Expert Systems and Advanced Algorithms in Mobile Robots Path Planning

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.Motion planning is an active field in robotics domain, it is responsible for translating high-level specifications of a motion task into low-level sequences of motion commands, which respect the robot and the environments constraints. In this work many path-planning approaches have been reviewed, mainly, the rapidly exploring random tree algorithm (RRT), the cell decomposition approaches (CD), and the application of fuzzy expert system (FES) in motion planning. These approaches have been adapted to solve some of mobile robots motion-planning problems efficiently, i.e. motion planning in small and narrow areas, the global path planning in dynamic workspace, and the improvement of planning efficiency using available information about the working environments. New planning approaches have been introduced based on exploiting and combining the advantages of cell-decomposition, and RRT, in addition to use other tools i.e. fuzzy expert system, to increase the efficiency and completeness of finding a solution. This thesis also proposed solutions for other motion-planning problems, for example the identification of narrow area and the important regions when using sampling-based algorithms, the path shortening for RRT, and the problem of planning a safe path. All proposed methods were implemented and simulated in Matlab to compare them with other methods, in different workspaces and under different conditions. Moreover, the results are evaluated by statistical methods using Matlab and Minitab environments.

On-line 3D path planning for close-proximity surveying with AUVs

We present an approach for planning collision-free paths on-line for an underwater multi-robot system, which is composed by a leading Autonomous Underwater Vehicle (AUV) endowed with a multibeam sonar and high processing capabilities and a second AUV. While the leading AUV follows a safe, pre-planned survey path, the second vehicle, herein referred to as Camera Vehicle (CV), must survey the bottom in close proximity while following the leader, complementing its survey capabilities. Due to their proximity to the bottom, the CV is exposed to a collision threat. We address this problem by incrementally building a 3D map of the environment onboard the leading vehicle by means of its multibeam sonar. Using this map, we plan on-line 3D paths that are transferred to the CV for close and safe surveying of the bottom. These paths are planned using the Transition-based RRT (T-RRT) algorithm, which is an RRT-variant that considers a cost function defined over the vehicle’s configuration space, or costmap for short. By defining a costmap in terms of distance to the bottom and path distance, we are able to keep the paths at a desired offset distance from the bottom for constant-resolution surveying. We have integrated our path planning system with the software architecture of the SPARUS-II and GIRONA500 AUVs. We demonstrate the feasibility of our approach in simulation. The multi-robot system presented is based on the context of the MORPH FP7 EU project

