Information Sharing via Projection Function for Coexistence of Robot and Human

Yujin Wakita, Shigeoki Hirai, Takashi Suehiro, Toshio Hori

Autonomous Robots, N0.10, 2001

Summary

                  The authors introduce the concept of safety based on intelligent augmentation of robotic systems. In previous studies the authors introduced the concept of tele-robotic systems (1992,1995,1996), where a robot is operated from another position with no physical contact and monitored through a television, and intelligent monitoring (1992), a system allowing conveyance of only required information through selection of data. The expansion of this last system has been the snapshot function (1995), where a laser pointer helps in teaching mode to estimate the deviation of the position, while the operator can move the robot, teaching the estimated relative deviation. A further implementation is the here proposed projection function (2001), where a robot and human jointly operate through a Digital Desk, a special environment provided with a projector perpendicular to the working table and a speaker. The aim of this research is to achieve intelligent augmentation in order to prevent and avoid undesirable contact, information sharing is a fundamental aspect in cooperative tasks between a person and a robot (Wakita, 1998). The experiment test a human and robot operating in mainly 5 states (initial, approach, grasp, release and final), the main issue is this kind of problem to be solves are: the person does not know the delivery coordinate, the person must keep holding the object until it is released, the person might be frightened by the robot movement.

The projection function consists of projecting on the table the simulated images of the moving robot, so that the human operator knows in real time the robots trajectory and understand the delivery trajectory. Force sensors in the robot’s fingers are used in order to allow the robot understand when the object has been grasped by the operator. A new teaching method also is introduced: the operator activated the teaching mode by touching the robot’s hand, then, instead of physically moving the manipulator, the projected image of the robot follows the operator’s hand to destination, the advantage is that only the model is required and no robot movement; the robot confirm through the speakers that the teaching trajectory has been saved.

The force sensors are an efficient communication method only during grasping, visual monitoring appears to be necessary for the entire delivery task.

It can be observed that humans in cooperation require visual feedback in order to understand that their motion and activity has been understood, each person expects to be observed during their action. So visual information appears to be extremely important by means of perception and it enhance safety in the system.

The digital desks comes to help once again in monitoring and indicating robots and humans in the system, in fact while operating a symbol (in the experiment it is a white rectangle) is projected on the hand of the operator when the robot has detected an action, in this way the human is aware that the robot knows about its presence.

In order to perform the experiment, a CCD camera was used for detection of human’s hand and robot position, and a video projector (SANYO LP-SG60) mounted on the ceiling in parallel with the camera.

The system as programmed, projects a white rectangle on the human’s hand when the CCD and the computer had performed the detection, while stationary hand is recognized a the delivery position.

Key Concepts

Human-Robot Interaction, Human-Robot Cooperation, Team Working

Key Results

The experiment appears to be useful prompting the importance of communication between robots and humans working together, a communication which need also visual feedback in order to ensure safety. A big part of communication is in fact performed not only by direct communication, but also by indirect feedback, showing that the message has been properly received. Future research may require adding information to the system.

Fuzzy-GA-Based Trajectory Planner for Robot Manipulator Sharing a Common Wokspace

Emmanuel A. Mechan-Cruz, Alan S. Morris

IEEE Transactions on Robotics, VOL. 22, NO.4, August 2006

Summary

                   Artificial Potential Fields (APF) are useful in describing workspace of manipulators for implementation of fuzzy collision-avoidance strategies. APF is capable of collision avoidance and trajectory planning simultaneously, but has a high risk of being stuck in local minima conditions in certain conditions, therefore in the literature, following Khatib studies, many solutions to avoid local minima have been performed. The authors present a novel fuzzy genetic algorithm (GA) approach to make trajectory planning of two manipulators sharing a common workspace. The application of fuzzy logic for information generation to drive a manipulator to a certain goal was first investigated by Althoefer (1996), but the system appear to no be feasible for two manipulators working simultaneously with a one of them in static condition nearby the goal.

·       Model

The fuzzy-GA-based trajectory planner (FuGABTP) is a simple Genetic Algorithm (GA) trajectory planner (GABTP) where individual fuzzy units provide correction to the displacement of each articulation. Manipulators are modeled as obstacles using a APF method, calculated only for the near vicinity (being a local approach).

The outline see initial configurations being input in both manipulators, which in parallel start first the GABTP and then implement the fuzzy corrections, at that point trajectories are updated and goal is verified if check or not, repeating the sequence if goal is not reached.

The GABTP carries an initial estimation of the motion without considering the presence of possible obstacles, in  free workspace the degree of extension of the manipulator is considered in order to minimize unnecessary displacements of the links (refer to Appendix A): f=1/(e^(error/R)), where the error is the distance between goal coordinated and actual coordinate, R is the distance between initial coordinates and actual current coordinates.

Fuzzy correction is achieved by individual Mamdami-type fuzzy units that provide collision avoidance. The obstacle direction can be right is the APF increased or left in the other case. μ_i are the fuzzy sets, used for mapping μ_p(d): D à [0;1]. The function to represent the fuzzy sets is asymmetrical triangular functions since these are easily computable. The correction is given according to the fuzzy rules: 1) obstacle on the left and small  APF then SN correction, 2) obstacle on the left and med APF then MN correction; 3) obstacle on the left and medbig APF then MBN correction; 4) if obstacle is on the left and APF is big then BN correction; 5) if obstacle is on the left and APF is 0 then zero correction; 6) if obstacle is on the right and APF is small then correction is SP; 7) if the obstacle is on the right and APF is small then correction is MP; 8) if the obstacle is right and APF is medbig then correction is MBP; 9) is obstacle is right and APF is bit then correction is BP; 10) if obstacle is right and APF is 0 then correction is 0; 11) if obstacle is left and error is 0 then correction is 0; 12) if obstacle is right and error is 0 then correction is 0. The fuzzy set for output correction showing the each acronym graph is report at page 616.

Key Concepts

Fuzzy-GA-Based Trajectory Planner, Planning, Workspace sharing

Key Results

The simulation has been carried on with a 3 d.o.f. and a 7 d.o.f. manipulator both in static and dynamic case, showing success in reaching the goal. The FuGABTP took generally more segment movement but an overall less time to reach the goals if compared with simple GABTP.

The approach demonstrates to be robust and stable, from the set of outputs coming from the model it is further own easy to computer trajectory, velocity and acceleration through kinematics. The parallel planning avoids the need of any further planning.