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Erion Plaku

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24 papers
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24

JAIR Journal 2026 Journal Article

Multi-Agent Pathfinding Under Team-Connected Communication Constraint via Adaptive Path Expansion and Dynamic Leading

  • Hoang-Dung Bui
  • Erion Plaku
  • Gregory J. Stein

This paper proposes a novel planning framework to handle a multi-agent pathfinding problem under a team-connected communication constraint, where all agents must have a connected communication channel to the rest of the team during their entire movements. Standard multi-agent pathfinding approaches (e.g., priority-based search) have potential in this domain but routinely fail when neighboring configurations at start and goal differ. Their single-expansion approach—computing each agent’s path from the start to the goal in just a single expansion—cannot reliably handle planning under communication constraints for agents as their neighbors change during navigating. Similarly, leader-follower approaches (e.g., platooning) are effective at maintaining team communication, but fixing the leader at the outset of planning can cause planning to become stuck in dense-clutter environments, limiting their practical utility. To overcome this limitation, we propose a novel two-level multi-agent pathfinding framework that integrates two techniques: adaptive path expansion to expand agent paths to their goals in multiple stages; and dynamic leading technique that enables the reselection of the leading agent during each agent path expansion whenever progress cannot be made. Simulation experiments show the efficiency of our planning approach, which can handle up to 25 agents across five environment types under a limited communication range constraint and up to 11–12 agents on three environments types under line-of-sight communication constraint, exceeding 90 % success-rate where baselines routinely fail.

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ICRA Conference 2025 Conference Paper

Multi-Goal Motion Memory

  • Yuanjie Lu
  • Dibyendu Das
  • Erion Plaku
  • Xuesu Xiao

Autonomous mobile robots (e. g. , warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e. g. , ware-house shelves). Traditional motion planners need to calculate the entire multi-goal path from scratch in response to changes in the environment, which results in a large consumption of computing resources. This process is not only time-consuming but also may not meet real-time requirements in application scenarios that require rapid response to environmental changes. In this paper, we provide a novel Multi-Goal Motion Memory technique 1 1 https://github.com/yuanjielu-64/MGMM_ICRA2025.git that allows sampling-based motion planners to use previous planning experiences to accelerate future multi-goal planning in changing environments. This algorithm allows robots to use previous planning experiences to accelerate future multi-goal planning in changing environments. Specifically, our approach predicts dynamically feasible trajectories and distances between goal pairs to guide the sampling process to construct a motion map, to inform Traveling Salesman Problem (TSP) solvers to compute a tour, and to efficiently produce motion plans. Experiments conducted with a vehicle and a snake-like robot in obstacle-rich environments show that the proposed Motion Memory technique can substantially accelerate planning speed by up to 90%. Furthermore, the solution quality is comparable to state-of-the-art algorithms and even better in some environments.

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IROS Conference 2024 Conference Paper

Learning-informed Long-Horizon Navigation under Uncertainty for Vehicles with Dynamics

  • Abhish Khanal
  • Hoang-Dung Bui
  • Erion Plaku
  • Gregory J. Stein

We present a novel approach to learning-augmented, long-horizon navigation under uncertainty in large-scale environments in which considering the robot dynamics is essential for informing good behavior. Our approach tightly integrates sampling-based motion planning, which computes dynamically feasible routes to the goal through different unexplored boundaries, and a high-level planner that leverages predictions about unseen space to select a route that best makes progress toward the unseen goal. Owing to its ability to understand the impacts of the robot’s dynamics on how it should attempt to reach the goal, our approach achieves both higher reliability and improved navigation performance compared to competitive learning-informed and non-learned baselines in simulated office-building-like environments.

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ICRA Conference 2024 Conference Paper

Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning

  • Dibyendu Das
  • Yuanjie Lu
  • Erion Plaku
  • Xuesu Xiao

When facing a new motion-planning problem, most motion planners solve it from scratch, e. g. , via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a simple but efficient method called Motion Memory, which allows different motion planners to accelerate future planning using past experiences. Treating existing motion planners as either a closed or open box, we present a variety of ways that Motion Memory can contribute to reduce the planning time when facing a new planning problem. We provide extensive experiment results with three different motion planners on three classes of planning problems with over 30, 000 problem instances and show that planning speed can be significantly reduced by up to 89% with the proposed Motion Memory technique and with increasing past planning experiences.

