Gustavo Alfonso Garcia Ricardez

Asymmetric Velocity Moderation for human-safe robot control

With the increasing physical proximity of human–robot interaction, ensuring that robots do not harm surrounding humans has become crucial. Therefore, we propose asymmetric velocity moderation as a low-level controller for robotic systems to enforce human-safe motions. While our method prioritizes human safety, it also maintains the robot’s efficiency. Our proposed method restricts the robot’s speed according to (1) the displacement vector between human and robot, and (2) the robot’s velocity vector. That is to say, both the distance and the relative direction of movement are taken into account to restrict the robot’s motion. Through real-robot and simulation experiments using simplified HRI scenarios and dangerous situations, we demonstrate that our method is able to maintain the robot’s efficiency without undermining human safety. Graphical Abstract

Asymmetric velocity moderation: A reactive strategy for human safety

As Human-Robot Interaction becomes closer, it is necessary to seek human safety so that the robot does not harm the human when the human ventures into the robots workspace. Therefore, we propose a reactive strategy for human safety called Asymmetric Velocity Moderation. Our proposed method restricts the velocity of the end-effector according to the distance between human and robot. Moreover, our method considers the displacement vector which is the vector formed by the closest points between human and robot. Therefore, we do not consider only the distance but also the direction. The reason to consider the direction is that even though the velocity directed towards the human should be firmly restricted if the distance is short, we can relax the restriction of the velocity directed away from the human even if the distance is short. Thus, by introducing the angle between the displacement vector and the end-effector velocity vector we can improve the trade-off between safety and efficiency. We carried out experiments with a human-size humanoid robot and a human subject standing next to each other, and made them perform independent tasks. Through these experiments, we verified that our method not only provides human safety but also copes with the trade-off between human safety and the efficiency of the robot when performing a task.

Withdrawal strategy for human safety based on a virtual force model

The Human-Robot Interaction gets increasingly closer. In consequence, human safety has become a key issue for the success of the symbiosis between humans and robots. When the minimum distance between a human and a robot is too short, it can be naturally considered that the probability of a collision increases. Therefore, we consider that the robot should increase the distance to the human when the human is getting closer. We propose Withdrawal strategy as a method that aims to increase the distance by moving the end-effector not only away from the human but also to a parking position that can be previously assessed to be safer. To withdraw the end-effector, we use a virtual force model consisting of two virtual forces: a repelling force exerted by the human and an attractive force exerted by the parking position. We carry out experiments using a human-sized humanoid robot and five human subjects, and report the task completion time to evaluate the efficiency of the robot when performing a simple task.

Alignment of Occupancy Grid and Floor Maps Using Graph Matching

Semantic information about the environment lets users operate a robot using natural language. This is a comfortable way to make a robot do tasks in daily-life environments. Because a floor map in a building includes semantic information, if the robot can relate its own map to the floor map, the robot can access this information. The robot obtains the map of the unknown place by using simultaneous localization and mapping (SLAM). In this paper, we propose a method using graph matching to align a floor map with an occupancy grid map generated by SLAM. Our experimental results verified that this method can perform a gross alignment of the maps in an actual situation.

Accelerating whole-body motion generation using regression of the torso posture of a humanoid robot

In everyday environments such as kitchens, humanoid robots require to have a large workspace. For example, a robot needs to grasp a dish in lower shelves while hunkering down. Furthermore, because the environment is dynamic, solving inverse kinematics for the whole body in real time is necessary. We propose to solve inverse kinematics in real time by splitting it into simpler problems. Given the target configurations of both hands as input, we calculate the orientation of the torso using a regressor and calculate the final position of the Center of Mass (CoM) from the reachability of the arms. We obtain the trajectory of the torso orientation and the CoM using interpolation. In each control step, we first compute the joint angles of the lower body from the CoM position, feet position, and torso orientation. Next, we calculate the joint angles of both arms. In the experiments, we apply the proposed method to the humanoid robot HRP-4 for the task of reaching low-height positions while hunkering down. The proposed inverse kinematics solver is ten times faster than the numerical solution.

Interaction Force Estimation for Quantitative Comfort Evaluation of an Eating Assistive Device

Robots» usage in the fields of human support and healthcare is wide-spreading. Robotic devices to assist humans in the self-feeding task have been developed to help patients with limited mobility in the upper limbs but the acceptance of these robots has been limited. In this work, we investigate how to quantitatively evaluate the comfort of an eating assistive device by estimating the interaction forces between the human and the robot when eating. We experimentally verify our concept with a commercially-available eating assistive device and a human subject. The evaluation results demonstrate the feasibility of our approach.

Quantification of Elegant Motions for Receptionist Android Robot

To improve the general image of robots, in this study we describe a method of achieving “elegant motions based on women’s sense” in an android robot. There have been many books published in Japan containing advice for women on how to have elegant manners. Our approach was to quantify the elegant motions that are qualitatively expressed in these etiquette books, using an android robot. In this research, we focused on arm- and face-based motions, such as giving directions, with an emphasis on “reception” tasks. We programmed the robot to perform desirable motions, such as “show the palm to a guest and do not raise the hand higher than the shoulder,” which are commonly expressed in the manners books. For each implemented motions, many patterns could be generated by changing certain parameters, such as the movement speed, the angle of the arm and the hand, and the distance and angle to the indicated location. We verified these motions using a subjective evaluation and discussed the elegant and quantified motions based on the result.

Comparison of Human Safety Metrics based on Safety, Efficiency and Comfort Criteria

Coexistence of humans and robots requires a harmless, efficient and smooth interaction. Numerous efforts have been done to produce human-safe robot behaviors but comparing them (e.g., which one is safer or more efficient) remains a challenge. This is because they have parametric and approach differences when tackling the human safety problems. In this paper, we propose a method to compare human-safe robot behaviors by following three comparison criteria: safety, efficiency, and comfort. We perform simulation experiments making a human and a humanoid robot interact in a shared workspace to compare three human-safe robot behaviors as proof-of-concept. The results summarized in a radar graph show the trade-off between human safety, robots efficiency and the comfort of the interaction.

Human Safety Index Based on Impact Severity and Human Behavior Estimation

With the increasing physical proximity of Human–Robot Interaction (HRI), ensuring that robots do not harm surrounding humans has become crucial. We propose to quantitatively evaluate human safety by modeling the human behavior so that it maximizes the potential injuries in a given situation. The potential injuries are rated using the impact severity of a collision. Therefore, we estimate the human motion that maximizes the impact severity, which is calculated considering a collision between the estimated state of the human and a future state of the robot given a control input. Through simulation experiments using two test cases and three HRI scenarios, we demonstrate that our method keeps human safety while achieving a competitive performance.

Human Safety and Efficiency of a Robot Controlled by Asymmetric Velocity Moderation

Maintaining human safety during HRI is key in the integration of the humanoids in our daily lives. With this in mind, we previously proposed Asymmetric Velocity Moderation (AVM) as a way of restricting the robot speed when interacting with humans. With AVM, the robot reduces its speed according to distance and the direction of the motion. In this paper, we propose a new way of calculating the speed restriction which solves a problem of previous proposals where human safety was sacrificed due to unexpected lesser restriction. We focus on a detailed investigation of how AVM treats situations where a humanoid could endanger a human and test it using different calculation methods of the speed restriction. Finally, we evaluate the efficiency of the humanoid HRP-4 in terms of task completion time by performing simulation experiments in simple HRI scenarios.