Human-Robot Collaboration: Safety by Design

High payload industrial robots, unlike collaborative robots are not designed to work together with humans. Collaboration can only happen in situations, where the human and robot is separated with a distance, which allows safety sensors to stop the robot system in any point if the human is in too close proximity of the robot. Safety sensors cannot decide over risks, consequences, neither any counter measures to prevent undesired outcome (e.g. collision between human and robot). Safety sensors are only reacting on proximity and can only give severity signal to the robotic system (e.g. no human, slow speed, full stop). This paper presents a new way to address safety sensors: voxel based, dynamic, collision state-space monitoring for human-robot collaboration with high payload robots. The general architecture and some initial test are presented, along with introduction of the problem statement.acceptedVersio

Hybrid approaches for mobile robot navigation

The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point

Predictive robot programming

One of the main barriers to automating a particular task with a robot is the amount of time needed to program the robot. Decreasing the programming time would facilitate automation in domains previously off limits. In this paper, we present a novel method for leveraging the previous work of a user to decrease future programming time: predictive robot programming. The decrease in programming time is accomplished by predicting waypoints in future robot programs and automatically moving the manipulator end-effector to the predicted position. To this end, we have developed algorithms that construct simple continuous-density hidden Markov models by a statemerging algorithm based on waypoints from prior robot programs. We then use these models to predict the waypoints in future robot programs. While the focus of this paper is the application of predictive robot programming, we also give an overview of the underlying algorithms used and present experimental results.

Predictive Robot Programming: Theoretical and Experimental Analysis

As the capabilities of manipulator robots increase, they are performing more complex tasks. The cumbersome nature of conventional programming methods limits robotic automation due to the lengthy programming time. We present a novel method for reducing the time needed to program a manipulator robot: Predictive Robot Programming (PRP). The PRP system constructs a statistical model of the user by incorporating information from previously completed tasks. Using this model, the PRP system computes predictions about where the user will move the robot. The user can reduce programming time by allowing the PRP system to complete the task automatically. In this paper, we derive a learning algorithm that estimates the structure of continuous-density hidden Markov models from tasks the user has already completed. We analyze the performance of the PRP system on two sets of data. The first set is based on data from complex, real-world robotic tasks. We show that the PRP system is able to compute predictions for about 25% of the waypoints with a median prediction error less than 0.5% of the distance traveled during prediction. We also present laboratory experiments showing that the PRP system results in a significant reduction in programming time, with users completing simple robot-programming tasks over 30% faster when using the PRP system to compute predictions of future positions. </p

Predictive Robot Programming: Theoretical and Experimental Analysis

As the capabilities of manipulator robots increase, they are performing more complex tasks. The cumbersome nature of conventional programming methods limits robotic automation due to the lengthy programming time. We present a novel method for reducing the time needed to program a manipulator robot: Predictive Robot Programming (PRP). The PRP system constructs a statistical model of the user by incorporating information from previously completed tasks. Using this model, the PRP system computes predictions about where the user will move the robot. The user can reduce programming time by allowing the PRP system to complete the task automatically. In this paper, we derive a learning algorithm that estimates the structure of continuous-density hidden Markov models from tasks the user has already completed. We analyze the performance of the PRP system on two sets of data. The first set is based on data from complex, real-world robotic tasks. We show that the PRP system is able to compute predictions for about 25% of the waypoints with a median prediction error less than 0:5% of the distance traveled during prediction. We also present laboratory experiments showing that the PRP system results in a significant reduction in programming time, with users completing simple robot programming tasks over 30% faster when using the PRP system to compute predictions of future positions

Abstract Predictive Robot Programming: Theoretical and Experimental Analysis

As the capabilities of manipulator robots increase, they are performing more complex tasks. The cumbersome nature of conventional programming methods limits robotic automation due to the lengthy programming time. We present a novel method for reducing the time needed to program a manipulator robot: Predictive Robot Programming (PRP). The PRP system constructs a statistical model of the user by incorporating information from previously completed tasks. Using this model, the PRP system computes predictions about where the user will move the robot. The user can reduce programming time by allowing the PRP system to complete the task automatically. In this paper, we derive a learning algorithm that estimates the structure of continuous-density hidden Markov models from tasks the user has already completed. We analyze the performance of the PRP system on two sets of data. The first set is based on data from complex, real-world robotic tasks. We show that the PRP system is able to compute predictions for about 25 % of the waypoints with a median prediction error less than 0.5 % of the distance traveled during prediction. We also present laboratory experiments showing that the PRP system results in a significant reduction in programming time, with users completing simple robotprogramming tasks over 30 % faster when using the PRP system to compute predictions of future positions.

Programming Complex Robot Tasks by Prediction: Experimental Results

One of the main obstacles to automating production is the time needed to program the robot. Decreasing the programming time would increase the appeal of automation in many industries. In this paper we analyze the performance of a Predictive Robot Programming (PRP) system on complex, real-world robotic tasks. The PRP system attempts to decrease programming time by predicting the waypoints of a robot program based on previous examples of user behavior. We show that the PRP system is able to generate a large percentage of useful and highly accurate predictions, resulting in a potentially great amount of time saved