Planning with SiMBA: Motion Planning under Uncertainty for Temporal Goals using Simplified Belief Guides

@article{Ho2022PlanningWS,
  title={Planning with SiMBA: Motion Planning under Uncertainty for Temporal Goals using Simplified Belief Guides},
  author={Qi Heng Ho and Zachary Sunberg and Morteza Lahijanian},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.10202}
}
This paper presents a new multi-layered algorithm for motion planning under motion and sensing uncertainties for Linear Temporal Logic specifications. We propose a technique to guide a sampling-based search tree in the combined task and belief space using trajectories from a simplified model of the system, to make the problem computationally tractable. Our method eliminates the need to construct fine and accurate finite abstractions. We prove correctness and probabilistic completeness of our… 
View PDF on arXiv

Figures and Tables from this paper

33 References

Rapidly-exploring Random Belief Trees for motion planning under uncertainty

  • A. BryN. Roy
  • Mathematics
    2011 IEEE International Conference on Robotics and Automation
  • 2011
The algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration results in a search tree in belief space that provably converges to the optimal path.

Sampling-based motion planning with temporal goals

This paper presents a geometry-based, multi-layered synergistic approach to solve motion planning problems for mobile robots involving temporal goals, and presents a technique to construct the discrete abstraction using the geometry of the obstacles and the propositions defined over the workspace.

Gaussian Belief Trees for Chance Constrained Asymptotically Optimal Motion Planning

This paper generalizes traditional sampling-based, tree-based motion planning algorithms for deterministic systems and proposes belief-A, a framework that extends any kinodynamical tree- based planner to the belief space for linear (or linearizable) systems.

Asymptotically Optimal Stochastic Motion Planning with Temporal Goals

This work presents a planning framework that allows a robot with stochastic action uncertainty to achieve a high-level task given in the form of a temporal logic formula and shows that high-quality policies to satisfy complex temporal logic specifications can be obtained in seconds, orders of magnitude faster than existing methods.

Online Mapping and Motion Planning Under Uncertainty for Safe Navigation in Unknown Environments

A unified mapping–planning strategy that enables robots to navigate autonomously and safely in harsh environments and results in tighter probability bounds in comparison to other uncertainty-aware planners in the literature.

Iterative temporal motion planning for hybrid systems in partially unknown environments

A multi-layered synergistic framework that can deal with general robot dynamics and combine it with an iterative planning strategy is employed and is successful in generating a trajectory whose satisfaction measure of the specification is optimal.

Belief Space Planning: a Covariance Steering Approach

The CS-BRM algorithm allows the sampling of non-stationary belief nodes, and thus is able to explore the velocity space and find efficient motion plans and the benefits of the proposed approach are demonstrated.

Motion planning with temporal-logic specifications: Progress and challenges

The paper examines robot motion planning with temporal-logic specifications and discusses open challenges and directions for future research to promote a continuing dialog between robotics and AI communities.

Safe Motion Planning for an Uncertain Non-Holonomic System with Temporal Logic Specification

A sampling-based motion planning algorithm for systems with complex dynamics and temporal logic specifications allowing to tackle sophisticated missions via construction of backward trees that allows for faster re-planning compared to the state-of-the-art.

Motion Planning With Dynamics by a Synergistic Combination of Layers of Planning

Simulation experiments with dynamical models of ground and flying vehicles demonstrate that the combination of discrete search and motion planning in SyCLoP offers significant advantages, with speedups of up to two orders of magnitude.