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: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xvii 1 Introduction 2 1.1 Motivation : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 Previous Work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 1.2.1 Obstacles Representation : : : : : : : : : : : : : : : : : : : : : 4 1.2.2 Configur...
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... The most complex operation is the second one, because it requires updating the points of each VO i , i = j + 1..m, with the intersection points between VO j and each VO i , and ordering them clockwise, which leads to a complexity of O( m 2 ). This can be reduced to O( m log m) by storing the points within a more efficient structure (see Fiorini, 1995). However, this approach provides information only for computing the next control command as constrained to the acceleration window. ...
This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented.
... A velocity obstacle defines the set of vehicle velocities that would result in a collision between the vehicle and an obstacle moving at a given velocity [9]. Consider a vehicle A and a moving obstacle B, with safety radius r A and r B around them and velocities V A and V B at time t 0 . ...
Automated and more efficient methods for resolution of conflicts between aircraft are necessary to support the sustained growth of air traffic. Distributed optimization is one of the proposed conflict resolution methods which can improve efficiency; but, sometimes it imposes an unequal burden on involved agents. This paper presents a method for conflict resolution by cooperation between agents which can lead to a fair contribution of all agents in resolving the collision.
... v obstacle the velocity of the moving object (calculated by simple kinematics based on the positional coordinates in two successive instants i and i + 1), R robot the radius of the robot, and R obstacle the radius of the moving object [it is supposed that both the robot and the moving object are circular, which does not lead to a loss in generality- Fig. 1(a)]. In order to be able to determine in a simpler manner the existence or not of a collision and where it will take place, we carry out a problem transformation [17], which enables us to pass from solving a cinematic problem between two nonpunctual objects to an equivalent static problem. In the equivalent transformed problem [ Fig. 1(b)] the velocity of the moving object is null and its size is R = R robot + R obstacle + R security (1) and the robot is a punctual object with the velocity 0 ! ...
The paper describes a fuzzy control system for the avoidance of
moving objects by a robot. The objects move with no type of restriction,
varying their velocity and making turns. Due to the complex nature of
this movement, it is necessary to realize temporal reasoning with the
aim of estimating the trend of the moving object. A new paradigm of
fuzzy temporal reasoning, which we call fuzzy temporal rules (FTRs), is
used for this control task. The control system has over 117 rules, which
reflects the complexity of the problem to be tackled. The controller has
been subjected to an exhaustive validation process and examples are
shown of the results obtained
... _ x = F(x; u) = f(x) + g(x)u (16) U = fu j u i (min) u i u i (max)g (17) where (13) is the initial manifold, (14) is the terminal manifold, (15) represents the time-varying obstacles, (16) is the robot dynamics, and (17) represents the admissible controls. State constraints due to the presence of obstacles (15) are di erentiated with respect to time until they become explicit in the controls u, and then appended as state dependent control constraint to the Hamiltonian 4,11]. ...
In a constantly changing and partially unpredictable environment, robot motion planning must be on-line. The planner then receives a continuous #ow of information about occurring events and generates motoin plans. While planned motions are being executed, new plans are generated in response to incoming events. This thesis describes an on-line planner for two cooperating arms whose task is to grab parts of various types on a conveyor belt and transfer them to their respective goals while avoiding collision with obstacles. Parts arrive on the belt in random order, at any time. Both goals and obstacles may be dynamically changed. This scenario is typical of manufacturing cells serving machine-tools, assembling products, or packaging objects. The proposed approach breaks the overall planning problem into subproblems, eachinvolving a low-dimension con#guration or con#guration#time space, and orchestrates very fast primitives solving these subproblems. This thesis describes this approach and...
... _ x = F(x; u) = f(x) + g(x)u (16) U = fu j u i (min) u i u i (max)g (17) where (13) is the initial manifold, (14) is the terminal manifold, (15) represents the time-varying obstacles, (16) is the robot dynamics, and (17) represents the admissible controls. State constraints due to the presence of obstacles (15) are di erentiated with respect to time until they become explicit in the controls u, and then appended as state dependent control constraint to the Hamiltonian 4,11]. ...