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IROS Conference 2023 Conference Paper

Leveraging Single-Goal Predictions to Improve the Efficiency of Multi-Goal Motion Planning with Dynamics

  • Yuanjie Lu
  • Erion Plaku

Multi-goal motion planning requires a robot to plan collision-free and dynamically-feasible motions to reach multiple goals, often in unstructured, obstacle-rich environments. This is challenging due to the complex dependencies between navigation and high-level reasoning, requiring the robot to explore a vast space of feasible motions and goal sequences. Our approach combines machine learning and Traveling Salesman Problem (TSP) solvers with sampling-based motion planning. Machine learning predicts distances and directions between locations, considering obstacles and robot dynamics, which the TSP solver uses to compute promising tours. Sampling-based motion planning expands a motion tree to follow the tours along the predicted directions. We demonstrate the effectiveness of our approach through experiments with vehicle and snake-like robot models operating in unstructured environments with multiple goals.

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IROS Conference 2023 Conference Paper

Simultaneous Survey and Inspection with Autonomous Underwater Vehicles

  • James McMahon
  • Riley Parker
  • Philip D. Baldoni
  • Stuart Anstee
  • Erion Plaku

As the future of autonomous underwater vehicle (AUV) deployments tends to multi-vehicle systems, new approaches in coordination and control are needed. In this work, we consider the problem of simultaneous survey and inspection where one vehicle dynamically discovers objects while another vehicle must inspect as many of the objects as possible over the course of the mission. This requires a fully autonomous inspection vehicle, and to this end, we present a planning approach which couples sampling-based motion planning with timed roadmap constraints as well as a real-time execution framework. The methods presented address the underlying challenges that arise during simultaneous survey and inspection using AUVs, namely those of communication constraints, safety of navigation constraints, and dynamically discovered tasks. Additionally, we present field results for the simultaneous survey and inspection mission using teamed AUVs.

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IROS Conference 2022 Conference Paper

Improving the Efficiency of Sampling-based Motion Planners via Runtime Predictions for Motion-Planning Problems with Dynamics

  • Hoang-Dung Bui
  • Yuanjie Lu
  • Erion Plaku

While sampling-based approaches have made significant progress, motion planning with dynamics still poses significant challenges as the planner has to generate not only collision-free but also dynamically-feasible trajectories that enable the robot to reach its goal. To improve the efficiency of sampling-based motion planners, this paper develops a framework, termed Motion-Planning Runtime Prediction (MPRP), that relies on machine learning to train models to predict the expected runtime of a planner. When solving a new motion-planning problem, the trained model is then incorporated into the motion planner to more effectively guide the search toward parts of the state space that are associated with low expected runtime predictions. This paper applies the MPRP framework to state-of-the-art sampling-based motion planners to obtain new planners, which are shown to be significantly faster.

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ICRA Conference 2021 Conference Paper

Multi-Robot Motion Planning with Unlabeled Goals for Mobile Robots with Differential Constraints

  • Duong Le
  • Erion Plaku

This paper studies the multi-robot motion-planning problem with unlabeled goals where n robots have to reach m goals. The proposed approach also takes into account the underlying dynamics of each robot to produce dynamically-feasible trajectories that enable the robots to reach all the goals while avoiding collisions with the obstacles and each other. The approach leverages the idea of combining sampling-based motion planning with goal assignment and multi-agent search. In fact, the goal-assignment layer seeks to effectively utilize the robots based on estimated costs to reach the remaining goals. The multi-agent search provides nonconflicting paths over roadmap graphs, which then guide the sampling-based expansion of a motion tree. The goal assignments and multi-agent paths are frequently updated based on the progress made during the motion-tree expansion. Simulation experiments using an increasing number of robots with nonlinear dynamics demonstrate the efficiency of the approach.

