Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments

• URL: http://arxiv.org/abs/2105.01606v1
• Date: Tue, 4 May 2021 16:29:44 GMT
• Title: Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments
• Authors: Ashley Peake, Joe McCalmon, Yixin Zhang, Daniel Myers, Sarra Alqahtani, Paul Pauca
• Abstract summary: We develop an adaptive exploration approach to trade off between exploration and exploitation in one single step for UAVs. The proposed approach uses a map segmentation technique to decompose the environment map into smaller, tractable maps. The results demonstrate that our proposed approach is capable of navigating through randomly generated environments and covering more AoI in less time steps compared to the baselines.
• Score: 6.90777229452271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Performing autonomous exploration is essential for unmanned aerial vehicles (UAVs) operating in unknown environments. Often, these missions start with building a map for the environment via pure exploration and subsequently using (i.e. exploiting) the generated map for downstream navigation tasks. Accomplishing these navigation tasks in two separate steps is not always possible or even disadvantageous for UAVs deployed in outdoor and dynamically changing environments. Current exploration approaches either use a priori human-generated maps or use heuristics such as frontier-based exploration. Other approaches use learning but focus only on learning policies for specific tasks by either using sample inefficient random exploration or by making impractical assumptions about full map availability. In this paper, we develop an adaptive exploration approach to trade off between exploration and exploitation in one single step for UAVs searching for areas of interest (AoIs) in unknown environments using Deep Reinforcement Learning (DRL). The proposed approach uses a map segmentation technique to decompose the environment map into smaller, tractable maps. Then, a simple information gain function is repeatedly computed to determine the best target region to search during each iteration of the process. DDQN and A2C algorithms are extended with a stack of LSTM layers and trained to generate optimal policies for the exploration and exploitation, respectively. We tested our approach in 3 different tasks against 4 baselines. The results demonstrate that our proposed approach is capable of navigating through randomly generated environments and covering more AoI in less time steps compared to the baselines.

Related papers

• Aerial View Goal Localization with Reinforcement Learning [6.165163123577484]
  We present a framework that emulates a search-and-rescue (SAR)-like setup without requiring access to actual UAVs. In this framework, an agent operates on top of an aerial image (proxy for a search area) and is tasked with localizing a goal that is described in terms of visual cues. We propose AiRLoc, a reinforcement learning (RL)-based model that decouples exploration (searching for distant goals) and exploitation (localizing nearby goals)
  arXiv  Detail & Related papers  (2022-09-08T10:27:53Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning [5.40729975786985]
  This paper explores the idea of combining exploration with auxiliary task learning using General Value Functions (GVFs) and a directed exploration strategy. We provide a simple way to learn options (sequences of actions) instead of having to handcraft them, and demonstrate the performance advantage in three navigation tasks.
  arXiv  Detail & Related papers  (2022-03-02T05:14:11Z)
• Uncertainty-driven Planner for Exploration and Navigation [36.933903274373336]
  We consider the problems of exploration and point-goal navigation in previously unseen environments. We argue that learning occupancy priors over indoor maps provides significant advantages towards addressing these problems. We present a novel planning framework that first learns to generate occupancy maps beyond the field-of-view of the agent.
  arXiv  Detail & Related papers  (2022-02-24T05:25:31Z)
• MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments [0.7742297876120561]
  MarsExplorer is an openai-gym compatible environment tailored to exploration/coverage of unknown areas. It translates the original robotics problem into a Reinforcement Learning setup that various off-the-shelf algorithms can tackle. Four different state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the MarsExplorer environment.
  arXiv  Detail & Related papers  (2021-07-21T10:29:39Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• 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)
• Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap [0.3867363075280543]
  We propose a novel dynamic exploration planner (DEP) for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM) Our method safely explores dynamic environments and outperforms the benchmark planners in terms of exploration time, path length, and computational time.
  arXiv  Detail & Related papers  (2020-10-14T22:52:37Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)
• Environment-agnostic Multitask Learning for Natural Language Grounded Navigation [88.69873520186017]
  We introduce a multitask navigation model that can be seamlessly trained on Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks. Experiments show that environment-agnostic multitask learning significantly reduces the performance gap between seen and unseen environments.
  arXiv  Detail & Related papers  (2020-03-01T09:06:31Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

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.