Ariyan M. Kabir

Resolving automated perception system failures in bin-picking tasks using assistance from remote human operators

We present an approach to resolve automated perception failures during bin-picking operations in hybrid assembly cells. Our model exploits complementary strengths of humans and robots. Whereas the robot performs bin-picking and proceeds to the subsequent operation like kitting or assembly, a remotely located human assists the robot in critical situations by resolving any automated perception problems encountered during bin-picking. We present the design details of our overall system comprising an automated part recognition system and a remote user interface that allows effective information exchange between the human and the robot that is geared toward solutions that minimize human operator time in resolving the detected perception failures. We use illustrative real robot experiments to show that human-robot information exchange leads to improved bin-picking performance.

Robotic bimanual cleaning of deformable objects with online learning of part and tool models

In this paper, we present an approach to perform automatic robotic cleaning of deformable objects with unknown stiffness characteristics. A bimanual robot setup is used, where one arm holds the part to be cleaned, while the other holds the cleaning tool. The robot maintains an approximate model of the deformation behavior of each part it interacts with and incrementally improves the model as it performs cleaning attempts, thereby gaining information. Simultaneously, the robot maintains a model of the cleaning tool performance which is independent of the particular part and can be learned over multiple episodes of interaction with different parts. During each attempt, the robot exploits its current knowledge of the part deformation behavior to select an optimal set of grasp locations that minimize the amount of deformation. Results indicate the system is able to incrementally learn the deformation model of parts with approximate linear geometry and that the improving model can be quickly used to select the correct grasp locations and tool parameters for rapid cleaning.

A systematic approach for minimizing physical experiments to identify optimal trajectory parameters for robots

Use of robots is rising in process applications where robots need to interact with parts using tools. Representative examples can be cleaning, polishing, grinding, etc. These tasks can be non-repetitive in nature and the physics-based models of the task performances are unknown for new materials and tools. In order to reduce operation cost and time, the robot needs to identify and optimize the trajectory parameters. The trajectory parameters that influence the performance can be speed, force, torque, stiffness, etc. Building physics-based models may not be feasible for every new task, material, and tool profile as it will require conducting a large number of experiments. We have developed a method that identifies the right set of parameters to optimize the task objective and meet performance constraints. The algorithm makes decisions based on uncertainty in the surrogate model of the task performance. It intelligently samples the parameter space and selects a point for experimentation from the sampled set by determining its probability to be optimum among the set. The iterative process leads to rapid convergence to the optimal point with a small number of experiments. We benchmarked our method against other optimization methods on synthetic problems. The method has been validated by conducting physical experiments on a robotic cleaning problem. The algorithm is general enough to be applied to any optimization problem involving black box constraints.

Automated Planning for Robotic Cleaning Using Multiple Setups and Oscillatory Tool Motions

This paper presents planning algorithms for robotic cleaning of stains on nonplanar surfaces. Access to different portions of the stain may require frequent repositioning and reorienting of the object. Some portions with prominent stain may require multiple passes to remove the stain completely. Two robotic arms have been used in the experiments. The object is immobilized with one arm and the cleaning tool is manipulated with the other. The algorithm generates a sequence of reorientation and repositioning moves required to clean the part after analyzing the stain. The plan is generated by accounting for the kinematic constraints of the robot. Our algorithm uses a depth-first branch-and-bound search to generate setup plans. Cleaning trajectories are generated and optimal cleaning parameters are selected by the algorithm. We have validated our approach through numerical simulations and robotic cleaning experiments with two KUKA robots.Note to Practitioners—We encounter nonrepetitive cleaning tasks everyday in both industrial and household environments. Variations in stain pattern, geometry, and material of the object make it difficult to manually program robots for such tasks. In this paper, we present planning algorithms to automate the cleaning task using robots. The practical impact of our approach is evidenced by the actual robot results involving realistic examples like cleaning of hard paint stains on curved surfaces and rust on metal surfaces. Practitioners from industry can use the methods presented in this paper to develop automated robotic systems for nonrepetitive tasks like cleaning and polishing. Our approach caters to the primary requirements of these applications like multiple setups, multiple passes within each setup, and determination of optimal motion parameters like velocity, force, and oscillation frequency of the cleaning tool.

Planning algorithms for multi-setup multi-pass robotic cleaning with oscillatory moving tools

We present planning algorithms for cleaning stains on a curved object. Removing the stain may require multiple reorientations and repositions of the object and some portions of the stain may require multiple cleaning passes. The experimental setup involves two robot arms. The first arm immobilizes the object. The second arm moves the cleaning tool. The algorithm analyzes the stain and determines the sequence of positions and orientations needed to clean the part based on the kinematic constraints of the robot arm. Our algorithm uses a depth-first branch-and-bound search to generate setup plan solutions. We also compute the cleaning trajectories and select the cleaning parameters to maximize the cleaning performance. The algorithm generates multi-pass trajectories by replanning based on the observed cleaning performance. Numerical simulations and cleaning experiments with two Kuka1 robots are used to validate our approach.