Motion Planning Algorithm for a Mobile Robot with a Smart Machine Vision System

This study is devoted to the challenges of motion planning for mobile robots with smart machine vision systems. Motion planning for mobile robots in the environment with obstacles is a problem to deal with when creating robots suitable for operation in real-world conditions. The solutions found today are predominantly private, and are highly specialized, which prevents judging of how successful they are in solving the problem of effective motion planning. The purpose of this study: develop and investigate a motion planning algorithm for a mobile robot with a smart machine vision system. The research subject for this article is a motion planning algorithm for a mobile robot with a smart machine vision system. This study provides a review of domestic and foreign mobile robots that solve the motion planning problem in a known environment with unknown obstacles. The following navigation methods are considered for mobile robots: local, global, individual. In the course of work and research, a mobile robot prototype has been built, capable of recognizing obstacles of regular geometric shapes, as well as plan and correct the movement path. Environment objects are identified and classified as obstacles by means of digital image processing methods and algorithms. Distance to the obstacle and relative angle are calculated by photogrammetry methods, image quality is improved by linear contrast enhancement and optimal linear filtering using the Wiener-Hopf equation. Virtual tools, related to mobile robot motion algorithm testing, have been reviewed, which led us to selecting Webots software package for prototype testing. Conclusions have been drawn regarding the concluded research.

The potential field method is widely used for autonomous mobile robot path planning due to its elegant mathematical analysis and simplicity. However, most researches have been focused on solving the motion planning problem in a stationary environment where both targets and obstacles are stationary. This paper proposes a new potential field method for motion planning of mobile robots in a dynamic environment where the target and the obstacles are moving. Firstly, the new potential function and the corresponding virtual force are defined. Then, the problem of local minima is discussed. Finally, extensive computer simulations and hardware experiments are carried out to demonstrate the effectiveness of the dynamic motion planning schemes based on the new potential field method.

PurposeThe purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).Design/methodology/approachBased on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.FindingsThe assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.Originality/valueThe work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

Dynamics and motion planning of a wheel-legged robot with two manipulator arms is discussed in this paper. This robot can move on flat surfaces fast, while it has the capability of moving over rough terrains. The two locomotion subsystems, i.e. wheeled mechanism and legged mechanism, will be discussed separately. Wheeled locomotion mechanism includes three wheels, two of them are in the back as active wheels with a differential driver. Legged locomotion mechanism includes two legs each with 4 revolute joints, i.e. 4 degrees of freedom (DOF). Two manipulators are installed on the main body to perform variant tasks, each of them is a PRR 3 DOF arm. Using direct path method (DPM) the robot kinematics is developed, and using Lagrangian approach the equations of motion are derived. A new method, called return path method (RPM), is proposed to add the constraints of the system to the kinetics equations without introducing additional variables. To eliminate the terms corresponding to the constraints, natural orthogonal complement method is used. Finally, motion planning of the robot is investigated to develop a stable gait planning procedure.

In this paper, we propose a motion-planning method of multiple mobile robots for cooperative transportation of a large object in a three-dimensional environment. This task has various kinds of problems, such as obstacle avoidance and stable manipulation. All of these problems cannot be solved at once, since it would result in a dramatic increase of the computational time. Accordingly, we divided the motion planner into a global path planner and a local manipulation planner, designed them, and integrated them. The aim was to integrate a gross motion planner and a fine motion planner. Concerning the global path planner, we reduced the dimensions of the configuration space (C-space) using the feature of transportation by mobile robots. We used the potential field to find the solution by searching in this smaller-dimension reconstructed C-space. In the global path planner, the constraints of the object manipulation are considered as the cost function and the heuristic function in the A/sup */ search. For the local manipulation planner, we developed a manipulation technique, which is suitable for mobile robots by position control. We computed the conditions in which the object becomes unstable during manipulation and generated each robot's motion, considering the robots' motion errors and indefinite factors from the planning stage. We verified the effectiveness of our proposed motion planning method through simulations.

This paper proposes a potential field immune network (PFIN) for dynamic motion planning of mobile robots in an unknown environment with moving obstacles and a fixed/moving goal. The velocity obstacle method is utilized to determine the imminent collision obstacle. Subsequently, PFIN is implemented to guide robots to avoid collision with the most dangerous objects at every time instant. The overall response of the immune network is calculated with the aid of fuzzy logic and genetic algorithms. Simulation results are presented to verify the effectiveness of the proposed architecture in dynamic environments with single and multiple fixed/moving obstacles.

