Human-in-the-Loop Swarms: A Bionic Swarm Approach to Real-World Soil Mapping
- URL: http://arxiv.org/abs/2605.29091v1
- Date: Wed, 27 May 2026 20:50:05 GMT
- Title: Human-in-the-Loop Swarms: A Bionic Swarm Approach to Real-World Soil Mapping
- Abstract summary: This paper introduces the Bionic Swarm'', a novel system that abstractes away many of the tasks that are difficult to implement on robots but which do not contribute to the overall algorithm evaluation.<n>Human users take directions from a smartphone web-app that takes measurements from Bluetooth-connected sensors and relays them to a centralized server.<n>We then apply the algorithm on the Bionic Swarm platform to validate its function in a real-world, outdoor setting.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Swarm and field robotics face significant barriers to real-world validation due to the high cost and development time to deploy hardware. This paper introduces the ``Bionic Swarm,'' a novel system that lowers these barriers by abstracting away many of the tasks that are difficult to implement on robots but which do not contribute to the overall algorithm evaluation, giving these tasks to human users. These human users take directions from a smartphone web-app that takes measurements from Bluetooth-connected sensors and relays them to a centralized server. This server runs the swarm algorithm and directs actions to the human users. We evaluate this system through the experimental validation of a geotechnically-focused search algorithm named Score-Biased-Search, which functions by assigning a ``score'' to each location on a reconstructed map, then biases search patterns through areas of higher expected scores, and which exhibits superlinear map reconstruction relative to the number of search agents. After presenting simulation results for the algorithm, we then apply the algorithm on the Bionic Swarm platform to validate its function in a real-world, outdoor setting. This work demonstrates that this human-in-the-loop approach significantly lowers the barrier to entry for field and swarm robotics research.
Related papers
- SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms [1.52292571922932]
We propose SPACE (Swarm Planning and Control Evaluation), a Python-based simulator designed to support the research, evaluation, and comparison of decentralized Multi-Robot Task Allocation (MRTA) algorithms.
SPACE streamlines core algorithmic development by allowing users to implement decision-making algorithms as Python plug-ins, easily construct agent behavior trees via an intuitive GUI, and leverage built-in support for inter-agent communication and local task awareness.
arXiv Detail & Related papers (2024-09-06T12:38:24Z) - Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach [47.373245682678515]
This work investigates how active visual localization can be used to overcome challenges of viewpoint changes.
Specifically, we focus on the problem of selecting the optimal viewpoint at a given location.
The result demonstrates the superior performance of the data-driven approach when compared to existing methods.
arXiv Detail & Related papers (2023-10-04T08:18:30Z) - Comparing Active Learning Performance Driven by Gaussian Processes or
Bayesian Neural Networks for Constrained Trajectory Exploration [0.0]
Currently, humans drive robots to meet scientific objectives, but depending on the robot's location, the exchange of information and driving commands may cause undue delays in mission fulfillment.
An autonomous robot encoded with a scientific objective and an exploration strategy incurs no communication delays and can fulfill missions more quickly.
Active learning algorithms offer this capability of intelligent exploration, but the underlying model structure varies the performance of the active learning algorithm in accurately forming an understanding of the environment.
arXiv Detail & Related papers (2023-09-28T02:45:14Z) - Discovering Unsupervised Behaviours from Full-State Trajectories [1.827510863075184]
We propose an analysis of Autonomous Robots Realising their Abilities; a Quality-Diversity algorithm that autonomously finds behavioural characterisations.
We evaluate this approach on a simulated robotic environment, where the robot has to autonomously discover its abilities from its full-state trajectories.
More specifically, the analysed approach autonomously finds policies that make the robot move to diverse positions, but also utilise its legs in diverse ways, and even perform half-rolls.
arXiv Detail & Related papers (2022-11-22T16:57:52Z) - Multi-Robot Active Mapping via Neural Bipartite Graph Matching [49.72892929603187]
We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps.
The key to this problem lies in the goal position estimation to enable more efficient robot movements.
We propose a novel algorithm, namely NeuralCoMapping, which takes advantage of both approaches.
arXiv Detail & Related papers (2022-03-30T14:03:17Z) - Frequent Itemset-driven Search for Finding Minimum Node Separators in
Complex Networks [61.2383572324176]
We propose a frequent itemset-driven search approach, which integrates the concept of frequent itemset mining in data mining into the well-known memetic search framework.
It iteratively employs the frequent itemset recombination operator to generate promising offspring solution based on itemsets that frequently occur in high-quality solutions.
In particular, it discovers 29 new upper bounds and matches 18 previous best-known bounds.
arXiv Detail & Related papers (2022-01-18T11:16:40Z) - 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) - Task-relevant Representation Learning for Networked Robotic Perception [74.0215744125845]
This paper presents an algorithm to learn task-relevant representations of sensory data that are co-designed with a pre-trained robotic perception model's ultimate objective.
Our algorithm aggressively compresses robotic sensory data by up to 11x more than competing methods.
arXiv Detail & Related papers (2020-11-06T07:39:08Z) - Batch Exploration with Examples for Scalable Robotic Reinforcement
Learning [63.552788688544254]
Batch Exploration with Examples (BEE) explores relevant regions of the state-space guided by a modest number of human provided images of important states.
BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot.
arXiv Detail & Related papers (2020-10-22T17:49:25Z) - Learning Topometric Semantic Maps from Occupancy Grids [2.5234065536725963]
We propose a new approach for deriving such instance-based semantic maps purely from occupancy grids.
We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a random-sized map.
We evaluate our approach on several publicly available real-world data sets.
arXiv Detail & Related papers (2020-01-10T22:06:10Z)
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.