Linear Planning Logic: An Efficient Language and Theorem Prover for Robotic Task Planning

Download

index.pdf

Date

2014-06-07

Author

Kortik, Sitar
Saranlı, Uluç

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

168
views

229
downloads

In this paper, we introduce a novel logic language and theorem prover for robotic task planning. Our language, which we call Linear Planning Logic (LPL), is a fragment of linear logic whose resource-conscious semantics are well suited for reasoning with dynamic state, while its structure admits efficient theorem provers for automatic plan construction. LPL can be considered as an extension of Linear Hereditary Harrop Formulas (LHHF), whose careful design allows the minimization of nondeterminism in proof search, providing a sufficient basis for the design of linear logic programming languages such as Lolli. Our new language extends on the expressivity of LHHF, while keeping the resulting nondeterminism in proof search to a minimum for efficiency. This paper introduces the LPL language, presents the main ideas behind our theorem prover on a smaller fragment of this language and finally provides an experimental illustration of its operation on the problem of task planning for the hexapod robot RHex.

Subject Keywords

Planning, Encoding, Rough surfaces, Surface roughness, Legged locomotion, Cognition

URI

https://hdl.handle.net/11511/38701

DOI

https://doi.org/10.1109/icra.2014.6907404

Conference Name

IEEE International Conference on Robotics and Automation (ICRA)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Domain-Structured Chaos in a Hopfield Neural Network Akhmet, Marat (World Scientific Pub Co Pte Lt, 2019-12-30) In this paper, we provide a new method for constructing chaotic Hopfield neural networks. Our approach is based on structuring the domain to form a special set through the discrete evolution of the network state variables. In the chaotic regime, the formed set is invariant under the system governing the dynamics of the neural network. The approach can be viewed as an extension of the unimodality technique for one-dimensional map, thereby generating chaos from higher-dimensional systems. We show that the dis...
MultiPoseNet: Fast Multi-Person Pose Estimation Using Pose Residual Network KOCABAŞ, Muhammed; KARAGÖZ, Salih; Akbaş, Emre (2018-09-14) In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method. MultiPoseNet can jointly handle person detection, person segmentation and pose estimation problems. The novel assignment method is implemented by the Pose Residual Network (PRN) which receives keypoint and person detections, and produces accurate poses by assigning keypoints to person instances. On the COCO keypoints dataset, our pose estimation...
Feedback motion planning of a novel fully actuated unmanned surface vehicle via sequential composition of random elliptical funnels Özdemir, Oğuz; Ankaralı, Mustafa Mert; Department of Electrical and Electronics Engineering (2022-12-27) This thesis proposes and analyzes a motion planning and control schema for unmanned surface vehicles that fuses sampling-based approaches’ probabilistic completeness with closed-loop approaches’ robustness. The Proposed schema is based on the sequential composition of elliptical funnels, and it consists of two stages: tree generation and motion control. For validation of the approach, we carried out experiments using both simulation and physical setup besides the mathematical analysis. In order to have a co...
Frequency-domain subspace identification of linear time-periodic (LTP) systems Uyanık, İsmail; Saranlı, Uluç; Ankaralı, Mustafa Mert; Cowan, Noah J.; Morgül, Ömer (Institute of Electrical and Electronics Engineers (IEEE), 2019-06) This paper proposes a new methodology for subspace-based state-space identification for linear time-periodic (LTP) systems. Since LTP systems can be lifted to equivalent linear time-invariant (LTI) systems, we first lift input-output data from an unknown LTP system as if they were collected from an equivalent LTI system. Then, we use frequency-domain subspace identification methods to find the LTI system estimate. Subsequently. we propose a novel method to obtain a time-periodic realization for the estimate...
Numerical Design of Testing Functions for the Magnetic-Field Integral Equation Karaosmanoglu, Bariscan; Ergül, Özgür Salih (2016-04-15) We present a novel numerical approach to design testing functions for the magnetic-field integral equation (MFIE). Enforcing the compatibility of matrix equations derived from MFIE and the electric-field integral equation (EFIE) for the same problem, testing weights for MFIE are determined on given templates of testing functions. The resulting MFIE systems produce more accurate results that the conventional MFIE implementations, without increasing the number of iterations and processing time. The design pro...

Citation Formats

S. Kortik and U. Saranlı, “Linear Planning Logic: An Efficient Language and Theorem Prover for Robotic Task Planning,” presented at the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, PEOPLES R CHINA, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38701.