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

Tajvar, Pouria; Barbosa, Fetnando S.; Tůmová, Jana

doi:10.1109/case48305.2020.9216891

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Robots must be capable of dealing with such uncertainty at runtime, without impacting too much on their expected productivity. The path planning problem has been extensively discussed in the literature ( Kavraki et al, 1996 ; Mac et al, 2016 ; Costa and Silva, 2019 ; Mannucci et al, 2019 ; Tajvar et al, 2020 ) as one important aspect to be able to guarantee a safe operation of the robot, and avoid collision with humans, robots, or other unexpected objects present in the environment. However, an efficient feasible path may not be easy to find at runtime, e.g., due to physical constraints of the environment, and the robot may need to stop waiting for the path to be cleared or make an extended detour.…”

Section: Introductionmentioning

confidence: 99%

Task Roadmaps: Speeding up Task Replanning

et al. 2022

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Modern industrial robots are increasingly deployed in dynamic environments, where unpredictable events are expected to impact the robot’s operation. Under these conditions, runtime task replanning is required to avoid failures and unnecessary stops, while keeping up productivity. Task replanning is a long-sighted complement to path replanning, which is mostly concerned with avoiding unexpected obstacles that can lead to potentially unsafe situations. This paper focuses on task replanning as a way to dynamically adjust the robot behaviour to the continuously evolving environment in which it is deployed. Analogously to probabilistic roadmaps used in path planning, we propose the concept of Task roadmaps as a method to replan tasks by leveraging an offline generated search space. A graph-based model of the robot application is converted to a task scheduling problem to be solved by a proposed Branch and Bound (B&B) approach and two benchmark approaches: Mixed Integer Linear Programming (MILP) and Planning Domain Definition Language (PDDL). The B&B approach is proposed to compute the task roadmap, which is then reused to replan for unforeseeable events. The optimality and efficiency of this replanning approach are demonstrated in a simulation-based experiment with a mobile manipulator in a kitting application. In this study, the proposed B&B Task Roadmap replanning approach is significantly faster than a MILP solver and a PDDL based planner.

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Section: Introductionmentioning

confidence: 99%

Task Roadmaps: Speeding up Task Replanning

et al. 2022

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“…The same authors then introduce in [7] sampling bias guided by the automaton capturing the LTL, something that [13] also proposes in a similar fashion. Lastly, besides proposing a heuristic to guide the search, [16] integrates feedback control laws to guarantee feasibility of plans by robots with complex, possibly non-holonomic, dynamics. Although these works propose ways of improving the time taken to find a plan, they all rely on having details of the environment a priori.…”

Section: A Related Workmentioning

confidence: 99%

Semantic Abstraction-Guided Motion Planning for scLTL Missions in Unknown Environments

Grover¹,

Barbosa²,

Tůmová³

et al. 2021

Robotics: Science and Systems XVII

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Complex mission specifications can be often specified through temporal logics, such as Linear Temporal Logic and its syntactically co-safe fragment, scLTL. Finding trajectories that satisfy such specifications becomes hard if the robot is to fulfil the mission in an initially unknown environment, where neither locations of regions or objects of interest in the environment nor the obstacle space are known a priori. We propose an algorithm that, while exploring the environment, learns important semantic dependencies in the form of a semantic abstraction, and uses it to bias the growth of an Rapidly-exploring random graph towards faster mission completion. Our approach leads to finding trajectories that are much shorter than those found by the sequential approach, which first explores and then plans. Simulations comparing our solution to the sequential approach, carried out in 100 randomized office-like environments, show more than 50% reduction in the trajectory length.

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“…Principled guidance of a lowlevel motion tree has been shown to improve tractability in the deterministic setting for both single and multi-temporal goals planning, using layers of planning [4], [5], [23], [24] or heuristic guidance [6]. For stochastic systems, [25] proposes a heuristic to guide tree extension, but their framework is only designed for dynamics noise. Extending these techniques to settings with both motion and measurement uncertainty is non-trivial because of the difficulty in constructing a guidance mechanism that captures belief dynamics well.…”

Section: Introductionmentioning

confidence: 99%

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

Ho¹,

Sunberg²,

Lahijanian³

2022

Preprint

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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 algorithm, and illustrate the benefits of our approach on several case studies. Our results show that guidance with a simplified belief space model allows for significant speed-up in planning for complex specifications.

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

Cited by 5 publications

References 21 publications

Task Roadmaps: Speeding up Task Replanning

Task Roadmaps: Speeding up Task Replanning

Semantic Abstraction-Guided Motion Planning for scLTL Missions in Unknown Environments

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

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