Intelligent mobile robots belong to advanced material handling systems that are finding increasing applications in modern manufacturing environments. Due to their mobility, mobile robots can adapt to changing environments in manufacturing systems and carry out various tasks such as transportation, inspection, exploration, or manipulation. On the other hand, features such as autonomy, intelligence, flexibility, and the capability to learn allow mobile robots to be widely used for many tasks, including material handling, material transporting, or part feeding tasks. Motion planning and scheduling of an intelligent mobile robot are one of the most vital issues in the field of robotics since these factors are essential for contributing to the efficiency of the overall manufacturing system. The robot scheduling problem belongs to the class of NP-hard problems and numerous efforts have been made to develop methodologies for obtaining optimal solutions to the problem. Therefore, this paper presents a review of the literature sources and gives a comparative analysis of biologically inspired optimization algorithms used to solve this problem. Four different optimization algorithms, namely genetic algorithms (GA), particle swarm optimization algorithm (PSO), chaotic particle swarm optimization algorithm (cPSO), and whale optimization algorithm (WOA) are proposed and implemented in Matlab software package. The experimental verification is carried out by using real-world benchmark examples. The experimental results indicate that all aforementioned algorithms can be successfully used for optimization of single mobile robot scheduling problem.

Motion planning of mobile robots in uncertain dynamic environments has been a hot topic in robotic literature. It requires a mobile robot to decide its motion behaviour on line using limited and noised information of the local environment from sensors. There are many methods having been proposed to deal with this problem Noticeably, artificial potential field (APF) based methods have gained increasingly popularity among researchers due to its high safety, simplicity and elegance (

Intelligent Motion Planning and Control for a Simulated Mobile Robot

This paper describes the motion planning problems of mobile robots in uncertain dynamic environments with no restrictions on the shape of obstacles. A new approach based on the behavior dynamics of collision-avoidance is proposed to deal with the real-time collision-free motion planning using the local coordinates of the mobile robot. The behavior dynamics of a dynamic collision-avoidance process was modeled, and then the motion-planning problem was transformed into a simple optimization problem with some inequality constraints based on control of the behavior dynamics. The decision-making space of this optimization problem is right the acceleration space of the mobile robot. By solving this optimization problem, the optimal behavior of the mobile robot to avoid obstacles and go to the desired target can be obtained easily. Simulations are given to illustrate the main ideas.

A novel vector-field-based motion planning algorithm for 3D nonholonomic robots

This study focuses on optimal motion planning for nonholonomic constraints mobile robot. We formulate the dynamics model of a differential-drive mobile robot by using Lagrangian mechanics, where the nonholonomic constraints are accurately described through differential equations. The optimal motion planning of the system is constructed as an optimal control problem which is then converted to a nonlinear programming problem by introducing trapezoidal collocation method, and the formulated nonlinear programming is solved by interior-point method. Compared with the prevailing methods in the field of motion planning, our proposed method can handle different kinds of path constraints, terminal conditions and collision-avoidance requirements. Simulation results indicate that the proposed approach can efficiently deal with various user-specified requirements with advantage of high computing efficiency.

This paper presents a fuzzy-flatness technique for the efficient manipulation of mobile robots with nonholonomic constraints. A decoupled “path trajectory” planning approach is firstly defined to ensure the generation of reference trajectories that respect the nonholonomic constraints. An auto-tuned endogenous feedback control law is then designed to ensure good tracking of the desired tasks despite uncertainties. The controller gains are adjusted online by using a suitable fuzzy controller. Based on the developed algorithm and the laboratory test prototype Pioneer-3dx, numerical tests were carried out to evaluate the performances of the proposed approach.

This article presents a novel approach to decentralized motion planning and conflict-resolution for multiple mobile robots. The proposed multirobot motion planning is an online operation, based on cost wave propagation within a discretized configuration space-time. By use of the planning method a framework for negotiation is developed, which permits quick decentralized and parallel decision-making. The key objective of the negotiation procedure is dynamic assignment of robot motion priorities. Thus, robots involved in a local conflict situation cooperate in planning and execution of the lowest cost motion paths without application of any centralized components. The features required for individual and cooperative motion are embedded in a hybrid control architecture. Results obtained from realistic simulation of a multirobot environment and also from experiments performed with two mobile robots demonstrate the flexibility and the efficiency of the proposed method.