Human-Centric Aware UAV Trajectory Planning in Search and Rescue
Missions Employing Multi-Objective Reinforcement Learning with AHP and
Similarity-Based Experience Replay
- URL: http://arxiv.org/abs/2402.18487v1
- Date: Wed, 28 Feb 2024 17:10:22 GMT
- Title: Human-Centric Aware UAV Trajectory Planning in Search and Rescue
Missions Employing Multi-Objective Reinforcement Learning with AHP and
Similarity-Based Experience Replay
- Authors: Mahya Ramezani and Jose Luis Sanchez-Lopez
- Abstract summary: This paper explores the effect of human-centric factor in UAV trajectory planning for Search and Rescue missions.
Through a comprehensive survey, we investigate the impact of gender cues and anthropomorphism in UAV design on public acceptance and trust.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The integration of Unmanned Aerial Vehicles (UAVs) into Search and Rescue
(SAR) missions presents a promising avenue for enhancing operational efficiency
and effectiveness. However, the success of these missions is not solely
dependent on the technical capabilities of the drones but also on their
acceptance and interaction with humans on the ground. This paper explores the
effect of human-centric factor in UAV trajectory planning for SAR missions. We
introduce a novel approach based on the reinforcement learning augmented with
Analytic Hierarchy Process and novel similarity-based experience replay to
optimize UAV trajectories, balancing operational objectives with human comfort
and safety considerations. Additionally, through a comprehensive survey, we
investigate the impact of gender cues and anthropomorphism in UAV design on
public acceptance and trust, revealing significant implications for drone
interaction strategies in SAR. Our contributions include (1) a reinforcement
learning framework for UAV trajectory planning that dynamically integrates
multi-objective considerations, (2) an analysis of human perceptions towards
gendered and anthropomorphized drones in SAR contexts, and (3) the application
of similarity-based experience replay for enhanced learning efficiency in
complex SAR scenarios. The findings offer valuable insights into designing UAV
systems that are not only technically proficient but also aligned with
human-centric values.
Related papers
- UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs.
We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
arXiv Detail & Related papers (2024-04-11T03:19:22Z) - Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents [49.85633804913796]
We present an exploration-based trajectory optimization approach, referred to as ETO.
This learning method is designed to enhance the performance of open LLM agents.
Our experiments on three complex tasks demonstrate that ETO consistently surpasses baseline performance by a large margin.
arXiv Detail & Related papers (2024-03-04T21:50:29Z) - Joint Path planning and Power Allocation of a Cellular-Connected UAV
using Apprenticeship Learning via Deep Inverse Reinforcement Learning [7.760962597460447]
This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment.
The UAV aims to maximize its uplink throughput and minimize the level of interference to the ground user equipment (UEs) connected to the neighbor cellular BSs.
An apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL)
arXiv Detail & Related papers (2023-06-15T20:50:05Z) - UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary
3D Environment [17.531224704021273]
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method.
We propose a deep reinforcement learning (DRL)-based method combined with human-in-the-loop, which allows the UAV to avoid obstacles automatically during flying.
arXiv Detail & Related papers (2023-04-07T01:44:05Z) - UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms [7.056347753829855]
Unmanned aerial vehicles (UAVs) play a key role in space-air-ground integrated network (SAGIN)
Due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs.
This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN.
arXiv Detail & Related papers (2022-11-27T20:35:03Z) - DL-DRL: A double-level deep reinforcement learning approach for
large-scale task scheduling of multi-UAV [65.07776277630228]
We propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework (DCF)
Particularly, we design an encoder-decoder structured policy network in our upper-level DRL model to allocate the tasks to different UAVs.
We also exploit another attention based policy network in our lower-level DRL model to construct the route for each UAV, with the objective to maximize the number of executed tasks.
arXiv Detail & Related papers (2022-08-04T04:35:53Z) - 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) - Transferable Deep Reinforcement Learning Framework for Autonomous
Vehicles with Joint Radar-Data Communications [69.24726496448713]
We propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions.
We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV.
We show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.
arXiv Detail & Related papers (2021-05-28T08:45:37Z) - Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs.
We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources.
As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
arXiv Detail & Related papers (2021-03-03T20:54:19Z) - Reinforcement Learning for Robust Missile Autopilot Design [0.0]
This work is pioneer in proposing Reinforcement Learning as a framework for flight control.
Under TRPO's methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance.
Results show that it is possible both to achieve the optimal performance and to improve the agent's robustness to uncertainties.
arXiv Detail & Related papers (2020-11-26T09:30:04Z)
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.