Some AI based approaches on mobile robots motion planning

2019

Building autonomous and intelligent robots has been an elusive dream for researchers for some time. Simultaneous Localization and Mapping (SLAM) systems have contributed towards achieving this goal by making robots better in navigating through complex environments. Until now it has only been possible to train and teach robots to move around in particular environments using a certain set of rules and heuristics. With the sudden surge in interest in AI and Machine Learning, a lot of effort has been put in into making robots intelligent and for them to automatically learn their paths in unknown environments (also referred to as Path Planning). This however has been met with mixed results as either the solution proposed is not too practical (e.g. requires too much training) or has limited success (e.g. works in specific environments). In this research, we develop a novel autonomous path planning framework using Deep Learning which can learn to navigate in unknown environments. The syste...

In this paper we describe the problem of motion planning for mul- tiple goals in unstructured environments by using temporal logic for speci- cation of the goals. We solve the problem of mission planning for mobile robots, wherein the goal of the robot can be a complex mission speci cation rather than a classical "Go from A to B" problem. To achieve this problem we take a two dimensional map of the real environment and identify the re- gions of interest, navigable areas and non-navigable areas, which are tagged to make the nal map. We also assume that during the motion of the robot the current position is known and the environment is stationary. We describe the high level planing algorithm which gives a trajectory catering to the tem- poral logic based goal speci cation. We use optimal graph search to search for an optimal solution, while at each step checking for the validity of the temporal logic. Simulation results to various practical scenarios are provided making a robot act over high level speci cations while yielding optimal plans.

Proceedings 11th International Conference on Image Analysis and Processing, 2001

We present an architecture for mobile robot navigation based on Bayesian Networks. The architecture ullows a robot to plan the correct path inside an environment with d)vwniic obstacles. Interactions between the robot and the environnient are based on a powetful vision agent. The results of simulations, showing the effectiveness of the approach, are described.

Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007

This paper introduces a genetic algorithm (GA) planner that is able to rapidly determine optimal or nearoptimal solutions for mobile robot path planning problems in environments containing moving obstacles. The method restricts the search space to the vertices of the obstacles, obviating the need to search the entire environment as in earlier GA-based approaches. The new approach is able to produce an off-line plan through an environment containing dynamic obstacles, and can also recalculate the plan on-line to deal with any motion changes encountered. A particularly novel aspect of the work is the incorporation of the selection of robot speed into the GA genes. The results from a number of realistic environments demonstrate that planning changes in robot speed significantly improves the efficiency of movement through the static and moving obstacles.

Procedia Economics and Finance, 2012

Many difficult problem solving require computational intelligence. One of the major directions in artificial intelligence consists in the development of efficient computational intelligence algorithms, like: evolutionary algorithms, and neural networks. Systems, that operate in isolation or cooperate with each other, like mobile robots could use computational intelligence algorithms for different problems/tasks solving, however in their behavior could emerge an intelligence called system s intelligence, intelligence of a system. The traveling salesman problem TSP has a large application area. It is a well-known business problem. Maximum benefits TSP, price collecting TSP have a large number of economic applications. TSP is also used in the transport logic Raja, 2012. It also has a wide range of applicability in the mobile robotic agent path planning optimization. In this paper a mobile robotic agent path planning will be discussed, using unsupervised neural networks for the TSP solving, and from the TSP results the finding of a closely optimal path between two points in the agent working area. In the paper a modification of the criteria function of the winner neuron selection will also be presented. At the end of the paper measurement results will be presented.

This paper presents the way some Artificial Intelligence techniques can contribute to obtaining an efficient solution for a cooperative robotic problem. Based on certain abstraction and sensing procedures the problem specification, the robots' environment and their motion capabilities can be transposed to a finite state representation. The distributed nature of the considered application involving two robots and the reduced computation resources of individual robots conducted us to attaching robot deliberative components in an agent based implementation using a specialized software platform, namely JACK. The obtained multiagent system creates a framework for an approach that allows the interleaving of planning and execution. The agents apply an A* type bidirectional search to find the movement plan. The developed coordination protocol permits correct path generation even when the environment is changing while the robots are moving. The computational complexity of the proposed approach is low, and the system operation is supported by simulation experiments.

Autonomous navigation of mobile robots is an area that has witnessed a lot of research activity in the recent years due to its increasing applications. Several approaches have been proposed for the navigation of mobile robots. This review paper describes the various developments and techniques that have been applied for navigation of robots in dynamic environments with special focus on the soft computing approaches.

Path planning for mobile robots is the process of physically mapping a route which a mobile robot will follow. In general, this path planning process involves the development of an algorithm, which a mobile robot will use to determine possible paths that might be traversed from one point to another and subsequently assess which is most appropriate, and then undertake it. Several different path planning algorithms already exist, and each one has both advantages and drawbacks, hence all situations must be assessed individually, and no one algorithm is suitable for all circumstances.

