PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs

• URL: http://arxiv.org/abs/2606.00104v1
• Date: Tue, 26 May 2026 10:03:22 GMT
• Title: PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs
• Abstract summary: We propose a planner-executor agent for PX4-based drones that decouples high-level mission planning from low-level control.<n>A large language model performs single-pass task planning, while execution is handled through a structured ROS 2 tool-calling interface.<n>Results highlight improved explainability, constraint enforcement, and reduced LLM calls compared to tightly coupled LLM control.
• Score: 4.022353603224729
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Foundation models are increasingly used to drive autonomous systems, yet existing approaches either keep the model in a tight control loop, raising latency and hallucination risk, or compile natural language into opaque end-to-end policies that are hard to explain, constraint and require domain-specific datasets and fine-tuning. We propose a planner-executor agent for PX4-based drones that decouples high-level mission planning from low-level control. A large language model performs single-pass task planning, while execution is handled through a structured ROS 2 tool-calling interface bridged to MAVLink. The system constructs a world model by combining modular 2D detectors (e.g., YOLO or vision-language models) with a pinhole depth projection module for 3D object localization. A constraint enforcement layer enforces altitude limits and horizontal geofencing, and bounded replanning enables recovery from execution-time action failures. We position our approach within three common design patterns for foundation-model-based robotics systems and demonstrate its feasibility in PX4 software-in-the-loop simulations in Gazebo. Results highlight improved explainability, constraint enforcement, and reduced LLM calls compared to tightly coupled LLM control. The code, dataset, videos, and other material can be found at the following link: https://github.com/erdemuysalx/PEACE

Related papers

• Multi-Agent Robotic Control with Onboard Vision-Language Models [38.78172334790013]
  Vision Language Models (VLMs) and Vision Language Action (VLA) models have shown promise in robotic control.<n>They face significant challenges regarding explainability, generalization, and compute requirements.<n>This paper presents a Multi-Agent System (MAS) architecture that addresses these limitations by deploying specialized agents on onboard hardware.
  arXiv  Detail & Related papers  (2026-07-08T13:37:31Z)
• EvoPlan: Evolutionary Neuro-Symbolic Robot Planning with Spatio-Temporal Guarantees [8.493077522675994]
  This paper presents a neuro-symbolic framework with three parts.<n>First, an offline procedure that mines a single global Signal Temporal Logic constraint on mobility from demonstration data.<n>Second, an evolutionary PDDL planner: an LLM proposes and repairs plans, programmatic validators decide which ones survive.<n>Third, a constrained execution loop: the planner's plan is compiled into waypoints, the waypoints are checked against the mined constraint, and the planner re-plans on a violation.
  arXiv  Detail & Related papers  (2026-07-07T18:42:59Z)
• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning [42.15812524999639]
  Agentic Fast-Slow Planning is a hierarchical framework that decouples perception, reasoning, planning, and control across natural timescales.<n>We show that Agentic Fast-Slow Planning improves robustness under perturbations, reducing lateral deviation by up to 45% and completion time by over 12% compared to pure MPC and an A*-guided MPC baseline.
  arXiv  Detail & Related papers  (2026-04-02T06:34:29Z)
• Fly0: Decoupling Semantic Grounding from Geometric Planning for Zero-Shot Aerial Navigation [14.466092698477858]
  Current Visual-Language Navigation (VLN) methodologies face a trade-off between semantic understanding and control precision.<n>We propose Fly0, a framework that decouples semantic reasoning from geometric planning.<n>Fly0 reduces computational overhead and improves system stability.
  arXiv  Detail & Related papers  (2026-02-02T09:06:50Z)
• OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning [50.45036742963495]
  We introduce OmniEVA, an embodied versatile planner that enables advanced embodied reasoning and task planning.<n>A Task-Adaptive 3D Grounding mechanism enables context-aware 3D grounding for diverse embodied tasks.<n>An Embodiment-Aware Reasoning framework incorporates task goals and embodiment constraints into the reasoning loop, resulting in planning decisions that are both goal-directed and executable.
  arXiv  Detail & Related papers  (2025-09-11T10:32:22Z)
• Learning to Reason and Navigate: Parameter Efficient Action Planning with Large Language Models [63.765846080050906]
  This paper proposes a novel parameter-efficient action planner using large language models (PEAP-LLM) to generate a single-step instruction at each location.<n>Experiments show the superiority of our proposed model on REVERIE compared to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2025-05-12T12:38:20Z)
• SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided Distillation [22.71952631383849]
  SmallPlan is a novel framework leveraging Large Language Models as teacher models to train lightweight Small Language Models (SLMs) for high-level path planning tasks.<n>SLMs are trained in a simulation-powered, interleaved manner with LLM-guided supervised fine-tuning (SFT) and reinforcement learning (RL)<n>We demonstrate that the fine-tuned SLMs perform competitively with larger models like GPT-4o on sequential path planning, without suffering from hallucination and overfitting.
  arXiv  Detail & Related papers  (2025-05-01T19:44:36Z)
• Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems [11.012092202226855]
  Connected multi-agent robotic systems (MRS) are prone to deadlocks in an obstacle environment. This paper explores the possibility of using text-based models, i.e., large language models (LLMs), and text-and-image-based models (VLMs), as high-level planners for deadlock resolution. We propose a hierarchical control framework where a foundation model-based high-level planner helps to resolve deadlocks by assigning a leader to the MRS along with a set of waypoints for the MRS leader.
  arXiv  Detail & Related papers  (2024-04-09T16:03:26Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• Embodied Task Planning with Large Language Models [86.63533340293361]
  We propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning with physical scene constraint. During inference, we discover the objects in the scene by extending open-vocabulary object detectors to multi-view RGB images collected in different achievable locations. Experimental results show that the generated plan from our TaPA framework can achieve higher success rate than LLaVA and GPT-3.5 by a sizable margin.
  arXiv  Detail & Related papers  (2023-07-04T17:58:25Z)
• Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents [99.17668730578586]
  Pre-trained large language models (LLMs) capture procedural knowledge about the world. Plan, Eliminate, and Track (PET) framework translates a task description into a list of high-level sub-tasks. PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.
  arXiv  Detail & Related papers  (2023-05-03T20:11:22Z)

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.