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22nd IFAC Conference on Technology, Culture and International Stability (TECIS)

作者： Stoev, Petko Ficherov, Rosen Georgiev, Momchil Bulgarian Acad Sci Inst Informat & Commun Technol Acad G Bonchev Str 2 Sofia 1113 Bulgaria Tech Univ Sofia 8 Kliment Ohridski blvd Sofia 1000 Bulgaria Bulgarian Acad Sci Space Res & Technol Acad G Bonchev Str 1 Sofia 1113 Bulgaria

Mobile robots are revolutionizing healthcare with their potential to streamline logistics, patient care, and facility maintenance in hospitals. This paper introduces a mobile robot designed for medicine distribution in patient rooms, detailing its mechanical design and working principle. A scenario demonstrating robot path planning for medicine distribution is presented. Future research directions are outlined. Copyright (C) 2024 The Authors.

关键词： Mobile Robots Path Planning Healthcare Robotics rapidly exploring random tree RRT Monte-Carlo simulation

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Anytime Dynamic exploring Rapid random tree Approach in Higher Dimension Search Space for Non-Holonomic Robotics

Anytime Dynamic Exploring Rapid Random Tree Approach in High...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Luo, Ren C. Huang, Charly Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781509051021

With the breakthrough of exploring Rapid random tree and several other improvement efforts, the sampling-based motion planning method has been gaining ground in 2D planning and gradually being accepted by many systems as their global planning algorithm. There are many recent approaches on integrating the incremental nature and algorithmic simplicity of sampling-based approach with the agile replanning strategy studied on Incremental Heuristic based motion planning algorithms. Therefore, we develop an anytime dynamic RRT* for non-holonomic systems. We also implement Lyapunov function, which better represents the true-cost-to-go, in order to generate ideal and smoother trajectories. Our method proved to be more robust than some state-of-the-art planning algorithms, with lower cost and smoother path. We evaluate our algorithm with 3 major benchmarks in simulated as well as real-environment. We compared our algorithm with other major planning approaches and proved the cost yields between 8.5%similar to 16.7% less cost and as may reach as low as 58.17% and 95% less than RRT and RRT* respectively.

关键词： Motion Planning optimal path planning sampling-based algorithms rapidly exploring random tree Anytime Dynamic Approach

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Increasing Diversity of Solutions in Sampling-based Path Planning 18

Increasing Diversity of Solutions in Sampling-based Path Pla...

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4th International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Vonasek, Vojtech Saska, Martin Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic

ISBN: (纸本)9781450365840

Sampling-based path planning algorithms like rapidly-exploring random trees (RRT) are widely used to solve path planning problems for robots with many Degrees Of Freedom (DOF). Although the sampling-based planners are stochastic and provide different solutions in every trial, many resulting trajectories can be quite similar, e.g., because they are close to each other. Computation of di verse paths, i.e., paths leading through different parts of the configuration space, is important for online navigation of mobile robots or for finding solutions leading through different narrow passages of the configuration space. In this paper, we propose an extension of the RRT algorithm to find diverse paths in the configuration space iteratively. The idea of the proposed method is to prohibit the exploration of selected regions of the configuration space. The prohibited regions are defined using the waypoints of paths computed in the previous iterations. We show that the proposed method finds more diverse trajectories than can be achieved by repeated computations of a single sampling-based planner.

关键词： Path planning rapidly exploring random tree

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Interactive Motion Planning for Steerable Needles in 3D Environments with Obstacles

Interactive Motion Planning for Steerable Needles in 3D Envi...

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3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

作者： Patil, Sachin Alterovitz, Ron Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA

ISBN: (纸本)9781424477098

Bevel-tip steerable needles for minimally invasive medical procedures can be used to reach clinical targets that are behind sensitive or impenetrable areas and are inaccessible to straight, rigid needles. We present a fast algorithm that can compute motion plans for steerable needles to reach targets in complex, 3D environments with obstacles at interactive rates. The fast computation makes this method suitable for online control of the steerable needle based on 3D imaging feedback and allows physicians to interactively edit the planning environment in real-time by adding obstacle definitions as they are discovered or become relevant. We achieve this fast performance by using a rapidly exploring random tree (RRT) combined with a reachability-guided sampling heuristic to alleviate the sensitivity of the RRT planner to the choice of the distance metric. We also relax the constraint of constant-curvature needle trajectories by relying on duty-cycling to realize bounded-curvature needle trajectories. These characteristics enable us to achieve orders of magnitude speed-up compared to previous approaches;we compute steerable needle motion plans in under 1 second for challenging environments containing complex, polyhedral obstacles and narrow passages.