This paper presents a method for computing the motions of a robot in dynamic environments, subject to the robot dynamics and its actuator constraints. This method is based on the concept of Velocity Obstacle, which defines the set of feasible robot velocities that would result in a collision between the robot and an obstacle moving at a given velocity. The avoidance maneuver at a specific time is thus computed by selecting robot's velocities out of that set. A trajectory consisting of a sequence of avoidance maneuvers at discrete time intervals is generated by a search of a tree of avoidance maneuvers. An exhaustive search computes near minimum-time trajectories, whereas a heuristic search generates feasible trajectories for on-line applications. These trajectories are compared to the optimal trajectory computed by a dynamic optimization that minimizes motion time, subject to robot dynamics, its actuator limits and the state inequality constraints due to the moving obstacles. This appr...
... The practical results are given for static environments. In [Fiorini, 1995, Fiorini and Shiller, 1998] an approach is described that uses velocity obstacles which define at every point in time the set of colliding velocities between robot and obstacles. The robot and the obstacles are represented as circles and the obstacles are moving translational. ...
Die Planung von Bewegungen ist eine der wichtigsten Aufgaben eines autonomen Robotersystems. In der Robotik-Forschung ist daher die Bewegungsplanung eines der Hauptthemen, und es wurden in den vergangenen Jahrzehnten große Anstrengungen unternommen, flexible Planungsverfahren zu entwickeln. Die dabei zu lösenden Probleme erwiesen sich als anspruchsvoll, und die meisten der existierenden Ansätze beschränken sich entweder auf das Finden von kollisionsfreien geometrischen Bahnen ohne Berücksichtigung der Roboterdynamik, oder die Berechnung einer Zeit- oder Energie optimalen Trajektorie ohne Berücksichtigung von Hindernissen. Der Vorteil eines Bewegungsplaners, der sowohl die Hindernisse im Raum als auch die Einhaltung der dynamischen Grenzen des Roboters beachtet, liegt auf der Hand. Durch den Einbezug der Zeit wird es möglich, Trajektorien zu berechnen, die sowohl kollisionsfrei sind, als auch die Dynamik des Roboters berücksichtigen. Hinzu kommt, dass es mit einem solchen Bewegungsplaner möglich wird, zeitvariante Hindernisse in den Planungsvorgang miteinzubeziehen. Diese Hindernisse haben die Eigenschaft, dass ihr Aufenthaltsort sich mit der Zeit ändert (zu jedem Zeitpunkt aber bekannt ist). Dies trifft zum Beispiel auf Szenarien zu, wie sie aus der Automobilindustrie bekannt sind. Man denke dabei an einen Roboter am Fließband, auf dem sich Werkstücke bewegen. In der vorliegenden Arbeit wird ein solches Verfahren zur Planung von Roboterbewegungen vorgestellt. Der zugrundeliegende Bewegungsplaner bezieht Roboterdynamik und zeitvariante Hindernisse in den Planungsprozess mit ein. Eine weitere Besonderheit des vorgestellten Verfahrens ist, dass die Trajektorienerzeugung vom eigentlichen Planungsvorgang entkoppelt ist. Dies hat den Vorteil, dass der Planungsvorgang über verschiedene Trajektorientypen ablaufen kann. Seien das nun Punkt-zu-Punkt Bewegungen oder überschliffene Bahnen, oder auch spezielle Trajektorien für nichtholonome Roboter. Die Planung selbst findet nur über Stützpunkte statt, die erlaubte Bereiche für die Trajektorie definieren. Durch das Modifizieren der Position und der Anzahl der Stützpunkte wird der erlaubte Trajektorienbereich und damit auch die Trajektorie selbst verändert. Um den Fortgang der Planung zu beurteilen, werden verschiedene Kriterien eingeführt, nach denen die generierte Trajektorie bewertet wird. Besonderen Augenmerk legen wir hierbei auf Kollisionsfreiheit und die dynamischen Randbedingungen, da eine Einhaltung dieser Kriterien als vorrangig anzusehen ist. Bei einer Bewegungsplanung mit zeitvarianten Hindernissen ist ein wichtiger Bestandteil die Erkennung von Kollisionen des Roboters. Wir erweitern dazu ein bestehendes Verfahren um die Möglichkeit der Kollisionserkennung in zeitvarianten Umgebungen, und stellen einen Algorithmus vor, der neben der eigentlichen Erkennung von Kollisionen auch noch unmittelbar eine Bewertung der Kollisionstiefe erlaubt.
