Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes

• URL: http://arxiv.org/abs/2311.16759v2
• Date: Mon, 29 Apr 2024 15:21:35 GMT
• Title: Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes
• Authors: Akshay K. Burusa, Eldert J. van Henten, Gert Kootstra,
• Abstract summary: We formulate this problem as a local next-best-view (NBV) planning task. Our formulation focuses on quickly improving the perception accuracy of a single target node to maximise its chances of being cut. We propose a gradient-based NBV planner using differential ray sampling, which directly estimates the local gradient direction for viewpoint planning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Robots are increasingly used in tomato greenhouses to automate labour-intensive tasks such as selective harvesting and de-leafing. To perform these tasks, robots must be able to accurately and efficiently perceive the plant nodes that need to be cut, despite the high levels of occlusion from other plant parts. We formulate this problem as a local next-best-view (NBV) planning task where the robot has to plan an efficient set of camera viewpoints to overcome occlusion and improve the quality of perception. Our formulation focuses on quickly improving the perception accuracy of a single target node to maximise its chances of being cut. Previous methods of NBV planning mostly focused on global view planning and used random sampling of candidate viewpoints for exploration, which could suffer from high computational costs, ineffective view selection due to poor candidates, or non-smooth trajectories due to inefficient sampling. We propose a gradient-based NBV planner using differential ray sampling, which directly estimates the local gradient direction for viewpoint planning to overcome occlusion and improve perception. Through simulation experiments, we showed that our planner can handle occlusions and improve the 3D reconstruction and position estimation of nodes equally well as a sampling-based NBV planner, while taking ten times less computation and generating 28% more efficient trajectories.

Related papers

• Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation [62.76017573929462]
  LLM-based agents have demonstrated impressive zero-shot performance in the vision-language navigation (VLN) task. We propose AO-Planner, a novel affordances-oriented planning framework for continuous VLN task. Our method establishes an effective connection between LLM and 3D world to circumvent the difficulty of directly predicting world coordinates.
  arXiv  Detail & Related papers  (2024-07-08T12:52:46Z)
• Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability [6.4111574364474215]
  The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios.
  arXiv  Detail & Related papers  (2024-01-15T18:01:59Z)
• Tree-Planner: Efficient Close-loop Task Planning with Large Language Models [63.06270302774049]
  Tree-Planner reframes task planning with Large Language Models into three distinct phases. Tree-Planner achieves state-of-the-art performance while maintaining high efficiency.
  arXiv  Detail & Related papers  (2023-10-12T17:59:50Z)
• Online Tool Selection with Learned Grasp Prediction Models [15.915410623440875]
  Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped with several end-effector tools that can be swapped on-the-fly, based on the target object. We show how to choose the order of grasps to perform, and corresponding tool-change actions, to improve system throughput.
  arXiv  Detail & Related papers  (2023-02-15T20:52:38Z)
• Achieving mouse-level strategic evasion performance using real-time computational planning [59.60094442546867]
  Planning is an extraordinary ability in which the brain imagines and then enacts evaluated possible futures. We develop a more efficient biologically-inspired planning algorithm, TLPPO, based on work on how the ecology of an animal governs the value of spatial planning. We compare the performance of a real-time agent using TLPPO against the performance of live mice, all tasked with evading a robot predator.
  arXiv  Detail & Related papers  (2022-11-04T18:34:36Z)
• Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning [36.300564378022315]
  We present a learning-enabled Task and Motion Planning (TAMP) algorithm for solving mobile manipulation problems in environments with many articulated and movable obstacles. The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan.
  arXiv  Detail & Related papers  (2022-11-03T04:12:04Z)
• Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts [0.0]
  We investigate the role of attention in improving targeted perception using an attention-driven NBV planning strategy. We show that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction.
  arXiv  Detail & Related papers  (2022-06-21T11:46:57Z)
• Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning [6.015556590955814]
  "Local planning" approaches adopt various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans but also ensure significantly reduced distances at no cost of prediction reliability.
  arXiv  Detail & Related papers  (2021-08-14T21:32:33Z)
• A feasibility study of a hyperparameter tuning approach to automated inverse planning in radiotherapy [68.8204255655161]
  The purpose of this study is to automate the inverse planning process to reduce active planning time while maintaining plan quality. We investigated the impact of the choice of dose parameters, random and Bayesian search methods, and utility function form on planning time and plan quality. Using 100 samples was found to produce satisfactory plan quality, and the average planning time was 2.3 hours.
  arXiv  Detail & Related papers  (2021-05-14T18:37:00Z)
• Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources [61.75759893720484]
  Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment and related domains. Many unpredicted factors, such as the change of observation task requirements, to the occurring of bad weather and resource failures, may cause the scheduled observation scheme to become infeasible. A bottom-up distributed coordinated framework together with an improved contract net are proposed to facilitate the dynamic task replanning for heterogeneous Earth observation resources.
  arXiv  Detail & Related papers  (2020-07-13T03:51:08Z)
• Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors [124.30562402952319]
  The ability to predict and plan into the future is fundamental for agents acting in the world. Current learning approaches for visual prediction and planning fail on long-horizon tasks. We propose a framework for visual prediction and planning that is able to overcome both of these limitations.
  arXiv  Detail & Related papers  (2020-06-23T17:58:56Z)

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.