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JAIR Journal 2018 Journal Article

Cooperative, Dynamics-based, and Abstraction-Guided Multi-robot Motion Planning

  • Duong Le
  • Erion Plaku

This paper presents an effective, cooperative, and probabilistically-complete multi-robot motion planner that enables each robot to move to a desired location while avoiding collisions with obstacles and other robots. The approach takes into account not only the geometric constraints arising from collision avoidance, but also the differential constraints imposed by the motion dynamics of each robot. This makes it possible to generate collision-free and dynamically-feasible trajectories that can be executed in the physical world.The salient aspect of the approach is the coupling of sampling-based motion planning to handle the complexity arising from the obstacles and robot dynamics with multi-agent search to find solutions over a suitable discrete abstraction. The discrete abstraction is obtained by constructing roadmaps to solve a relaxed problem that accounts for the obstacles but not the dynamics. Sampling-based motion planning expands a motion tree in the composite state space of all the robots by adding collision-free and dynamically-feasible trajectories as branches. Efficiency is obtained by using multi-agent search to find non-conflicting routes over the discrete abstraction which serve as heuristics to guide the motion-tree expansion. When little or no progress is made, the routes are penalized and the multi-agent search is invoked again to find alternative routes. This synergistic coupling makes it possible to effectively plan collision-free and dynamically-feasible motions that enable each robot to reach its goal. Experiments using vehicle models with nonlinear dynamics operating in complex environments, where cooperation among robots is required, show significant speedups over related work.

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IJCAI Conference 2018 Conference Paper

Multi-Robot Motion Planning with Dynamics Guided by Multi-Agent Search

  • Duong Le
  • Erion Plaku

This paper presents an effective multi-robot motion planner that enables each robot to reach its desired location while avoiding collisions with the other robots and the obstacles. The approach takes into account the differential constraints imposed by the underlying dynamics of each robot and generates dynamically-feasible motions that can be executed in the physical world. The crux of the approach is the sampling-based expansion of a motion tree in the continuous state space of all the robots guided by multi-agent search over a discrete abstraction. Experiments using vehicle models with nonlinear dynamics operating in complex environments show significant speedups over related work.

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ICAPS Conference 2017 Conference Paper

Cooperative Multi-Robot Sampling-Based Motion Planning with Dynamics

  • Duong Le
  • Erion Plaku

This paper develops an effective, cooperative, and probabilistically-complete multi-robot motion planner. The approach takes into account geometric and differential constraints imposed by the obstacles and the robot dynamics by using sampling to expand a motion tree in the composite state space of all the robots. Scalability and efficiency is achieved by using solutions to a simplified problem representation that does not take dynamics into account to guide the motion-tree expansion. The heuristic solutions are obtained by constructing roadmaps over low-dimensional configuration spaces and relying on cooperative multi-agent graph search to effectively find graph routes. Experimental results with second-order vehicle models operating in complex environments, where cooperation among the robots is required to find solutions, demonstrate significant improvements over related work.

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JAIR Journal 2016 Journal Article

A Survey of Computational Treatments of Biomolecules by Robotics-Inspired Methods Modeling Equilibrium Structure and Dynamic

  • Amarda Shehu
  • Erion Plaku

More than fifty years of research in molecular biology have demonstrated that the ability of small and large molecules to interact with one another and propagate the cellular processes in the living cell lies in the ability of these molecules to assume and switch between specific structures under physiological conditions. Elucidating biomolecular structure and dynamics at equilibrium is therefore fundamental to furthering our understanding of biological function, molecular mechanisms in the cell, our own biology, disease, and disease treatments. By now, there is a wealth of methods designed to elucidate biomolecular structure and dynamics contributed from diverse scientific communities. In this survey, we focus on recent methods contributed from the Robotics community that promise to address outstanding challenges regarding the disparate length and time scales that characterize dynamic molecular processes in the cell. In particular, we survey robotics-inspired methods designed to obtain efficient representations of structure spaces of molecules in isolation or in assemblies for the purpose of characterizing equilibrium structure and dynamics. While an exhaustive review is an impossible endeavor, this survey balances the description of important algorithmic contributions with a critical discussion of outstanding computational challenges. The objective is to spur further research to address outstanding challenges in modeling equilibrium biomolecular structure and dynamics.