This paper introduces a study of motion planning algorithms for mobile robots in which we addresses the problem of designing provable path planning algorithm in the frame work of the model with incomplete information. We introduces two such algorithms are called Bug algorithms that uses less sensing information than any other within the family of bug algorithm. This paper present the first approach to visibility-based potential application areas include surveillance, high risk military operations, video game design search-and-rescue efforts, firefighting, and law enforcement. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding.

World Academy of Research in Science and Engineering, 2020

A mobile robot is a mechatronic system that can facilitate human labor. These systems are widely used in various fields of production. A key element of a mobile robot is its navigation system. For the successful use of the navigation system of the mobile robot and its subsequent efficient operation, it is necessary to plan the route. This will avoid errors in the movement of the robot, solve the tasks. Among the main tasks of route planning for a mobile robot, as a rule, are distinguished: building a map of the robot's motion environment, and adjusting the robot's motion path. The article discusses the main points of such a generalization, provides algorithms for solving specific problems.

This article considers the problem of motion planning in the unknown environment, where terrain features and goal positioning data are used for navigation. The described control algorithm for path finding with the use of terrain-following motion is based on reactive collision avoidance methods, but also involves a strong deliberative component as well as consideration of kinematic and dynamic constraints of the autonomous mobile agent. That way common pitfalls such as generating impossible paths, losing the goal, and getting stuck in the local minima are avoided, whereas the necessary ability to react quickly to changes in the environment is ensured. Terrain-acquiring sensor model constitutes an important part of the described navigation algorithm since processing of sensor data determines agent's behavior, for example, whether it tends to choose low-lying terrain areas vs. passing above the hills, or favors close-to-horizontal motion. The implemented terrainacquiring sensor model is consistent with the simplified model of rotating laser rangefinder/ LIDAR, where terrain vision process is discrete, and could be viewed as "snapshot-based ray-tracing". Influence of sensor parameters on the character of motion is studied and acceptable parameter ranges are determined. The equations of motion are derived using Udwadia-Kalaba Equation, thus, obtained control force is always minimized. Case studies, illustrating different behavior types and resulting paths, are presented.

2006

In this paper, we consider the problem of robot motion planning in order to satisfy formulas expressible in temporal logics. Temporal logics naturally express traditional robot specifications such as reaching a goal or avoiding an obstacle, but also more sophisticated specifications such as sequencing, coverage, or temporal ordering of different tasks. In order to provide computational solutions to this problem, we first construct discrete abstractions of robot motion based on some environmental decomposition. We then generate discrete plans satisfying the temporal logic formula using powerful model checking tools, and finally translate the discrete plans to continuous trajectories using hybrid control. Critical to our approach is providing formal guarantees ensuring that if the discrete plan satisfies the temporal logic formula, then the continuous motion also satisfies the exact same formula.

2006

The problem of mobile robot navigation has received a noticeable attention over last few years. Several different approaches were presented, each having major limitations. In this paper a new, agentbased solution the problem of mobile robots navigation is proposed. It is based on a novel representation of the environment, that divides it into a number of distinct regions, and assigns autonomous software Space Agents to supervise it. Space Agents create a graph, that represents a high-level structure of the entire environment. The graph is used as a virtual space, that robot controlling agents work in. The most important features of the approach are: path planning for multiple robots based on most recent data available in the system, automated collision avoidance, simple localization of a "lost robot" and unrestricted scalability.

This article presents a computer simulated artificial intelligence (AI) agent that is able to move and interact in 2D and 3D environments. The agent has two operating modes: Manual Mode and Map or Autopilot mode. In the Manual mode the user has full control over the agent and can move it in all possible directions depending on the environment. In addition to that, the designed agent avoids hitting any obstacle by sensing them from a certain distance. The second and most important mode is the Map mode, in which the user can create a custom map, assign a starting and target location, and add predefined and sudden obstacles. The agent will then move to the target location by finding the shortest path avoiding any collision with any obstacle during the agent’s journey. The article suggests as a solution, an algorithm that can help the agent to find the shortest path to a predefined target location in a complex 3D environment, such as cities and mountains, avoiding all predefined and sudden obstacles. It also avoids these obstacles during manual control and moves the agent to a safe location automatically.

Control and Intelligent Systems, 2011

Path planning is considered as one of the core problems of autonomous mobile robots. Different approaches have been proposed with different levels of complexity, accuracy, and applicability. This paper presents a hybrid approach to the problem of path planning that can be used to find global optimal collision-free paths. This approach relies on combining potential field (PF) method and genetic algorithm (GA) which takes the strengths of both and overcomes their inherent limitations. In this integrated frame, the PF method is designed as a gradient-based searching strategy to exploit local optimal, and the GA is used to explore over the whole problem space. In this work, different implementing strategies are examined in different complexity scenarios. The conducted experiments show that global optimal paths can be achieved effectively using the proposed approach with a strategy of high diversity and memorization.