Recursive Probabilistic Velocity Obstacles for Reflective Navigation

Kluge, Boris; Prassler, Erwin

doi:10.1007/10991459_8

Cited by 25 publications

(23 citation statements)

References 4 publications

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“…Early models assumed that each person would take full responsibility for avoiding other people. Several variations include the reactive behavior of other models [1,16,11]. One example is reciprocal velocity obstacles (RVO), where the assumption is that all other people will take half the responsibility for avoiding collisions [3,12].…”

Section: Introductionmentioning

confidence: 99%

Fast Simulation of Crowd Collision Avoidance

Charlton

Gonzalez

Maddock

et al. 2019

Advances in Computer Graphics

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0000−0001−8402−6723] , Luis Rene Montana Gonzalez 1[0000−0002−0511−0466] , Steve Maddock 1[0000−0003−3179−0263] , and Paul Richmond 1[0000−0002−4657−5518]Abstract. Real-time large-scale crowd simulations with realistic behavior, are important for many application areas. On CPUs, the ORCA pedestrian steering model is often used for agent-based pedestrian simulations. This paper introduces a technique for running the ORCA pedestrian steering model on the GPU. Performance improvements of up to 30 times greater than a multi-core CPU model are demonstrated. This improvement is achieved through a specialized linear program solver on the GPU and spatial partitioning of information sharing. This allows over 100,000 people to be simulated in real time (60 frames per second).

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Section: Introductionmentioning

confidence: 99%

Fast Simulation of Crowd Collision Avoidance

Charlton

Gonzalez

Maddock

et al. 2019

Advances in Computer Graphics

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“…But it was shown in Trautman and Krause (2010), that the freezing robot problem (FRP) cannot be solved without considering the avoidance possibilities of the objects. In addition, Kluge and Prassler (2006) claim that a robot which is navigating too defensively will surely get stuck in dense pedestrian traffic. Like described by Althoff et al (2010) two kinds of workspace objects are distinguished: -Passive objects: they ignore the robot trajectoryũ.…”

Section: Goal Functionsmentioning

confidence: 97%

Safety assessment of robot trajectories for navigation in uncertain and dynamic environments

et al. 2011

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This paper presents a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects. For safety assessment, the overall collision probability is used to rank candidate trajectories by considering the probability of colliding with known objects as well as the estimated collision probability beyond the planning horizon. In addition, we introduce a safety assessment cost metric, the probabilistic collision cost, which considers the relative speeds and masses of multiple moving objects in which the robot may possibly collide with. The collision probabilities with other objects are estimated by probabilistic reasoning about their future motion trajectories as well as the ability of the robot to avoid them. The results are integrated into a navigation framework that generates and selects trajectories that strive to maximize safety while minimizing the time to reach a goal location. An example implementation of the proposed framework is applied to simulation scenarios, that explores some of the inherent computational tradeoffs.

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“…Our methodology of modeling uncertainty in the system is similar to that presented in works like [5], [6], [7], [8], [9], [10], [11], [12], [16], [17], [18]. in the sense that uncertainty at any time instant is represented as a normal random variable with a particular mean vector and covariance matrix.…”

Section: Uncertainty Modelmentioning

confidence: 99%

Chance constraint based multi agent navigation under uncertainty

Gopalakrishnan

Singh²,

Kaushik

et al. 2017

Proceedings of the Advances in Robotics

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Abstract. We present Probabilistic Reciprocal Velocity Obstacle or PRVO as a general algorithm for navigating multiple robots under perception and motion uncertainty. PRVO is defined as the space of velocities that ensures dynamic collision avoidance between a pair of robots with a specified probability. Our approach is based on defining chance constraints over the inequalities defined by the deterministic Reciprocal Velocity Obstacle (RVO). The computational complexity of the proposed probabilistic RVO is comparable to the deterministic counterpart. This is achieved by a series of reformulations where we first substitute the computationally intractable chance constraints with a family of surrogate constraints and then adopt a time scaling based solution methodology to efficiently characterize their solution space. Further, we also show that the solution space of each member of the family of surrogate constraints can be mapped in closed form to the probability with which the original chance constraints are satisfied and thus consequently to probability of collision avoidance. We validate our formulations through numerical simulations where we highlight the importance of incorporating the effect of motion uncertainty and the advantages of PRVO over existing formulations which handles the effect of uncertainty by using conservative bounding volumes.

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Recursive Probabilistic Velocity Obstacles for Reflective Navigation

Cited by 25 publications

References 4 publications

Fast Simulation of Crowd Collision Avoidance

Fast Simulation of Crowd Collision Avoidance

Safety assessment of robot trajectories for navigation in uncertain and dynamic environments

Chance constraint based multi agent navigation under uncertainty

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