The fundamental problem of robot motion planning in a dynamic environment (RMPDE) is to find an optimal collision-free path from the start to the goal in a dynamic environment. Our literature survey of over 100 papers from the last four decades reveals that there are more than 30 models of RMPDE, and there is no benchmarking criterion to select one that is the best in a given situation. In this context, generating a regression-based model with 10 attributes is the first and foremost contribution of our research. Given a highly human-interactive environment like a cafeteria or a bus stand, the gross hidden Markov model has special importance for modeling a robot path. A variant of the growing hidden Markov model for a serving robot in a cafeteria is the second contribution of this paper. We simulated the behavior of GHMM in a cafeteria with static and dynamic obstacles (static obstacles were both convex and concave) and with three different arrangements of the tables and obstacles. Robots have been employed in mushroom harvesting. A novel proposition discussed in this paper is probabilistic road map planning for a robot that finds an optimum path for reaching the ripened mushrooms in a randomly planted mushroom farm and a dexterous hand to pluck the selected mushrooms by employing inverse kinematics. Further, two biologically inspired meta-heuristic algorithms, ant colony optimization, and firefly has been studied for their application to latex collection. The simulation results with this environment show that the firefly algorithm outperforms ant colony optimization in the general case. Finally, we have proposed a few pointers for future research in this domain. The compilation and comparison of various approaches to robot motion planning in highly dynamic environments, and the simulation of a few models for some typical scenarios, have been the contributions of this paper.
Dynamic path planning is a core research content for intelligent robots. This paper presents a CG-Space-based dynamic path planning and obstacle avoidance algorithm for 10 DOF wheeled mobile robot (Rover) traversing over 3D uneven terrains. CG-Space is the locus of the center of gravity location of Rover while moving on a 3D terrain. A CG-Space-based modified RRT* samples a random space tree structure. Dynamic rewiring this tree can handle the randomly moving obstacles and target in real time. Simulations demonstrate that the Rover can obtain the target location in 3D uneven dynamic environments with fixed and randomly moving obstacles.
This chapter describes the application of a knowledge and reasoning representation model named Fuzzy Temporal Rules (FTRs) to the field of mobile robotics. This model makes it possible to explicitly incorporate time as a variable, thus allowing the evolution of variables in a temporal reference to be described. Specifically, two behaviors have been implemented on a Nomad 200 robot: wall following and avoidance of moving objects. In both cases it was shown how the use of FTRs not only enables us to determine the tendency of the different variables, but also to filter part of the sensor noise, as the input data are analyzed over temporal references. In this manner a more effective realization of both behaviors is obtained than by conventional fuzzy control.
Real time modelling methods are compared for use with a robot manufacturing work-cell and a simple image processing system. The static parts of a robotic manufacturing work-cell are modelled as a number of solid polyhedra. The robot is modelled as a number of connected spheres and cylinders. The static model is renewed when an object enters or leaves the static work-place. Simple polyhedra, spheres and similar 2-D slices in actuator space are compared with other models as representations of objects move in and out of the reach of the robot. Models are compared for their efficiency in accessing data and ability to update as information about moving objects changes. Geometric models of the robot and the robot work-cell are loaded into a path planner to compare the models for efficiency on planning paths around moving objects. Some preliminary results are presented.
KeywordsRobot2-D sliceObstaclePath
