Provably Safe Navigation for Mobile Robots with Limited Field-of-Views in Unknown Dynamic Environments

Technical session - Conf. website: http://icra2012.orgInternational audienceThis paper addresses the problem of navigating a mobile robot with a limited field-of-view in a unknown dynamic environment. In such a situation, absolute motion safety, i.e. such that no collision will ever take place whatever happens, is impossible to guarantee. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest. Passive motion safety is tackled using a variant of the Inevitable Collision State (ICS) concept called Braking ICS, i.e. states such that, whatever the future braking trajectory of the robot, a collision occurs before it is at rest. Passive motion safety is readily obtained by avoiding Braking ICS at all times. Building upon an existing Braking ICS-Checker, i.e. an algorithm that checks if a given state is a Braking ICS or not, this paper presents a reactive collision avoidance scheme called PASSAVOID. The main contribution of this paper is the formal proof of PASSAVOID's passive motion safety. Experiments in simulation demonstrates how PASSAVOID operates

Passively Safe Partial Motion Planning for Mobile Robots with Limited Field-of-Views in Unknown Dynamic Environments

International audienceThis paper addresses the problem of planning the motion of a mobile robot with a limited sensory field-of-view in an unknown dynamic environment. In such a situation, the upper-bounded planning time prevents from computing a complete motion to the goal, partial motion planning is in order. Besides the presence of moving obstacles whose future behaviour is unknown precludes \textit{absolute motion safety} (in the sense that no collision will ever take place whatever happens) is impossible to guarantee. The stance taken herein is to settle for a weaker level of motion safety called \textit{passive motion safety}: it guarantees that, if a collision takes place, the robot will be at rest. The primary contribution of this paper is {\passivepmp}, a partial motion planner enforcing passive motion safety. {\passivepmp} periodically computes a passively safe partial trajectory designed to drive the robot towards its goal state. Passive motion safety is handled using a variant of the Inevitable Collision State (ICS) concept called \textit{Braking ICS}, {\ie} states such that, whatever the future braking trajectory of the robot, a collision occurs before it is at rest. Simulation results demonstrate how {\passivepmp} operates and handles limited sensory field-of-views, occlusions and moving obstacles with unknown future behaviour. More importantly, {\passivepmp} is provably passively safe

SAFETY-GUARANTEED TASK PLANNING FOR BIPEDAL NAVIGATION IN PARTIALLY OBSERVABLE ENVIRONMENTS

Bipedal robots are becoming more capable as basic hardware and control challenges are being overcome, however reasoning about safety at the task and motion planning levels has been largely underexplored. I would like to make key steps towards guaranteeing safe locomotion in cluttered environments in the presence of humans or other dynamic obstacles by designing a hierarchical task planning framework that incorporates safety guarantees at each level. This layered planning framework is composed of a coarse high-level symbolic navigation planner and a lower-level local action planner. A belief abstraction at the global navigation planning level enables belief estimation of non-visible dynamic obstacle states and guarantees navigation safety with collision avoidance. Both planning layers employ linear temporal logic for a reactive game synthesis between the robot and its environment while incorporating lower level safe locomotion keyframe policies into formal task specification design. The high-level symbolic navigation planner has been extended to leverage the capabilities of a heterogeneous multi-agent team to resolve environment assumption violations that appear at runtime. Modifications in the navigation planner in conjunction with a coordination layer allow each agent to guarantee immediate safety and eventual task completion in the presence of an assumption violation if another agent exists that can resolve said violation, e.g. a door is closed that another dexterous agent can open. The planning framework leverages the expressive nature and formal guarantees of LTL to generate provably correct controllers for complex robotic systems. The use of belief space planning for dynamic obstacle belief tracking and heterogeneous robot capabilities to assist one another when environment assumptions are violated allows the planning framework to reduce the conservativeness traditionally associated with using formal methods for robot planning.M.S

Will the Driver Seat Ever Be Empty?

