Robot path planning using Field Programmable Analog Arrays

Koziol, Scott; Hasler, P.; Stilman, Mike

doi:10.1109/icra.2012.6225303

Cited by 22 publications

(13 citation statements)

References 24 publications

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“…where L 1 and L 2 mean the length of the second and third links, respectively. Moreover, obstacles are represented by (10), and numerical values of A and B positions are A(15…”

Section: -Dof Robotic Arm With Three Obstaclesmentioning

confidence: 99%

“…Grid-based approaches overlay a grid on configuration space, and they assume that there is a grid point for each location in the configuration space, for example, reconfigurable mesh (RMESH), 3 grid roadmap (GRM), 4 and resistive grid (RG). [5][6][7][8][9][10][11][12] Geometrical-based algorithms generate position and orientation trajectories for geometrically complex spatial paths using mathematical expressions, for example, ordinary differential equation-based algorithm, 13 parametric parabolas, 14 homotopy continuation method, 15,16 angle preprocessing, 17 and visibility binary tree algorithm. 18 Finally, sampling-based algorithms represent configuration space with roadmaps of sampled configurations, for example, rapidly exploring random tree (RRT) algorithm, [19][20][21] evolutionary optimization algorithm, 22 co-evolutionary improved genetic algorithm (CIGA), 23 neural computation, 24 and multi-objective particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Collision-Free Path Planning Modeling and Simulation Methodology for Robotical Arms Using Resistive Grids

Hernández-Mejía

Vázquez-Leal²,

Torres-Muñoz³

2019

Robotica

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SUMMARYPath planning represents planning collision-free strategies to move from starting point to ending point. These strategies can be carried out for known and unknown environments. Recently, a novel and reduced CPU-time modeling and simulation methodology for path planning in known environment based on resistive grids (RGs) has been introduced. In this work, a novel modified version of Resistive Grid Path Planning Methodology (RGPPM) methodology is presented with the purpose of exploring collision-free path planning for robotic arms. This extension of the methodology allows to numerically relate positions in the RG with angular values of the robotic systems. In addition, it is possible to include obstacles in the configuration space, and therefore collision detection can be established for RGs. Finally, the variation of links for robotic arms and obstacles for configuration space is explored by simulating different scenarios.

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“…where L 1 and L 2 mean the length of the second and third links, respectively. Moreover, obstacles are represented by (10), and numerical values of A and B positions are A(15…”

Section: -Dof Robotic Arm With Three Obstaclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Collision-Free Path Planning Modeling and Simulation Methodology for Robotical Arms Using Resistive Grids

Hernández-Mejía

Vázquez-Leal²,

Torres-Muñoz³

2019

Robotica

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“…In order to design a seemingly random motion that will also guarantee efficient grid exploration, many researchers have considered chaotic path planning [2,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. In this approach, a chaotic dynamical system is utilized to generate a chaotic motion trajectory, that is then performed by the robot to achieve efficient coverage.…”

Section: Introductionmentioning

confidence: 99%

An Inverse Pheromone Approach in a Chaotic Mobile Robot’s Path Planning Based on a Modified Logistic Map

et al. 2019

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One major topic in the research of path planning of autonomous mobile robots is the fast and efficient coverage of a given terrain. For this purpose, an efficient method for covering a given workspace is proposed, based on chaotic path planning. The method is based on a chaotic pseudo random bit generator that is generated using a modified logistic map, which is used to generate a chaotic motion pattern. This is then combined with an inverse pheromone approach in order to reduce the number of revisits in each cell. The simulated robot under study has the capability to move in four or eight directions. From extensive simulations performed in Matlab, it is derived that motion in eight directions gives superior results. Especially, with the inclusion of pheromone, the coverage percentage can significantly be increased, leading to better performance.

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“…Other works in VLSI path planning include [5] [6] [7] [8], and [9). A spike based neuron inspired wavefront planner and simulation results was presented in [10).…”

mentioning

confidence: 99%

Path planning using a neuron array integrated circuit

Koziol

Brink

Hasler

2013

2013 IEEE Global Conference on Signal and Information Processing

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Neuromorphic Engineering is an interdisciplinary field which combines concepts from fields such as biology, neuroscience, computer science and engineering. The goal of this field is to design systems that are based on the principles of biological nervous systems. This paper presents hardware results for path planning using a neuron array integrated circuit. The algorithm is explained and experimental results are presented showing 100% correct performance for a large number of maze environment scenarios. Although there is still more work to be completed before this is a fielded system, this work represents an new application of a neuromorphic Integrated Circuit and the results demonstrate definite potential.Index Terms-aVLSI, path planning, robotics, neuromorphic 1. ANA LOG VLSI NEURON EMBEDDED SYSTEM FOR PA TH PLANNING Path planning is a critical task for robots, autonomous vehicles, animated characters, etc. Fig 1 is a cartoon showing the ultimate goal of the problem being addressed in this paper, namely how to use a neuron array integrated circuit (IC) [2] to plan a path for a Micro Aerial Vehicle (MAV) (or similar power constrained ground or sea robot) through an environment in an effort to conserve its limited battery resources. The IC used for the results in this work, Fig 2a, uses biologically realistic transistor based models which operate how neurons do, however not necessarily how the brain does path planning. The IC has 100 Neurons and 30,000 synapses. It was constructed in a 0.35 micron process and the die size is 5x5 mm. Floating gate transistors are used as the synapse elements. The floating gate synapse transistors are used to create the programmable routing. A synaptic weight is stored by an adjustable charge on the gate of the floating gate synapse transistor. Multiple synapses are connected to a dendrite by adjusting the weights on the synapse transistors which are connected in parallel to the dendrite wires. Unused synapses are not connected internally to the path planning circuit. The floating gates for these transistors are not set to conduct. The system uses Address Event Representation (AER) to record spike data from the neurons. Neuron Elements include: Soma, dendrite, synapses, and axons. Path planning can be summarized with the following three tasks given that states, actions, an initial state, and a goal state are provided. The robot should:1) Find a sequence of actions that take the robot from its Initial state to its Goal state Robot Start a) Discretized locations in maze A -. C -. I I T T b) Grid representation of maze c) Neuron representation of maze Fig. 1. The goal of this research: To use a reconfigurable neuron Array IC to plan a path for small robot from point A to point Z through an environment. a) Maze environment which is discretized into grid points b) Simplified grid representation of the maze in (a). Hash marks denote in-active edges (i.e. walls)c) Bidirectionally connected neurons implementing the edges between nodes.2) Find actions that take the robot from any state to t...

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Robot path planning using Field Programmable Analog Arrays

Cited by 22 publications

References 24 publications

A Novel Collision-Free Path Planning Modeling and Simulation Methodology for Robotical Arms Using Resistive Grids

A Novel Collision-Free Path Planning Modeling and Simulation Methodology for Robotical Arms Using Resistive Grids

An Inverse Pheromone Approach in a Chaotic Mobile Robot’s Path Planning Based on a Modified Logistic Map

Path planning using a neuron array integrated circuit

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