A practical autonomous path planner for turn-of-the-century planetary microrovers

With the success of Mars Pathfinder's Sojourner rover, a new era of planetary exploration has opened, with demand for highly capable mobile robots. These robots must be able to traverse long distances over rough, unknown terrain autonomously, under severe resource constraints. Based on the authors' firsthand experience with the Mars Pathfinder mission, this paper reviews issues which are critical for successful autonomous navigation of planetary rovers. No currently proposed methodology addresses all of these issues. We next report on the 'Wedgebug' algorithm, which is applicable to planetary rover navigation in SE(2). The Wedgebug algorithm is complete, correct, requires minimal memory for storage of its worked model, and uses only on-board sensors, which are guided by the algorithm to efficiently senses only the data needed for motion planning. The implementation of a version of Wedgebug on the Rocky7 Mars Rover prototype at the Jet Propulsion Laboratory is described, and experimental results from operation in simulated martian terrain are presented

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

Intelligent Navigation Service Robot Working in a Flexible and Dynamic Environment

Numerous sensor fusion techniques have been reported in the literature for a number of robotics applications. These techniques involved the use of different sensors in different configurations. However, in the case of food driving, the possibility of the implementation has been overlooked. In restaurants and food delivery spots, enhancing the food transfer to the correct table is neatly required, without running into other robots or diners or toppling over. In this project, a particular algorithm module has been proposed and implemented to enhance the robot driving methodology and maximize robot functionality, accuracy, and the food transfer experience. The emphasis has been on enhancing movement accuracy to reach the targeted table from the start to the end. Four major elements have been designed to complete this project, including mechanical, electrical, electronics, and programming. Since the floor condition greatly affecting the wheels and turning angle selection, the movement accuracy was improved during the project. The robot was successfully able to receive the command from the restaurant and go to deliver the food to the customers\u27 tables, considering any obstacles on the way to avoid. The robot has equipped with two trays to mount the food with well-configured voices to welcome and greet the customer. The performance has been evaluated and undertaken using a routine robot movement tests. As part of this study, the designed service wheeled robot required to be with a high-performance real-time processor. As long as the processor was adequate, the experimental results showed a highly effective search robot methodology. Having concluded from the study that a minimum number of sensors are needed if they are placed appropriately and used effectively on a robot\u27s body, as navigation could be performed by using a small set of sensors. The Arduino Due has been used to provide a real-time operating system. It has provided a very successful data processing and transfer throughout any regular operation. Furthermore, an easy-to-use application has been developed to improve the user experience, so that the operator can interact directly with the robot via a special setting screen. It is possible, using this feature, to modify advanced settings such as voice commands or IP address without having to return back to the code

Advanced Robot Path Planning (RRT)

Tato diplomová práce práce se zabývá plánováním cesty všesměrového mobilního robotu pomocí algoritmu RRT (Rapidly-exploring Random Tree – Rychle rostoucí náhodný strom). Teoretická část popisuje základní algoritmy plánování cesty a prezentuje bližší pohled na RRT a jeho potenciál. Praktická část práce řeší návrh a tvorbu v zásadě multiplatformní C++ aplikace v prostředí Windows 7 za použití aplikačního frameworku Qt 4.8.0, která implementuje pokročilé RRT algoritmy s parametrizovatelným řešičem a speciálním dávkovým režimem. Tento mód slouží k testování efektivnosti nastavení řešiče pro dané úlohy a je založen na post-processingu a vizualizaci výstupu měřených úloh pomocí jazyka Python. Vypočtené cesty mohou být vylepšeny pomocí zkracovacích algoritmů a výsledná trajektorie odeslána do pohonů Maxon Compact Drive všesměrové mobilní platformy pomocí CANopen. Aplikace klade důraz na moderní grafické uživatelské rozhraní se spolehlivým a výkonným 2D grafickým engine.This master's thesis deals with path planning of omnidirectional mobile robot using the RRT algorithm (Rapidly-exploring Random Tree). Theoretical part describes basic algorithms of path planning and presents closer view on RRT and its potential. Practical part deals with designing and creation of essentially multiplatform C++ application in Windows 7 environment with Qt 4.8.0 application framework, which implements advanced RRT algorithms with user-programmable solver and special batch mode. This mode is used for testing the effectiveness of solver on given tasks and it is based on postprocessing and visualization of measurement tasks output by Python language. Computed paths can be enhanced by shortening algorithms and result trajectory sent to Maxon Compact Drives of omnidirectional platform via the CANopen. Application puts emphasis on modern GUI with reliable and powerful 2D graphics engine.