Self-driving technologies have matured and improved to the point that, in the past few years, self-driving cars have been able to safely drive an impressive number of kilometers. It should be noted though that, in all cases, the driver seat was never empty: a human driver was behind the wheel, ready to take over whenever the situation dictated it. This is an interesting paradox since the point of a self-driving car is to remove the most unreliable part of the car, namely the human driver. So, the question naturally arises: will the driver seat ever be empty? Besides legal liability issues, the answer to that question may lie in our ability to improve the self-driving technologies to the point that the human driver can safely be removed from the driving loop altogether. However, things are not that simple. Motion safety, i.e. the ability to avoid collisions, is the critical aspect concerning self-driving cars and autonomous vehicles in general. Before letting self-driving cars transport people around (and move among them) in a truly autonomous way, it is crucial to assess their ability to avoid collision, and to seek to characterize the levels of motion safety that can be achieved and the conditions under which they can be guaranteed. All these issues are explored in this article.Les technologies de conduite automatique ont mûries et se sont améliorées au point que, au cours des dernières années, les voitures automatiques ont été en mesure de conduire en toute sécurité un nombre impressionnant de kilomètres. Il convient cependant de noter que, dans tous les cas, le siège du conducteur n'était jamais vide : un conducteur humain était au volant, prêt à prendre le relais dès que la situation dictée. C'est un paradoxe intéressant car le point d'une voiture automatique est d'enlever la partie la plus sensible de la voiture, à savoir le conducteur humain. Ainsi, la question se pose naturellement: le siège du conducteur sera t'il vide un jour? Outre les questions de responsabilité juridique, la réponse à cette question réside peut-être dans notre capacité à améliorer les technologies de la conduite automatique, au point que le pilote humain peut en toute sécurité être retiré de la boucle de conduite. Toutefois, les choses ne sont pas aussi simple que cela. La sécurité de mouvement, i.e. la capacité à éviter les collisions, est l'aspect critique à l'égard de voitures automatiques et les véhicules autonomes en général. Avant de laisser les voitures automatiques transporter des personnes (et se déplacer parmi eux) d'une manière réellement autonome, il est crucial d'évaluer leur capacité à éviter la collision, et de chercher à caractériser les niveaux de sécurité de mouvement qui peuvent être atteints et les conditions dans lesquelles elles peuvent être garanties. Toutes ces questions sont examinées dans cet article

Real-time Safe Path Planning for Robot Navigation in Unknown Dynamic Environments

International audienceThis paper solves a motion planning problem from a motion safety perspective, where a variant of the classical Rapidly exploring Random Tree (RRT) approach [1] called p-safe RRT is proposed. The exploration of the search space is similar to RRT, however, the highlight of p-safe RRT is the integration of passive motion safety. The basic principle of this safety level is to guarantee that the system can brake down and stop before collision. P-safe RRT extends a tree through the state time space, where tree's nodes and primitives are checked for passive motion safety. The computed trajectory is passively safe and drives the robot from its initial state to the goal state. The developed algorithms have been tested in simulation scenarios; featuring both fixed and moving objects with unknown trajectories for a car-like robot with a limited field of view

A representation method based on the probability of collision for safe robot navigation in domestic environments

This paper introduces a three-dimensional volumetric representation for safe navigation. It is based on the OctoMap representation framework that probabilistically fuses sensor measurements to represent the occupancy probability of volumes. To achieve safe navigation in a domestic environment this representation is extended with a model of the occupancy probability if no sensor measurements are received, and a proactive approach to deal with unpredictably moving obstacles that can arise from behind occlusions by always expecting obstacles to appear on the robot’s path. By combining the occupancy probability of volumes with the position uncertainty of the robot, a probability of collision is obtained. It is shown that by relating this probability to a safe velocity limit a robot in a real domestic environment can move close to a certain maximum velocity but decides to attain a slower safe velocity limit when it must, analogous to slowing down in traffic when approaching an occluded intersection.</p