REACTIVE MOTION REPLANNING FOR HUMAN-ROBOT COLLABORATION

Negli ultimi anni si è assistito a un incremento significativo di robot che condividono lo spazio di lavoro con operatori umani, per combinare la rapidità e la precisione proprie dei robot con l'adattabilità e l'intelligenza umana. Tuttavia, questa integrazione ha introdotto nuove sfide in termini di sicurezza ed efficienza della collaborazione. I robot devono essere in grado di adattarsi prontamente ai cambiamenti nell'ambiente circostante, come i movimenti degli operatori, adeguando in tempo reale il loro percorso per evitare collisioni, preferibilmente senza interruzioni. Inoltre, nelle operazioni di collaborazione tra uomo e robot, le traiettorie ripianificate devono rispettare i protocolli di sicurezza, al fine di evitare rallentamenti e fermate dovute alla prossimità eccessiva del robot all'operatore. In questo contesto è fondamentale fornire soluzioni di alta qualità in tempi rapidi per garantire la reattività del robot. Le tecniche di ripianificazione tradizionali tendono a faticare in ambienti complessi, soprattutto quando si tratta di robot con molti gradi di libertà e numerosi ostacoli di dimensioni considerevoli. La presente tesi affronta queste sfide proponendo un nuovo algoritmo sampling-based di ripianificazione del percorso per manipolatori robotici. Questo approccio sfrutta percorsi pre-calcolati per generare rapidamente nuove soluzioni in poche centinaia di millisecondi. Inoltre, incorpora una funzione di costo che guida l'algoritmo verso soluzioni che rispettano lo standard di sicurezza ISO/TS 15066, riducendo così gli interventi di sicurezza e promuovendo una cooperazione efficiente tra uomo e robot. Viene inoltre presentata un'architettura per gestire il processo di ripianificazione durante l'esecuzione del percorso del robot. Infine, viene introdotto uno strumento software che semplifica l'implementazione e il testing degli algoritmi di ripianificazione del percorso. Simulazioni ed esperimenti condotti su robot reali dimostrano le prestazioni superiori del metodo proposto rispetto ad altre tecniche popolari.In recent years, there has been a significant increase in robots sharing workspace with human operators, combining the speed and precision inherent to robots with human adaptability and intelligence. However, this integration has introduced new challenges in terms of safety and collaborative efficiency. Robots now need to swiftly adjust to dynamic changes in their environment, such as the movements of operators, altering their path in real-time to avoid collisions, ideally without any disruptions. Moreover, in human-robot collaborations, replanned trajectories should adhere to safety protocols, preventing safety-induced slowdowns or stops caused by the robot's proximity to the operator. In this context, quickly providing high-quality solutions is crucial for ensuring the robot's responsiveness. Conventional replanning techniques often fall short in complex environments, especially for robots with numerous degrees of freedom contending with sizable obstacles. This thesis tackles these challenges by introducing a novel sampling-based path replanning algorithm tailored for robotic manipulators. This approach exploits pre-computed paths to generate new solutions in a few hundred milliseconds. Additionally, it integrates a cost function that steers the algorithm towards solutions that strictly adhere to the ISO/TS 15066 safety standard, thereby minimizing the need for safety interventions and fostering efficient cooperation between humans and robots. Furthermore, an architecture for managing the replanning process during the execution of the robot's path is introduced. Finally, a software tool is presented to streamline the implementation and testing of path replanning algorithms. Simulations and experiments conducted on real robots demonstrate the superior performance of the proposed method compared to other popular techniques

Planning feasible and safe paths online for autonomous underwater vehicles in unknown environments

We present a framework for planning collision-free and safe paths online for autonomous underwater vehicles (AUVs) in unknown environments. We build up on our previous work and propose an improved approach. While preserving its main modules (mapping, planning and mission handler), the framework now considers motion constraints to plan feasible paths, i.e., those that meet vehicle’s motion capabilities. The new framework also incorporates a risk function to avoid navigating close to nearby obstacles, and reuses the last best known solution to eliminate time-consuming pruning routines. To evaluate this approach, we use the Sparus II AUV, a torpedo-shaped vehicle performing autonomous missions in a 2-dimensional workspace. We validate the framework’s new features by solving tasks in both simulation and real-world in-water trials and comparing results with our previous approach

Survey of Robot 3D Path Planning Algorithms

Robot 3D (three-dimension) path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints (including geometric, physical, and temporal constraints). The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses

Iterative Temporal Motion Planning for Hybrid Systems in Partially Unknown Environments

This paper considers the problem of motion planning for a hybrid robotic system with complex and nonlinear dynamics in a partially unknown environment given a temporal logic specification. We employ a multi-layered synergistic framework that can deal with general robot dynamics and combine it with an iterative planning strategy. Our work allows us to deal with the unknown environmental restrictions only when they are discovered and without the need to repeat the computation that is related to the temporal logic specification. In addition, we define a metric for satisfaction of a specification. We use this metric to plan a trajectory that satisfies the specification as closely as possible in cases in which the discovered constraint in the environment renders the specification unsatisfiable. We demonstrate the efficacy of our framework on a simulation of a hybrid second-order car-like robot moving in an office environment with unknown obstacles. The results show that our framework is successful in generating a trajectory whose satisfaction measure of the specification is optimal. They also show that, when new obstacles are discovered, the reinitialization of our framework is computationally inexpensive