a substrate populated with MEMS actuators.