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IROS Conference 2014 Conference Paper

Guiding sampling-based tree search for motion planning with dynamics via probabilistic roadmap abstractions

  • Duong Le
  • Erion Plaku

This paper focuses on motion-planning problems for high-dimensional mobile robots with nonlinear dynamics operating in complex environments. It is motivated by a recent framework that combines sampling-based motion planning in the state space with discrete search over a workspace decomposition. Building on this line of work, the premise of this paper is that the computational efficiency can be significantly improved by tightly coupling sampling-based motion planning with probabilistic roadmap abstractions instead of workspace decompositions. Probabilistic roadmap abstractions are constructed over a low-dimensional configuration space obtained by considering relaxed and simplified representations of the robot model and its feasible motions. By capturing the connectivity of the free configuration space, roadmap abstractions provide the framework with promising suggestions of how to effectively expand the sampling-based search in the full state space. Experiments with high-dimensional robot models, nonlinear dynamics, and nonholonomic constraints show significant computational speedups over related work.

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IROS Conference 2014 Conference Paper

Sampling-based tree search with discrete abstractions for motion planning with dynamics and temporal logic

  • James McMahon
  • Erion Plaku

This paper presents an efficient approach for planning collision-free, dynamically-feasible, and low-cost motion trajectories that satisfy task specifications given as formulas in a temporal logic, namely Syntactically Co-Safe Linear Temporal Logic (LTL). The planner is geared toward high-dimensional mobile robots with nonlinear dynamics operating in complex environments. The planner incorporates physics-based engines for accurate simulations of rigid-body dynamics. To obtain computational efficiency and generate low-cost solutions, the planner first imposes a discrete abstraction by combining an automaton representing the LTL formula with a workspace decomposition. The planner then uses the discrete abstraction to induce a partition of a sampling-based motion tree being expanded in the state space into equivalence classes. Each equivalence class captures the progress made toward achieving the temporal logic specifications. Heuristics defined over the abstraction are used to estimate the feasibility of expanding the motion tree from these equivalence classes and reaching an accepting automaton state. Costs are adjusted based on progress made, giving the planner the flexibility to make rapid progress while discovering new ways to expand the search. Comparisons to related work show statistically significant computational speedups and reduced solution costs.

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AAAI Conference 2013 Conference Paper

Robot Motion Planning with Dynamics as Hybrid Search

  • Erion Plaku

This paper presents a framework for motion planning with dynamics as hybrid search over the continuous space of feasible motions and the discrete space of a low-dimensional workspace decomposition. Each step of the hybrid search consists of expanding a frontier of regions in the discrete space using cost heuristics as guide followed by sampling-based motion planning to expand a tree of feasible motions in the continuous space to reach the frontier. The approach is geared towards robots with many degrees-of-freedom (DOFs), nonlinear dynamics, and nonholonomic constraints, which make it difficult to follow discrete-search paths to the goal, and hence require a tight coupling of motion planning and discrete search. Comparisons to related work show significant computational speedups.

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SoCS Conference 2012 Conference Paper

Motion Planning With Differential Constraints as Guided Search Over Continuous and Discrete Spaces

  • Erion Plaku

To compute a motion trajectory that avoids collisions, reaches a goalregion, and satisfies differential constraints imposed by robotdynamics, this paper proposes an approach that conducts a guidedsearch over the continuous space of motions and over a discrete spaceobtained by a workspace decomposition. A tree of feasible motions anda frontier of workspace regions are expanded simultaneously by firstdetermining the next region along which to expand the search and thenusing sampling-based motion planning to add trajectories to the treeto reach the selected region. When motion planning is not able toreach the selected region, its cost is increased sothat the approach has the flexibility to expand the search along newregions. Comparisons to related work show significant computationalspeedups.