A practical autonomous path planner for turn-of-the-century planetary microrovers

With the success of Mars Pathfinder's Sojourner rover, a new era of planetary exploration has opened, with demand for highly capable mobile robots. These robots must be able to traverse long distances over rough, unknown terrain autonomously, under severe resource constraints. Based on the authors' firsthand experience with the Mars Pathfinder mission, this paper reviews issues which are critical for successful autonomous navigation of planetary rovers. No currently proposed methodology addresses all of these issues. We next report on the 'Wedgebug' algorithm, which is applicable to planetary rover navigation in SE(2). The Wedgebug algorithm is complete, correct, requires minimal memory for storage of its worked model, and uses only on-board sensors, which are guided by the algorithm to efficiently senses only the data needed for motion planning. The implementation of a version of Wedgebug on the Rocky7 Mars Rover prototype at the Jet Propulsion Laboratory is described, and experimental results from operation in simulated martian terrain are presented

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

A Highly Reliable, Low Power Consumption, Low-Cost Multisensory Based System For Autonomous Navigational Mobile Robot

There has been remarkable growth in most real-time systems in the area of autonomous mobile robots. Collision-free path planning is one of the critical requirements in designing mobile robot systems since they all featured some obstacle detection techniques. This work focuses on the collaborations of low cost multi-sensor system to produce a complementary collision-free path for mobile robots. The proposed algorithm is used with a new model to produce the shortest, and most energy-efficient path from a given initial point to a goal point. Multiple sensors are utilized together, so the benefits of one compensate for the limitations of the other. The experimental results demonstrate that the robot is capable of measuring different distances to obstacles in unknown environments. Moreover, this work aims to minimize the energy consumption of a wheeled mobile robot in dynamic environments. The total energy consumption is evaluated in multiple directions, where both motional energy and operational energy are considered, while the robot is moving in dynamic environments and avoiding collisions. A time complexity analysis and a comparison of the proposed model, and states-of-arts methods are presented by using required resources and the overall performance of the proposed model. The proposed model is characterized by its low cost, low power consumption, and its efficiencies to follow the shortest path while avoiding collisions

On learning task-directed motion plans

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 119-129).Robotic motion planning is a hard problem for robots with more than just a few degrees of freedom. Modern probabilistic planners are able to solve many problems very quickly, but for difficult problems, they are still unacceptably slow for many applications. This thesis concerns the use of previous planning experience to allow the agent to generate motion plans very quickly when faced with new but related problems. We first investigate a technique for learning from previous experience by simply remembering past solutions and applying them where relevant to new problems. We find that this approach is useful in environments with very low variability in obstacle placement and task endpoints, and that it is important to keep the set of stored plans small to improve performance. However, we would like to be able to better generalize our previous experience so we next investigate a technique for learning parameterized motion plans. A parameterized motion plan is a function from planning problem parameters to a motion plan. In our approach, we learn a set of parameterized subpaths, which we can use as suggestions for a probabilistic planner, leading to substantially reduced planning times. We find that this technique is successful in several standard motion planning domains. However, as the domains get more complex, the technique produces less of an advantage. We discover that the learning problem as we have posed it is likely to be intractible, and that the complexity of the problem is due to the redundancy of the robotics platform. We suggest several possible approaches for addressing this problem as future work.by Sarah J. Finney.Ph.D