关键词： 3D environments 3D imaging feedback bevel-tip steerable needles complex polyhedral obstacles constant-curvature needle trajectories interactive motion planning medical control systems motion control needles online control position control rapidly exploring random tree reachability-guided sampling heuristics

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FPGA based Combinatorial Architecture for Parallelizing RRT

FPGA based Combinatorial Architecture for Parallelizing RRT

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European Conference on Mobile Robots

作者： Malik, Gurshaant Singh Gupta, Krishna Krishna, K. Madhava Chowdhury, Shubhajit Roy

ISBN: (纸本)9781467391634

Complex tasks are often handled through software implementation in combination with high performance processors. Taking advantage of hardware parallelism, FPGA is breaking the paradigm by accomplishing more per clock cycle with closely matched application requirements. With the aim to minimise computation delay with increase in map's size and geometric constraints, we present the FPGA based combinatorial architecture that allows multiple RRTs to work together to achieve accelerated, uniform exploration of the map. We also analyse our architecture against hardware implementation of other scalable RRT methods for motion planning. We observe notable furtherance of acceleration capabilities with the proposed architecture delivering a minimum 3X gain over the other implementations while maintaining uniformity in exploration.

关键词： parallel processing path planning random processes trees (mathematics) FPGA combinatorial architecture geometric constraints hardware parallelism high performance processors motion planning parallelizing RRT rapidly exploring random tree software implementation Acceleration Collision avoidance Computer architecture Field programmable gate arrays Hardware Robots Space exploration Field programmable gate arrays software implementation geometric constraint motion planning Space Exploration Computer Architecture Computer hardware Collision avoidance Stochastic Processes Path planning PARALLEL PROCESSING (COMPUTERS) Robot acceleration HIGH-PERFORMANCE Building construction

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Online flight path planning with flight time constraints for fixed-wing UAVs in dynamic environments

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INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2022年第4期10卷 416-443页

作者： Babel, Luitpold Univ Bundeswehr Munchen Fak Betriebswirtsch Neubiberg Germany

Purpose A major challenge for mission planning of aircraft is to generate flight paths in highly dynamic environments. This paper presents a new approach for online flight path planning with flight time constraints for fixed-wing UAVs. The flight paths must take into account the kinematic restrictions of the vehicle and be collision-free with terrain, obstacles and no-fly areas. Moreover, the flight paths are subject to time constraints such as predetermined time of arrival at the target or arrival within a specified time interval. Design/methodology/approach The proposed flight path planning algorithm is an evolution of the well-known RRT* algorithm. It uses three-dimensional Dubins paths to reflect the flight capabilities of the air vehicle. Requirements for the flight time are realized by skillfully concatenating two rapidly exploring random trees rooted in the start and target point, respectively. Findings The approach allows to consider static obstacles, obstacles which might pop up unexpectedly, as well as moving obstacles. Targets might be static or moving with constantly changing course. Even a change of the target during flight, a change of the target approach direction or a change of the requested time of arrival is included. Originality/value The capability of the flight path algorithm is demonstrated by simulation results. Response times of fractions of a second qualify the algorithm for real-time applications in highly dynamic scenarios.

关键词： Unmanned aerial vehicles Flight path planning Online path planning Flight time constraints rapidly exploring random tree Dubins path

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Iterative Solution for the Narrow Passage Problem in Motion Planning 22nd

Iterative Solution for the Narrow Passage Problem in Motion ...

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22nd Annual International Conference on Computational Science (ICCS)

作者： Szkandera, Jakub Kolingerova, Ivana Univ West Bohemia Dept Comp Sci & Engn Fac Appl Sci Univ 8 Plzen 30614 Czech Republic

ISBN: (纸本)9783031087516;9783031087509

Finding a path in a narrow passage is a bottleneck for randomised sampling-based motion planning methods. This paper introduces a technique that solves this problem. The main inspiration was the method of exit areas for cavities in protein models, but the proposed solution can also be used in another context. For data with narrow passages, the proposed method finds passageways for which sampling-based methods are not sufficient, or provides information that a collision-free path does not exist. With such information, it is possible to quit the motion planning computation if no solution exists and its further search would be a loss of time. Otherwise, the method continues to sample the space with sampling-based method (a RRT algorithm) until a solution is found or the maximum number of iterations is reached. The method was tested on real biomolecular data - dcp protein - and on artificial data (to show the superiority of the proposed solution on better-imagined data) with positive results.

关键词： Motion planning Sample-based algorithms rapidly exploring random tree Narrow passage Bottleneck Binary search

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A Comparative Study on Grid-Based and Non-grid-based Path Planning Algorithm 8

A Comparative Study on Grid-Based and Non-grid-based Path Pl...