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IROS Conference 2012 Conference Paper

Path planning with probabilistic roadmaps and co-safe linear temporal logic

  • Erion Plaku

Linear Temporal Logic makes it possible to express tasks in terms of propositions, logical connectives, and temporal connectives. This paper shows how to incorporate a subclass of LTL, namely co-safe LTL, into Probabilistic RoadMap (PRM) path planners. PRMs provide an important class of approaches which have been shown to work well for high-dimensional configuration spaces. The proposed Temporal-PRM approach combines the roadmap with a finite automaton representing the co-safe LTL formula φ and conducts the search over the combined graph. As a result, roadmap connections are reused when needed to find paths that satisfy φ. Experimental validation is provided in simulation by using different scenes, co-safe LTL specifications, a snake-like robot model with numerous degrees-of-freedom, and different sampling strategies.

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IROS Conference 2011 Conference Paper

Sensor and sampling-based motion planning for minimally invasive robotic exploration of osteolytic lesions

  • Wen P. Liu
  • Blake C. Lucas
  • Kelleher Guerin
  • Erion Plaku

This paper develops a sensor- and sampling-based motion planner to control a surgical robot in order to explore osteolytic lesions in orthopedic surgery. Because of the difficulty of using conventional surgical tools, such exploration is needed in minimally-invasive treatments of ¿particle diseases, ¿ which commonly result from material wear in total hip replacements. Since a geometric model of the osteolytic cavity is not always available, the planner relies only on a robot model that can detect collisions. As such, the planner can work in conjunction with real systems. The planner effectively combines global and local exploration. The global layer determines which regions to explore, while local exploration uses information gain to move the robot tip to positions in the region that increase exploration. Simulation experiments are conducted using a snake-like cannula robot on surgically-relevant osteolytic cavities. As desired in minimally-invasive treatment of osteolysis, performance is measured as the volume explored by the robot tip. The proposed method achieves 83-92% performance rate when compared to methods that require 3D models of osteolytic cavities. Comparisons to sensor-based related work (i. e. , no 3D models) show significant improvements in performance.

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ICRA Conference 2010 Conference Paper

Sampling-Based Motion and Symbolic Action Planning with geometric and differential constraints

  • Erion Plaku
  • Gregory D. Hager

To compute collision-free and dynamically-feasibile trajectories that satisfy high-level specifications given in a planning-domain definition language, this paper proposes to combine sampling-based motion planning with symbolic action planning. The proposed approach, Sampling-based Motion and Symbolic Action Planner (SMAP), leverages from sampling-based motion planning the underlying idea of searching for a solution trajectory by selectively sampling and exploring the continuous space of collision-free and dynamically-feasible motions. Drawing from AI, SMAP uses symbolic action planning to identify actions and regions of the continuous space that sampling-based motion planning can further explore to significantly advance the search. The planning layers interact with each-other through estimates on the utility of each action, which are computed based on information gathered during the search. Simulation experiments with dynamical models of vehicles carrying out tasks given by high-level STRIPS specifications provide promising initial validation, showing that SMAP efficiently solves challenging problems.

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ICRA Conference 2008 Conference Paper

Impact of workspace decompositions on discrete search leading continuous exploration (DSLX) motion planning

  • Erion Plaku
  • Lydia E. Kavraki
  • Moshe Y. Vardi

We have recently proposed DSLX, a motion planner that significantly reduces the computational time for solving challenging kinodynamic problems by interleaving continuous state-space exploration with discrete search on a workspace decomposition. An important but inadequately understood aspect of DSLX is the role of the workspace decomposition on the computational efficiency of the planner. Understanding this role is important for successful applications of DSLX to increasingly complex robotic systems. This work shows that the granularity of the workspace decomposition directly impacts computational efficiency: DSLX is faster when the decomposition is neither too fine-nor too coarse-grained. Finding the right level of granularity can require extensive fine-tuning. This work demonstrates that significant computational efficiency can instead be obtained with no fine-tuning by using conforming Delaunay triangulations, which in the context of DSLX provide a natural workspace decomposition that allows an efficient interplay between continuous state-space exploration and discrete search. The results of this work are based on extensive experiments on DSLX using grid, trapezoidal, and triangular decompositions of various granularities to solve challenging first and second-order kinodynamic motion-planning problems.

