Provably safe motion of mobile robots in human environments

Liu, Stefan B.; Roehm, Hendrik; Heinzemann, Christian; Lütkebohle, Ingo; Oehlerking, Jens; Althoff, Matthias

doi:10.1109/iros.2017.8202313

Cited by 53 publications

(36 citation statements)

References 28 publications

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“…Creating predictions that satisfy Assumption 13 and Definition 12 is the topic of ongoing research [4], [6]- [8], but is not the focus of this work. Instead, given such a prediction, we show how to design guaranteed not-at-fault trajectories.…”

Section: Definition 12mentioning

confidence: 99%

Towards Provably Not-At-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments

Vaskov¹,

Kousik²,

Larson³

et al. 2019

Robotics: Science and Systems XV

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As autonomous robots increasingly become part of daily life, they will often encounter dynamic environments while only having limited information about their surroundings. Unfortunately, due to the possible presence of malicious dynamic actors, it is infeasible to develop an algorithm that can guarantee collision-free operation. Instead, one can attempt to design a control technique that guarantees the robot is not-at-fault in any collision. In the literature, making such guarantees in real time has been restricted to static environments or specific dynamic models. To ensure not-at-fault behavior, a robot must first correctly sense and predict the world around it within some sufficiently large sensor horizon (the prediction problem), then correctly control relative to the predictions (the control problem). This paper addresses the control problem by proposing Reachability-based Trajectory Design for Dynamic environments (RTD-D), which guarantees that a robot with an arbitrary nonlinear dynamic model correctly responds to predictions in arbitrary dynamic environments. RTD-D first computes a Forward Reachable Set (FRS) offline of the robot tracking parameterized desired trajectories that include fail-safe maneuvers. Then, for online receding-horizon planning, the method provides a way to discretize predictions of an arbitrary dynamic environment to enable real-time collision checking. The FRS is used to map these discretized predictions to trajectories that the robot can track while provably not-at-fault. One such trajectory is chosen at each iteration, or the robot executes the fail-safe maneuver from its previous trajectory which is guaranteed to be not at fault. RTD-D is shown to produce not-at-fault behavior over thousands of simulations and several real-world hardware demonstrations on two robots: a Segway, and a small electric vehicle.

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Section: Definition 12mentioning

confidence: 99%

Towards Provably Not-At-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments

Vaskov¹,

Kousik²,

Larson³

et al. 2019

Robotics: Science and Systems XV

View full text Add to dashboard Cite

show abstract

“…3) Transition model: The transition model of the ego vehicle depends on the current action and state of the ego vehicle and consists of a point mass model. For the transition of the pedestrian, we use a simple reachability model [13], which depends on the current pedestrian state and calculates further positions for the pedestrian based on a set of possible acceleration values. It is assumes that the pedestrian can take any of those acceleration with uniform probability.…”

Section: B Scenario Modelingmentioning

confidence: 99%

Pedestrian Collision Avoidance System for Scenarios with Occlusions

Schraner

Bouton

Kochenderfer

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. In this work, we formulate the problem as a partially observable Markov decision process (POMDP), to derive a policy robust to uncertainty in the pedestrian location. We investigate how to integrate such a policy with an AEB system that operates only when a collision is unavoidable. In addition, we propose a rigorous evaluation methodology on a set of well defined scenarios. We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.

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“…A large body of literature has been devoted to formal analysis of reachability for finite [13], continuous [6,11], and hybrid systems [4,28,5,10] with applications ranging from ensuring the safety of mobile robots in human environments to flight maneuver verification [27,7,22,32,36,33,24,17,21,28]. Accurate reachability analysis necessitates the computation of the reachable set for every single state in an uncountable state space, which is computationally intractable in practice [34].…”

Section: Related Workmentioning

confidence: 99%

Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees

Liebenwein¹,

Baykal²,

Gilitschenski³

et al. 2018

Robotics: Science and Systems XIV

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Abstract-The successful deployment of many autonomous systems in part hinges on providing rigorous guarantees on their performance and safety through a formal verification method, such as reachability analysis. In this work, we present a simple-to-implement, sampling-based algorithm for reachability analysis that is provably optimal up to any desired approximation accuracy. Our method achieves computational efficiency by judiciously sampling a finite subset of the state space and generating an approximate reachable set by conducting reachability analysis on this finite set of states. We prove that the reachable set generated by our algorithm approximates the ground-truth reachable set for any user-specified approximation accuracy. As a corollary to our main method, we introduce an asymptoticallyoptimal, anytime algorithm for reachability analysis. We present simulation results that reaffirm the theoretical properties of our algorithm and demonstrate its effectiveness in real-world inspired scenarios.I. INTRODUCTION Autonomous and highly automated systems inherently depend on effectively incorporating rigorous guarantees on the performance and safety through formal verification and validation methods. For instance, in order to ensure collision-free paths, advanced driver-assistance systems need to be capable of anticipating all potential actions of the driver without overly conservative assumptions. This requires performing on-line reachability analysis, i.e., computation of states that these vehicles can reach within a given time interval. It can also serve as a supervisory mechanism for any motion planner that incorporates deep learning. Going beyond the realm of autonomous driving, reachability analysis has shown promise as a tool for formal verification of a wide variety of systems. Applications of reachability analysis include safety, correctness, and controller synthesis problems involving intricate specifications or robotic systems such as autonomous aircraft and cars, medical robots, and personal-assistance robots.Typically the state of a system is not fully observable, e.g., a car might not have precise knowledge about its position. Thus, conducting accurate reachability analysis by definition requires reasoning about all possible trajectories from every possible state. Reasoning about all possible behaviors of a system renders reachability analysis computationally intractable in practice [3]. This computational challenge is further compounded by the generally large size and high complexity of the system in consideration, and the practical need to obtain verification results in a reasonably short time (i.e., seconds or minutes, not days) for the sake of, for example,

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Provably safe motion of mobile robots in human environments

Cited by 53 publications

References 28 publications

Towards Provably Not-At-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments

Towards Provably Not-At-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments

Pedestrian Collision Avoidance System for Scenarios with Occlusions

Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees

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