Abstract

In recent times, autonomous robots have become more relevant, aiming not only to be an extension of mobility and human performance but also allowing them to independently solve specific problems such as finding free-collision paths within some defined environments. In order to achieve this, several techniques have been developed, like action-reaction algorithms, sampling-based algorithms, and deterministic algorithms such as the Homotopy Path Planning Method (HPPM). This work presents, for the first time, a complete deterministic collision-free path planning scheme implemented in FPGA, which is mounted on a Scribbler 2 robot from Parallax. Then, an automatic algorithm of the repulsion parameter for the HPPM method is presented, using as a reference the minimum distance between the center of each obstacle with respect to the homotopic ideal path; furthermore, an algorithm is proposed for discriminating dead-end routes and collision risk trajectories, which allows us to obtain a feasible free-collision path that takes into account the robot dimensions. Besides, comparative performance tests have been carried out against other path-finding methods from the low degrees of freedom (low DoF) and sampling-based planners. Our proposal exhibits path calculation times which are 5 to 10 times faster on FPGA implementation, compared to the other methods and 10 to 100 times faster on PC implementation also compared to the rest. Similar results are obtained with regards to memory consumption, namely 20 to 200 times lower on FPGA implementation and 10 to 100 times lower on PC implementation.

Highlights

  • Robotics has improved the quality of human life by simplifying tasks, especially those that require high precision or imply physical risks [1]

  • To show the performance of the proposed methodology (HPPM), implemented in PC and Field Programmable Gate Array (FPGA), it is compared to the sampling-based path planning algorithms Expansive-Configuration Space Trees (EST), Kinodynamic Planning by Interior-Exterior Cell Exploration (KPIECE), Probabilistic RoadMap method (PRM), and Rapidly-exploring Random Tree (RRT); it is compared to the low degrees of freedom (low degrees of freedom (DoF)) planners: Bug algorithm and the APF method

  • To guarantee the results obtained from these tools are as similar as possible, all are bounded to the same conditions: for sampling-based algorithms, the searching method step size is the same as the Homotopy Path Planning Method (HPPM) hyperspheres; in addition, the same grid size is used for the low DoF algorithms

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Summary

Introduction

Robotics has improved the quality of human life by simplifying tasks, especially those that require high precision or imply physical risks [1]. The RRT_Connect [4] has been designed with the goal of solving problems that do not consider differential restrictions It is based on two main ideas: one is to grow trees simultaneously from starting and goal positions and the other is to use heuristics attempting to reach a maximum distance rather than a fixed step size per expansion. During each iteration, both trees try to connect to the nearest vertex of the other one, resulting in a guided exploration.

Homotopy Continuation Method as a Collision-Free Path Planning Method
Automatic Assignment of the Repulsion Parameter
Assignment of the Repulsion Parameter
Obstacle Grouping by Vicinity
Correction of the Repulsion Parameter
FPGA Implementation
Study Cases
Case 1
Case 2
Case 3
Discussion
Findings
Method
Concluding Remarks and Future Work
Full Text
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