Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints

@article{Renganathan2020TowardsIP,
  title={Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints},
  author={Venkatraman Renganathan and Iman Shames and Tyler Holt Summers},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.02928}
}

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References

SHOWING 1-10 OF 17 REFERENCES

Distributionally Robust Sampling-Based Motion Planning Under Uncertainty

  • T. Summers
  • Mathematics
    2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2018
The DR-RRT method generates risk-bounded trajectories and feedback control laws for robots operating in dynamic, cluttered, and uncertain environments, explicitly incorporating localization error, stochastic process disturbances, unpredictable obstacle motion, and unknown obstacle location.

Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees

A novel riskbased objective function, shown to be admissible within RRT*, allows the user to trade-off between minimizing path duration and risk-averse behavior, which enables the modeling of soft risk constraints simultaneously with hard probabilistic feasibility bounds.

A probabilistic approach to optimal robust path planning with obstacles

The key idea behind the approach is that the probabilistic obstacle avoidance problem can be expressed as a disjunctive linear program using linear chance constraints, such that planning with uncertainty requires minimal additional computation.

Chance-Constrained Optimal Path Planning With Obstacles

A chance-constrained approach that plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold, and introduces a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality.

Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty

A novel real-time planning algorithm, chance constrained rapidly-exploring random trees (CC-RRT), which uses chance constraints to guarantee probabilistic feasibility for linear systems subject to process noise and/or uncertain, possibly dynamic obstacles.

Incremental sampling-based algorithm for risk-aware planning under motion uncertainty

  • Wei LiuM. Ang
  • Computer Science, Engineering
    2014 IEEE International Conference on Robotics and Automation (ICRA)
  • 2014
This paper considers the problem of motion planning for linear systems subject to Gaussian motion noise and proposes a risk-aware planning algorithm: CC-RRT*-D. The proposed CC-RRT*-D employs the

How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics

The goal of this paper is to advocate axioms that risk metrics in robotics applications should satisfy in order to be employed as rational assessments of risk, and discuss general representation theorems that precisely characterize the class of metrics that satisfy theseAxioms and provide instantiations that can be used in applications.

FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements

FIRM is introduced as an abstract framework, a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods and the so-called SLQG-FIRM, a concrete instantiation of FIRM that focuses on kinematic systems and then extends to dynamical systems by sampling in the equilibrium space.

Integrated Perception and Control at High Speed: Evaluating Collision Avoidance Maneuvers Without Maps

The probabilistic formulation provides a natural way to integrate reactive obstacle avoidance with arbitrary navigation objectives and is presented as a method for robust high-speed quadrotor flight through unknown cluttered environments using integrated perception and control.

Perception-aware Path Planning

It is argued that motion planning for vision-controlled robots should be perception aware in that the robot should also favor texture-rich areas to minimize the localization uncertainty during a goal-reaching task.