We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

/pdf/algorithms-for-collision-free-navigation-of-mobile-robots-in-1s1ksc76if.pdf

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

An approach for formally verifying the safety of automated vehicles is proposed. Due to the uniqueness of each traffic situation, we verify safety online, i.e., during the operation of the vehicle. The verification is performed by predicting the set of all possible occupancies of the automated vehicle and other traffic participants on the road. In order to capture all possible future scenarios, we apply reachability analysis to consider all possible behaviors of mathematical models considering uncertain inputs (e.g., sensor noise, disturbances) and partially unknown initial states. Safety is guaranteed with respect to the modeled uncertainties and behaviors if the occupancy of the automated vehicle does not intersect that of other traffic participants for all times. The applicability of the approach is demonstrated by test drives with an automated vehicle at the Robotics Institute at Carnegie Mellon University.

/pdf/online-verification-of-automated-road-vehicles-using-5702vmzc1h.pdf

Online Verification of Automated Road Vehicles Using Reachability Analysis

Autonomous robotic systems are complex, hybrid, and often safety critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics have received some attention in the literature, but no resource provides a current overview. This article systematically surveys the state of the art in formal specification and verification for autonomous robotics. Specially, it identifies and categorizes the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics.

https://dl.acm.org/doi/pdf/10.1145/3342355

Formal Specification and Verification of Autonomous Robotic Systems: A Survey

Autonomous robotic systems are complex, hybrid, and often safety-critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics has received some attention in the literature, but no resource provides a current overview. This paper systematically surveys the state-of-the-art in formal specification and verification for autonomous robotics. Specially, it identifies and categorises the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics.

Nowadays, robots interact more frequently with a dynamic environment outside limited manufacturing sites and in close proximity with humans. Thus, safety of motion and obstacle avoidance are vital safety features of such robots. We formally study two safety properties of avoiding both stationary and moving obstacles: (i) passive safety, which ensures that no collisions can happen while the robot moves, and (ii) the stronger passive friendly safety in which the robot further maintains sufficient maneuvering distance for obstacles to avoid collision as well. We use hybrid system models and theorem proving techniques that describe and formally verify the robot’s discrete control decisions along with its continuous, physical motion. Moreover, we formally prove that safety can still be guaranteed despite location and actuator uncertainty.

http://www.roboticsproceedings.org/rss09/p14.pdf

On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles

This paper addresses the problem of navigating in a provably safe manner a mobile robot with a limited field-of-view placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. 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.

The primary contribution of this paper is the concept of Braking Inevitable Collision States (ICS), i.e. a version of the ICS corresponding to passive motion safety. Braking ICS are defined as states such that, whatever the future braking trajectory followed by the robot, a collision occurs before it is at rest. Passive motion safety is obtained by avoiding Braking ICS at all times.

It is shown that Braking ICS verify properties that allow the design of an efficient Braking ICS-Checking algorithm, i.e. an algorithm that determines whether a given state is a Braking ICS or not.

To validate the Braking ICS concept and demonstrate its usefulness, the Braking ICS-Checking algorithm is integrated in a reactive navigation scheme called PassAvoid. It is formally established that PassAvoid is provably passively safe in the sense that it is guaranteed that the robot will always stay away from Braking ICS no matter what happens in the environment.

https://hal.inria.fr/hal-00733913/document

Provably safe navigation for mobile robots with limited field-of-views in dynamic environments

This 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.

/pdf/provably-safe-navigation-for-mobile-robots-with-limited-3b4pby6paz.pdf

Provably safe navigation for mobile robots with limited field-of-views in unknown dynamic environments

This 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.

/pdf/passively-safe-partial-motion-planning-for-mobile-robots-1xqynficuf.pdf

Passively safe partial motion planning for mobile robots with limited field-of-views in unknown dynamic environments

This paper addresses the problem of provably safe navigation for a mobile robot with a limited field-of-view placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision is inevitable, the robot will be at rest. The primary contribution of this paper is a relaxation of the Inevitable Collision State (ICS) concept called Braking ICS. A Braking ICS is a state for which, no matter what the future trajectory of the robot is, it is impossible to stop before a collision takes place. Braking ICS are designed with a passive motion safety perspective for robots with a limited field-of-view in unknown dynamic environments. Braking ICS are formally defined and a number of important properties are established. These properties are then used to design a Braking ICS checker, i.e. an algorithm that checks whether a given state is a Braking ICS or not. In a companion paper, it is shown how the Braking ICS checker can be integrated into a reactive navigation scheme whose passive motion safety is provably guaranteed.

/pdf/relaxing-the-inevitable-collision-state-concept-to-address-2yso5ul4k5.pdf

Relaxing the inevitable collision state concept to address provably safe mobile robot navigation with limited field-of-views in unknown dynamic environments

Robots are now among us and even though they compete with human beings in terms of performance and efficiency, they still fail to meet the challenge of performing a task optimally while providing strict motion safety guarantees. It is therefore necessary that the future generation of robots evolves in this direction. Generally, in robotics state-of-the-art approaches, the trajectory optimization and the motion safety issues have been addressed separately. An important contribution of this paper is to propose a motion planning method intended to simultaneously solve these two problems in a formal way. This motion planner is dubbed PassPMP-PSO. It is based on a periodic process that interleaves planning and execution for a regular update of the environment’s information. At each cycle, PassPMP-PSO computes a safe near-optimal partial trajectory using a new tree encoding technique based on particle swarm optimization (PSO). The performances of the proposed approach are firstly highlighted in simulation environments in the presence of moving objects that travel at high speed with arbitrary trajectories, while dealing with sensors field-of-view limits and occlusions. The PassPMP-PSO algorithm is tested for different tree expansions going from 13 to more than 200 nodes. The results show that for a population between 20 and 100 particles, the frequency of obtaining optimal trajectory is 100% with a rapid convergence of the algorithm to this solution. Furthermore, an experiment-based comparison demonstrates the performances of PassPMP-PSO over two other motion planning methods (the PassPMP, a previous variant of PassPMP-PSO, and the input space sampling). Finally, PassPMP-PSO algorithm is assessed through experimental tests performed on a real robotic platform using robot operating system in order to confirm simulation results and to prove its efficiency in real experiments.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/094D5FAB7EAFA67605F6A813F9E13395/S0263574720000806a.pdf/div-class-title-safe-motion-planning-based-on-a-new-encoding-technique-for-tree-expansion-using-particle-swarm-optimization-div.pdf

Sara Bouraine

Papers

Provably safe navigation for mobile robots with limited field-of-views in dynamic environments

Provably safe navigation for mobile robots with limited field-of-views in unknown dynamic environments

Passively safe partial motion planning for mobile robots with limited field-of-views in unknown dynamic environments

Relaxing the inevitable collision state concept to address provably safe mobile robot navigation with limited field-of-views in unknown dynamic environments

Safe Motion Planning Based on a New Encoding Technique for Tree Expansion Using Particle Swarm Optimization