Crash test-based assessment of injury risks for adults and children when colliding with personal mobility devices and service robots

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who are inherently smaller and fragile. This study compared the risks faced by different pedestrian categories and determined risks through crash testing involving a service robot hitting an adult and a child dummy. Results of collisions at 3.1 m/s (11.1 km/h/6.9 mph) showed risks of serious head (14%), neck (20%), and chest (50%) injuries in children, and tibia fracture (33%) in adults. Furthermore, secondary impact analysis resulted in both populations at risk of severe head injuries, namely, from falling to the ground. Our data and simulations show mitigation strategies for reducing impact injury risks below 5% by either lowering the differential speed at impact below 1.5 m/s (5.4 km/h/3.3 mph) or through the usage of absorbent materials. The results presented herein may influence the design of controllers, sensing awareness, and assessment methods for robots and small vehicles standardization, as well as, policymaking and regulations for the speed, design, and usage of these devices in populated areas.

Toward Online Probabilistic Path Replanning

Björn Jensen, Roland Philippsen, Roland Siegwart

In this talk we present work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and probabilistic motion modeling are combined with a smooth navigation function to perform on-line path planning and replanning in cluttered dynamic environments such as public exhibitions. Human behavior is unforeseeable in most situations that include human-robot interaction, e.g. service robots or robotic companions. This makes motion prediction problematic (they rarely move e.g. with constant velocity along straight lines), especially in settings which include large numbers of humans. Additionally, the robot is usually required to react swiftly rather than optimally, in other words the time required to calculate the plan becomes part of the optimality criterion. The "Probabilistic Navigation Function" (PNF) is an approach for planning in these cluttered dynamic environments. It relies on probabilistic worst-case computations of the collision risk and weighs regions based on that estimate. The PNF is intended to be used for gradient-descent control of a vehicle, where the gradient indicates the best trade-off between risk and detour. An underlying reactive collision avoidance provides the tight perception-action loop to cope with the remaining collision probability. As this is work in progress, we present the approach and describe finished components and give an outlook on remaining implementation issues. Two algorithmic building blocks have been developed and tested: On-line motion detection from a mobile platform is performed by the SLIP scan alignment method to separate static from dynamic objects (it also helps with pose estimation). The interface between motion detection and path planning is a probabilistic co-occurrence estimation measuring the risk of future collisions given environment constraints and worst-case scenarios, which unifies dynamic and static elements. The risk is translated into traversal costs for an E* path planner, which produces smooth navigation functions that can incorporate new environmental information in near real-time.

2006

Wearable Technologies for Embodied Human-Robot Interaction

Carine Rognon

Robotic teleoperation is fundamental to augment the resilience, precision, and force of robots with the cognition of the operator. However, current interfaces, such as joysticks and remote controllers, are often complicated to handle since they require cognitive effort and learned skills. Wearable interfaces can enable more natural and intuitive interactions with robots, which would make robotic teleoperation accessible to a larger population of users for demanding tasks, such as manipulation or search-and-rescue. The aim of this thesis is to explore solutions to simplify our interactions with robots promoting their access to a broader range of the population. To achieve this, we are presenting a soft upper body exoskeleton, called the FlyJacket, for the bidirectional control of drones. Drones can greatly benefit us as they extend our perception and range of action. The exoskeleton controls a drone by recording torso movement and, through embedded haptic feedback devices, renders either kinesthetic guidance to improve the flight performance or tactile feedback to render the sensation of flying. We developed and tested an interface to control both a simulated and a real drone. The FlyJacket is a soft exoskeleton with arm support conceived to address the challenges of adapting to different morphologies and supporting the user during flight to prevent fatigue. We demonstrated that this novel interface allowed more consistent performance than when performing the same task with a remote controller and users felt more immersed into the flight. Interacting with a robot can be greatly enhanced by having multiple channels of sensorial feedback to increase the awareness of the operator. Information on the state of the drone can be intuitively rendered with haptic feedback. To create this bidirectional interaction with the drone, the two types of haptic feedback - kinesthetic and tactile - have been explored. Kinesthetic feedback was implemented with a cable-driven system to give guidance to the userâs torso position. Performing user studies, we could determine that the embedded guidance improved the flight performance and that a quadratically shaped force feedback curve was the most adequate profile to guide the user. We also established the minimal force difference, defined the perceived magnitude of this system and studied the learning process of users. Tactile feedback was investigated to render the sensation of flying by enhancing flight awareness, realism and immersion. To this end, we developed and embedded a new type of soft actuator that was compliant and lightweight such that it remained wearable and portable. Four devices, placed on the torso, provided feedback by compressing closed air pouches against the skin rendering the sensation of air pressure. A mechanical model and simulation of the pouch device were developed to determine appropriate parameters. We evaluated whether it conveyed useful information to the user and whether it enhanced the experience of flying. We demonstrated that users were able to understand the direction of the cues without prompting, could distinguish the cues quickly, and do so with high accuracy. The device was also used in a simulated flight task and users indicated that it increased the flight realism. We believe that the contributions of this thesis provides insights to the design of intuitive interfaces for human-robot interaction and increases their accessibility to a wider range of the population.

EPFL2019

Physical Safety in Collisions Between Robots and Pedestrians

Aude Billard, David Julian Gonon, Diego Felipe Paez Granados, Pericle Salvini

The widespread deployment of mobile service robots in public environments and the growing use of autonomy as control modality will increase the exposure to hazards for people simply sharing the robot floor. The need to ensure safety becomes an ethical and legal obligation for designers. However, current safety requirements are mostly focused on the end-users of robot services, while pedestrians are often not addressed in safety standard requirements. In this paper, we propose a preliminary method for assessing robot safety with respect to collisions with bystanders. Consisting of three main phases: (1) identification of collision contacts points; (2) estimation of collision injuries; (3) design and control recommendations. We evaluate the robot Qolo - a person carrier robot as an example by simulating collisions with walking pedestrian models, using motion capture and body shape data of a male human adult, and a child. We present results of a simulation comparing multiple post-collision reactions and discuss further work needed for creating a framework on robot safety requirements that address all vulnerable populations.