Parallel Analogue Computation for Real-Time Path Planning

Tarassenko, Lionel; Marshall, G.F.; Gómez-Castañeda, F.; Murray, Alan F.

doi:10.1007/978-1-4615-3752-6_9

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…A potential advantage of our method is that it is easily parallelised and could be implemented in analog hardware so that the current flow is computed physically. This aspect of our model relates to previous work on resistive networks [24] for analog parallel computing. An early use of this concept was as acceleration for Laplacian Operator to find edges in an image [49].…”

Section: Using a Resistive Network For Planningmentioning

confidence: 78%

“…An early use of this concept was as acceleration for Laplacian Operator to find edges in an image [49]. Later, it was developed for hardware-accelerated path planning, particularly as an improvement over potential field methods, and applied to scenarios such as mazes [24,[50][51][52][53], grid worlds with obstacles [54,55], city roads [56], and robot arms [57,58]. The platforms for the computation include Field Programmable Analog Arrays [52], Very Large-scale Integration [24,51,59], and circuit simulators such as PSPICE [55].…”

Section: Using a Resistive Network For Planningmentioning

confidence: 99%

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Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Wei,

Guo,

Webb

2024

PLoS Comput Biol

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Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This ‘distal’ or ‘sparse’ reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.

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Section: Using a Resistive Network For Planningmentioning

confidence: 78%

Section: Using a Resistive Network For Planningmentioning

confidence: 99%

Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Wei,

Guo,

Webb

2024

PLoS Comput Biol

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“…Then, the highest intensity current will be observed on a path with lowest resistivity (which corresponds to the shortest path if we assume identical specific resistivity of the liquid throughout the maze). Approximation of the shortest collision-free path using electrical current was first demonstrated in a network of resistors in 1991 [32,33]: a space is represented as a resistor network, obstacles are insulators. Later visualisation of the path-solving with electrical current without physical discretisation of space was demonstrated in [8,9] Fig.…”

Section: Discussionmentioning

confidence: 99%

Liquid metal droplet solves maze

2020

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A room temperature liquid metal features a melting point around room temperature. We use liquid metal gallium due to its non-toxicity. A physical maze is a connected set of Euclidean domains separated by impassable walls. We demonstrate that a maze filled with sodium hydroxide solution is solved by a gallium droplet when direct current is applied between start and destination loci. During the maze solving the droplet stays compact due to its large surface tension, navigates along lines of the highest electrical current density due its high electrical conductivity, and goes around corners of the maze's corridors due to its high conformability. The droplet maze solver has a long life-time due to the negligible vapour tension of liquid gallium and its corrosion resistance and its operation enables computational schemes based on liquid state devices.

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“…For RGBPM, a multiple-query ability implies that the source and ground connection nodes must be changed in each query, while the resistive grid’s arrangement must not have any change. If the original formulation of RGBPM is used, the change in the destination node does not alter the formulation; however, a change in the position of the voltage source alters the entire configuration of the MNA matrix, i.e., the solution of the linear system for each query will be different [ 16 , 23 , 30 , 32 , 36 ]. To solve these issues, three changes are proposed here that improve the performance of the RGBPM and result in a novel multiple-query planner.…”

Section: A Multiple-query Rgbpmmentioning

confidence: 99%

Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

Huerta-Chua

Diaz-Arango

Vázquez-Leal

et al. 2021

Sensors

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The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.

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Parallel Analogue Computation for Real-Time Path Planning

Cited by 5 publications

References 4 publications

Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Liquid metal droplet solves maze

Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

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