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ICRA Conference 2007 Conference Paper

A Motion Planner for a Hybrid Robotic System with Kinodynamic Constraints

  • Erion Plaku
  • Lydia E. Kavraki
  • Moshe Y. Vardi

The rapidly increasing complexity of tasks robotic systems are expected to carry out underscores the need for the development of motion planners that can take into account discrete changes in the continuous motions of the system. Completion of tasks such as exploration of unknown or hazardous environments often requires discrete changes in the controls and motions of the robot in order to adapt to different terrains or maintain operability during partial failures or other mishaps. The contribution of this work toward this objective is the development of an efficient motion planner for a hybrid robotic system. The controls and motion equations of the robot could change discretely in order to enable the robot to operate in different terrains. The framework in this paper blends discrete searching with sampling-based motion planning for continuous state spaces and is well-suited for robotic systems modeled as hybrid systems with numerous discrete modes and transitions. This multi-layered approach offers considerable improvements over existing methods addressing similar problems, as indicated by the experimental results.

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ICRA Conference 2007 Conference Paper

OOPS for Motion Planning: An Online, Open-source, Programming System

  • Erion Plaku
  • Kostas E. Bekris
  • Lydia E. Kavraki

The success of sampling-based motion planners has resulted in a plethora of methods for improving planning components, such as sampling and connection strategies, local planners and collision checking primitives. Although this rapid progress indicates the importance of the motion planning problem and the maturity of the field, it also makes the evaluation of new methods time consuming. We propose that a systems approach is needed for the development and the experimental validation of new motion planners and/or components in existing motion planners. In this paper, we present the online, open-source, programming system for motion planning (OOPS MP ), a programming infrastructure that provides implementations of various existing algorithms in a modular, object-oriented fashion that is easily extendible. The system is open-source, since a community-based effort better facilitates the development of a common infrastructure and is less prone to errors. We hope that researchers will contribute their optimized implementations of their methods and thus improve the quality of the code available for use. A dynamic Web interface and a dynamic linking architecture at the programming level allows users to easily add new planning components, algorithms, benchmarks, and experiment with different parameters. The system allows the direct comparison of new contributions with existing approaches on the same hardware and programming infrastructure

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ICRA Conference 2005 Conference Paper

Distributed Sampling-Based Roadmap of Trees for Large-Scale Motion Planning

  • Erion Plaku
  • Lydia E. Kavraki

High-dimensional problems arising from complex robotic systems test the limits of current motion planners and require the development of efficient distributed motion planners that take full advantage of all the available resources. This paper shows how to effectively distribute the computation of the Sampling-based Roadmap of Trees (SRT) algorithm using a decentralized master-client scheme. The distributed SRT algorithm allows us to solve very high-dimensional problems that cannot be efficiently addressed with existing planners. Our experiments show nearly linear speedups with eighty processors and indicate that similar speedups can be obtained with several hundred processors.

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IROS Conference 2003 Conference Paper

Multiple query probabilistic roadmap planning using single query planning primitives

  • Kostas E. Bekris
  • Brian Y. Chen
  • Andrew M. Ladd
  • Erion Plaku
  • Lydia E. Kavraki

We propose a combination of techniques that solve multiple queries for motion planning problems with single query planners. Our implementation uses a probabilistic roadmap method (PRM) with bidirectional rapidly exploring random trees (BI-RRT) as the local planner. With small modifications to the standard algorithms, we obtain a multiple query planner, which is significantly faster and more reliable than its component parts. Our method provides a smooth spectrum between the PRM and BI-RRT techniques and obtains the advantages of both. We observed that the performance differences are most notable in planning instances with several rigid nonconvex robots in a scene with narrow passages. Our work is in the spirit of non-uniform sampling and refinement techniques used in earlier work on PRM.

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