A Hybrid Evolutionary Approach for Multi Robot Coordinated Planning at Intersections

• URL: http://arxiv.org/abs/2412.01082v1
• Date: Mon, 02 Dec 2024 03:40:04 GMT
• Title: A Hybrid Evolutionary Approach for Multi Robot Coordinated Planning at Intersections
• Authors: Victor Parque,
• Abstract summary: Coordinated multi-robot motion planning at intersections is key for safe mobility in roads, factories and warehouses. We propose a new evolutionary-based algorithm using a parametric lattice-based configuration and the discrete-based RRT for collision-free multi-robot planning at intersections.
• Score: 0.0
• License:
• Abstract: Coordinated multi-robot motion planning at intersections is key for safe mobility in roads, factories and warehouses. The rapidly exploring random tree (RRT) algorithms are popular in multi-robot motion planning. However, generating the graph configuration space and searching in the composite tensor configuration space is computationally expensive for large number of sample points. In this paper, we propose a new evolutionary-based algorithm using a parametric lattice-based configuration and the discrete-based RRT for collision-free multi-robot planning at intersections. Our computational experiments using complex planning intersection scenarios have shown the feasibility and the superiority of the proposed algorithm compared to seven other related approaches. Our results offer new sampling and representation mechanisms to render optimization-based approaches for multi-robot navigation.

Related papers

• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce kinematically feasible trajectories. The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• SCoTT: Wireless-Aware Path Planning with Vision Language Models and Strategic Chains-of-Thought [78.53885607559958]
  A novel approach using vision language models (VLMs) is proposed for enabling path planning in complex wireless-aware environments. To this end, insights from a digital twin with real-world wireless ray tracing data are explored. Results show that SCoTT achieves very close average path gains compared to DP-WA* while at the same time yielding consistently shorter path lengths.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• 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)
• Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences [20.879194337982803]
  Multi-Agent Path-Finding (MAPF) algorithms have shown promise in discrete 2D domains, providing rigorous guarantees. We propose an approach for accelerating conflict-based search algorithms by leveraging their repetitive and incremental nature.
  arXiv  Detail & Related papers  (2024-03-29T20:31:07Z)
• Multi-agent Path Finding for Cooperative Autonomous Driving [8.8305853192334]
  We devise an optimal and complete algorithm, Order-based Search with Kinematics Arrival Time Scheduling (OBS-KATS), which significantly outperforms existing algorithms. Our work is directly applicable to many similarly scaled traffic and multi-robot scenarios with directed lanes.
  arXiv  Detail & Related papers  (2024-02-01T04:39:15Z)
• A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments [3.318708963153893]
  The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments.
  arXiv  Detail & Related papers  (2022-09-03T19:13:16Z)
• Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
  We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
  arXiv  Detail & Related papers  (2022-07-04T13:52:10Z)
• Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications [59.34642007625687]
  The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots. An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
  arXiv  Detail & Related papers  (2022-05-03T17:14:47Z)
• Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation [13.65857209749568]
  We demonstrate a new purely distributed technique based on a generic factor graph defining dynamics and collision constraints. We show that our method allows extremely high performance collaborative planning in a simulated road traffic scenario.
  arXiv  Detail & Related papers  (2022-03-22T11:13:36Z)
• Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams [2.294915015129229]
  We present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.
  arXiv  Detail & Related papers  (2021-09-07T13:44:28Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

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.