Abstract
In an increasing demand for human-robot collaboration systems, the need for safe robots is crucial. This paper presents a proactive strategy to enable an awareness of the current risk for the robot. The awareness is based upon a map of historically occupied space by the operator. The map is built based on a risk evaluation of each pose presented by the operator. The risk evaluation results in a risk eld that can be used to evaluate the risk of a collaborative task. Based on this risk eld, a control algorithm that constantly reduces the current risk within its task constraints was developed. Kinematic redundancy was exploited for simultaneous task performance within task constraints, and risk minimization. Sphere-based geometric models were used both for the human and robot. The strategy was tested in simulation, and implemented and experimentally tested on a NACHI MR20 7-axes industrial robot.
Highlights
With the more open innovation model seen in the later years (Chesbrough, 2003), Small and Medium Enterprises (SMEs) have a growing importance in the industry
A collision with a humans head must be avoided. While it is moving fast, it would be less likely that the robot would be able to do a successful evasive maneuver, should the human head be detected in using an obstacle avoidance system
In this paper we have presented proactive safety strategy for human robot collaboration
Summary
With the more open innovation model seen in the later years (Chesbrough, 2003), Small and Medium Enterprises (SMEs) have a growing importance in the industry. Separation monitoring is one of these performance control methods This is a system which at all times ensures that the robot manipulator is at a certain distance from the human operator to avoid injuries. The system uses the danger index in a real-time trajectory generator to re-plan its path if a danger threshold is exceeded These systems are all reactive and will manipulate the robot’s path strictly to avoid an imminent collision. If the risk is too high to accept, the robot has the chance to re-plan its path, or change its current pose Based on this it can be more ”cautious” if it is forced to operate in a highly risky area, e.g. move slower, make sound- or light signals or give vibrotactile feedback to the operator (Thomessen and Niitsuma, 2013). The results illustrate the robots ability to avoid high risk areas in the workspace
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