Optimized Scheduling and Positioning of Mobile Manipulators in Collaborative Applications

• URL: http://arxiv.org/abs/2512.17584v1
• Date: Fri, 19 Dec 2025 13:50:07 GMT
• Title: Optimized Scheduling and Positioning of Mobile Manipulators in Collaborative Applications
• Abstract summary: We propose a digital model-based optimization framework for mobile manipulators in human-robot collaborative environments.<n>We demonstrate improvements in cycle time, task sequencing, and adaptation to human presence in a collaborative box-packing scenario.
• Score: 4.497527935481452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing integration of mobile robots in shared workspaces requires efficient path planning and coordination between the agents, accounting for safety and productivity. In this work, we propose a digital model-based optimization framework for mobile manipulators in human-robot collaborative environments, in order to determine the sequence of robot base poses and the task scheduling for the robot. The complete problem is treated as black-box, and Particle Swarm Optimization (PSO) is employed to balance conflicting Key-Performance Indicators (KPIs). We demonstrate improvements in cycle time, task sequencing, and adaptation to human presence in a collaborative box-packing scenario.

Related papers

• Adaptive Human-Robot Collaborative Painting Combining Preference-Based Optimization and Dynamic Motion Primitives [0.0]
  This work presents a human-centered collaborative framework that integrates Preference-Based Optimization (PBO) and Dynamic Movement Primitives (DMPs) to optimize robot-assisted tasks such as painting.<n>The system allows the operator to perform the process while the robot adapts its behavior in real-time, dynamically adjusting the orientation of the piece in order to match the orientation of the operator's hand.
  arXiv  Detail & Related papers  (2026-08-03T09:42:44Z)
• Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement [0.9199488958939335]
  A fundamental challenge in multi-robot motion planning is achieving sufficient coordination without incurring the large computational expense of searching the joint configuration space of the robot group.<n>We present a method for multiple mobile robot motion planning that achieves an improvement in planning time up to an order of magnitude by leveraging the insight that we can use discrete search over a workspace decomposition to provide coordination between robots during planning.
  arXiv  Detail & Related papers  (2026-05-19T18:44:09Z)
• Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming [7.507798648267691]
  This paper introduces a framework that unifies approaches by modeling an individual's spatial behavior (motion paths) and temporal behavior (time required to complete tasks) over multiple cycles.<n>We demonstrate the importance of this dual adaptation through an ablation study in simulation and a physical robot scenario using a 7-DOF robot arm.
  arXiv  Detail & Related papers  (2026-04-21T16:49:59Z)
• Efficient and Compliant Control Framework for Versatile Human-Humanoid Collaborative Transportation [2.4087148947930634]
  We present a control framework that enables humanoid robots to perform collaborative transportation tasks with a human partner.<n>The framework supports both translational and rotational motions, which are fundamental to co-transport scenarios.<n>It comprises three components: a high-level planner, a low-level controller, and a stiffness modulation mechanism.
  arXiv  Detail & Related papers  (2025-12-08T18:52:10Z)
• Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots [1.1049608786515839]
  We propose a Cooperative and Asynchronous Transformer-based Mission Planning (CATMiP) framework to coordinate distributed decision making among agents.<n>We evaluate CATMiP in a 2D grid-world simulation environment and compare its performance against planning-based exploration methods.
  arXiv  Detail & Related papers  (2024-10-08T21:14:09Z)
• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments [54.39292848359306]
  We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
  arXiv  Detail & Related papers  (2023-03-27T01:50:45Z)
• Rearrange Indoor Scenes for Human-Robot Co-Activity [82.22847163761969]
  We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. Our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet. Our experiments show that rearranged scenes provide an average of 14% more accessible space and 30% more objects to interact with.
  arXiv  Detail & Related papers  (2023-03-10T03:03:32Z)
• A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams [0.0]
  We present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs) Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration.
  arXiv  Detail & Related papers  (2023-01-19T12:30:56Z)
• Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning [2.7716102039510564]
  We propose a novel multi-robot social-aware efficient cooperative planner that on the basis of off-policy multi-agent reinforcement learning (MARL) We adopt temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relation between each robot and the pedestrians in its field of view (FOV)
  arXiv  Detail & Related papers  (2022-11-29T03:38:47Z)
• Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks [16.323122275188354]
  We focus on the multi-robot coverage path planning problem in large-scale planar areas with random dynamic interferers. We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment. We propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station.
  arXiv  Detail & Related papers  (2022-08-05T05:36:42Z)
• Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations [96.03270112422514]
  We construct a Virtual Kinematic Chain (VKC) that consolidates the kinematics of the mobile base, the arm, and the object to be manipulated in mobile manipulations. A mobile manipulation task is represented by altering the state of the constructed VKC, which can be converted to a motion planning problem.
  arXiv  Detail & Related papers  (2021-08-03T02:59:41Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)

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.