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8th International Conference on Control and Robotics Engineering (ICCRE)

作者： Ghosh, Arindam Ojha, Muneendra Singh, Krishna Pratap IIIT Allahabad Dept IT Prayagraj India

ISBN: (纸本)9798350345650

Recent years have seen a dramatic uptick in research efforts dedicated to the development of mobile robots. One of the most common research topics in this area involves the path planning of mobile robots. The existing algorithms use the samples to construct a network or a route. There are many methods available for creating samples on the map as well. However, planners need to explore a bigger search space while building a path for the mobile robot because the samples are dispersed around the map. In this study, we examine a gridbased sampling strategy that narrows the search while still allowing us to probe potential avenues of exploration. For this objective, we implement the three most well-known path planning algorithms namely, A*, Probabilistic Roadmap (PRM), and rapidly-exploring random tree (RRT). The algorithms are compared using a grid-based path planner and a non-grid-based planner. The observed findings show that the proposed sampling technique is more effective than the previous one.

关键词： path planning probabilistic road map rapidly exploring random tree A* algorithm grid-based methods

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Expert Systems and Advanced Algorithms in Mobile Robots Path Planning

Expert Systems and Advanced Algorithms in Mobile Robots Path...

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作者： Abbadi, Ahmad Brno University of Technology

Metody plánování pohybu jsou významnou součástí robotiky, resp. mobilních robotických platforem. Technicky je realizace plánování pohybu z globální ... 详细信息

Metody plánování pohybu jsou významnou součástí robotiky, resp. mobilních robotických platforem. Technicky je realizace plánování pohybu z globální úrovně převedena do posloupnosti akcí na úrovni specifické robotické platformy a definovaného prostředí, včetně omezení. V rámci této práce byla provedena recenze mnoha metod určených pro plánování cest, přičemž hlavním těžištěm byly metody založené na tzv. rychle rostoucích stromech (RRT), prostorovém rozkladu (CD) a využití fuzzy expertních systémů (FES). Dosažené výsledky, resp. prezentované algoritmy, využívají dostupné informace z pracovního prostoru mobilního robotu a jsou aplikovatelné na řešení globální pohybové trajektorie mobilních robotů, resp. k řešení specifických problémů plánování cest s omezením typu úzké koridory či překážky s proměnnou polohou v čase. V práci jsou představeny nové plánovací postupy využívající výhod algoritmů RRT a CD. Navržené metody jsou navíc efektivně rozšířeny s využitím fuzzy expertního systému, který zlepšuje jejich chování. Práce rovněž prezentuje řešení pro plánovací problémy typu identifikace úzkých koridorů, či významných oblastí prostoru řešení s využitím přístupů na bázi dekompozice prostoru. V řešeních jsou částečně zahrnuty sub-optimalizace nalezených cest založené na zkracování nalezené cesty a vyhlazování cesty, resp. nahrazení trajektorie hladkou křivkou, respektující lépe předpokládanou dynamiku mobilního zařízení. Všechny prezentované metody byly implementovány v prostředí Matlab, které sloužilo k simulačnímu ověření efektivnosti vlastních i převzatých metod a k návrhu prostoru řešení včetně omezení (překážky). Získané výsledky byly vyhodnoceny s využitím statistických přístupů v prostředí Minitab a Matlab.

关键词： Plánování pohybu plánování cest RRT rychle rostoucí stromy expertní system fuzzy system prostorový rozklad Motion Planning Path planning rapidly exploring random tree RRT Expert system Fuzzy system Cell decomposition Text

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Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

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IEEE ACCESS 2020年 8卷 197407-197419页

作者： Liu, Haishi Chen, Qiyong Pan, Nan Sun, Yuxuan Yang, Yaluo Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Mat Sci & Engn Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Yunnan Police Officer Acad Fac Publ Secur Kunming 650223 Yunnan Peoples R China

Most research regarding multi-target, multi-base, and multi-unmanned aerial vehicle (UAV) coordinated combat mission planning faces the problems of ignoring heterogeneous UAVs, as well as poor task allocation and trajectory planning coupling. To solve these problems, based on air maneuver combat mission backgrounds, the present paper provided a heterogeneous multi-UAV cooperative mission planning method in the complex three-dimensional (3D) mountain environment. In the present paper, based on the Life-cycle Swarm Optimization (LSO) algorithm, varying the number of individuals in the population was utilized to improve the algorithm and further combined with the rapidly exploring random tree (RRT) algorithm to obtain an optimized path. Then, an improved algorithm was utilized for task allocation and trajectory optimization, and the number and speed of drones dispatched by each base were determined regarding time coordination. Finally, a simulation experiment was conducted. Numerical simulation results showed that the following algorithm was compared with the Particle Swarm Optimization (PSO) algorithm and the Whale Optimization Algorithm (WOA) when considering radar threats and solid obstacle areas. This has good approximation and high convergence accuracy, and it was effectively utilized in the planning of UAV collaborative missions in 3D complex terrain environments.

关键词： Planning Task analysis Particle swarm optimization Trajectory planning Optimization Drones Collaborative work particle swarm optimization artificial intelligence heterogeneous multi-UAV life-cycle swarm optimization algorithm rapidly exploring random tree

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