Navigational Path-Planning For All-Terrain Autonomous Agricultural Robot

• URL: http://arxiv.org/abs/2109.02015v2
• Date: Tue, 7 Sep 2021 04:53:19 GMT
• Title: Navigational Path-Planning For All-Terrain Autonomous Agricultural Robot
• Authors: Vedant Ghodke, Jyoti Madake
• Abstract summary: This report compares novel algorithms for autonomous navigation of farmlands. High-resolution grid map representation is taken into consideration specific to Indian environments. Results proved the applicability of the algorithms for autonomous field navigation and feasibility with robotic path planning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The shortage of workforce and increasing cost of maintenance has forced many farm industrialists to shift towards automated and mechanized approaches. The key component for autonomous systems is the path planning techniques used. Coverage path planning (CPP) algorithm is used for navigating over farmlands to perform various agricultural operations such as seeding, ploughing, or spraying pesticides and fertilizers. This report paper compares novel algorithms for autonomous navigation of farmlands. For reduction of navigational constraints, a high-resolution grid map representation is taken into consideration specific to Indian environments. The free space is covered by distinguishing the grid cells as covered, unexplored, partially explored and presence of an obstacle. The performance of the compared algorithms is evaluated with metrics such as time efficiency, space efficiency, accuracy, and robustness to changes in the environment. Robotic Operating System (ROS), Dassault Systemes Experience Platform (3DS Experience), MATLAB along Python were used for the simulation of the compared algorithms. The results proved the applicability of the algorithms for autonomous field navigation and feasibility with robotic path planning.

Related papers

• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning [91.95362946266577]
  Path planning is a fundamental scientific problem in robotics and autonomous navigation. Traditional algorithms like A* and its variants are capable of ensuring path validity but suffer from significant computational and memory inefficiencies as the state space grows. We propose a new LLM based route planning method that synergistically combines the precise pathfinding capabilities of A* with the global reasoning capability of LLMs. This hybrid approach aims to enhance pathfinding efficiency in terms of time and space complexity while maintaining the integrity of path validity, especially in large-scale scenarios.
  arXiv  Detail & Related papers  (2024-06-20T01:24:30Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• POA: Passable Obstacles Aware Path-planning Algorithm for Navigation of a Two-wheeled Robot in Highly Cluttered Environments [53.41594627336511]
  Passable Obstacles Aware (POA) planner is a novel navigation method for two-wheeled robots in a cluttered environment. Our algorithm allows two-wheeled robots to find a path through passable obstacles.
  arXiv  Detail & Related papers  (2023-07-16T19:44:27Z)
• Graph-Based Multi-Robot Path Finding and Planning [3.4260993997836753]
  Planning collision-free paths for multiple robots is important for real-world multi-robot systems. Recent advances have resulted in MAPF algorithms that can compute collision-free paths for hundreds of robots.
  arXiv  Detail & Related papers  (2022-06-22T18:47:00Z)
• Systematic Comparison of Path Planning Algorithms using PathBench [55.335463666037086]
  Path planning is an essential component of mobile robotics. Development of learning-based path planning algorithms has been experiencing rapid growth. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2022-03-07T01:52:57Z)
• A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops [38.4971490647654]
  We present a complete algorithmic pipeline for row-based crops autonomous navigation, specifically designed to cope with low-range sensors and seasonal variations. We build on a robust data-driven methodology to generate a viable path for the autonomous machine, covering the full extension of the crop with only the occupancy grid map information of the field.
  arXiv  Detail & Related papers  (2021-12-07T16:46:17Z)
• Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams [2.294915015129229]
  We present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.
  arXiv  Detail & Related papers  (2021-09-07T13:44:28Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows [0.0]
  Precision agriculture aims at introducing affordable and effective automation into agricultural processes. Our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows. The segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status.
  arXiv  Detail & Related papers  (2021-07-01T18:51:58Z)
• Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy [1.0312968200748118]
  This study presents a low-cost local motion planner for autonomous navigation in vineyards. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. A second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block.
  arXiv  Detail & Related papers  (2020-05-26T15:47:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.