Goal Position Research Articles

Distributed Control of Unmanned Surface Vessels Using the Derivative-free Nonlinear Kalman Filter

Intelligence and autonomy is becoming a prerequisite for maritime transportation systems. In this paper a distributed control problem for unmanned surface vessels (USVs) is formulated as follows: there are N USVs which pursue another vessel (moving target). Each USV can be equipped with various sensors, such as IMU, cameras and non-imaging sensors such as sonar, radar and thermal signature sensors. At each time instant each USV can obtain measurements of the target’s cartesian coordinates. Additionally, each USV is aware of the target’s distance from a reference monitoring station (coastal or satellite monitoring units). The objective is to make the USVs converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in their motion plane. A distributed control law is developed for the USVs which enables not only convergence of the USVs to the goal position, but also makes possible to maintain the cohesion of the multi-USV system. Moreover, distributed filtering is performed, so as to obtain an estimate of the target vessel’s state vector. This provides the desirable state vector to be tracked by each one of the USVs. To this end, a new distributed nonlinear filtering method of improved accuracy and computation speed is introduced. This filtering approach, under the name Derivative-free distributed nonlinear Kalman filter is based on differential flatness theory and on an exact linearization of the target vessel’s dynamic/kinematic model. The performance of the proposed distributed filtering scheme is compared against the Extended and the Unscented Information filter.

Social categorization and cooperation in motor joint action: evidence for a joint end-state comfort.

The present study investigated to what extent group membership affects an actor's representation of their partner's task in cooperative joint action. Participants performed a joint pick-and-place task in a naturalistic, breakfast-table-like paradigm which allowed the demonstration of varying degrees of cooperation. Participants transported a wooden cup from one end of a table to the other, with one actor moving it to an intermediate position from where their partner transported it to a goal position. Hand and finger movements were recorded via 3D motion tracking to assess actors' cooperative behavior. Before the joint action task was performed, participants were categorized as belonging to the same or to different groups, supposedly based on an assessment of their cognitive processing styles. Results showed that the orientation of the actors' fingers when picking up the cup was affected by its required angle at the goal position. When placing the cup at the intermediate position, most actors adapted the rotation of the cup's handle to the joint action goal, thereby facilitating the partner's subsequent movement. Male actors demonstrated such cooperative behavior only when performing the task together with an ingroup partner, while female actors demonstrated cooperative behavior irrespective of social categorization. These results suggest that actors tend to represent a partner's end-state comfort and integrate it into their own movement planning in cooperative joint action. However, social factors like group membership may modulate this tendency.

Autonomous Mobile Robot Navigation Using Harmonic Potential Field

Mobile robot navigation has been an area of robotics which has gained massive attention among the researchers of robotics community. Path planning and obstacle avoidance are the key aspects of mobile robot navigation. This paper presents harmonic potential field based navigation algorithm for mobile robots. Harmonic potential field method overcomes the issue of local minima which was a major bottleneck in the case of artificial potential field method. The harmonic potential field is calculated using harmonic functions and Dirichlet boundary conditions are used for the obstacles, goal and initial position. The simulation results shows that the proposed method is able to overcome the local minima issue and navigate successfully from initial position to the goal without colliding into obstacles in static environment.

Real-time collision-free linear trajectory generation on GPU for crowd simulations

Crowd simulations are mostly employed to compose a background in the current scene. For such ambient crowds, it might be unnecessary to perform complex steering calculations. In this study, a steering-free crowd simulation which eliminates the computational cost arising from expensive steering maneuvers is presented. For this purpose, agents are assigned a linear trajectory that is guaranteed to be collision free before entering the simulation. These trajectories are calculated using readily available rendering pipeline of the GPU. To that end, existing agents' bounding discs are rendered in a spatio-temporal manner as each one forms a straight 3D tube and a projection from a selected initial position is captured. Using the blank areas (holes) in this image, it is possible to determine a suitable constant velocity (a goal position and a speed). In experiments, we not only assess three different methods to choose one of the candidate solutions, but also compare our approach with an existing work. Test results reveal that our technique gives better results in both populating an empty environment with agents quicker and reaching a higher maximum number of agents than the existing method.

Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm

In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly in the unknown multi-obstacle environment, this paper presented the navigation problem of a wheel mobile robot based on proximity sensors by fuzzy logic controller. Then a genetic algorithm was applied to optimize the membership function of input and output variables and the rule base of the fuzzy controller. Here the environment is unknown for the robot and contains various types of obstacles. The robot should detect the surrounding information by its own sensors only. For the special condition of path deadlock problem, a wall following method named angle compensation method was also developed here. The simulation results showed a good performance for navigation problem of mobile robots.

Navigation of autonomous mobile robot using different activation functions of wavelet neural network

Abstract An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

Thorough robot navigation based on SVM local planning

A prerequisite for autonomous robot navigation is the extraction of a path that is both efficient and safe in terms of collision. Towards this end, the paper in hand presents a novel local path planning method, incorporating the support vector machines (SVM) theory. The original SVM based module exploits a 2D map of points which are considered to be obstacles, so as to culminate in a collision free path. A unique attribute of the proposed SVM based local path planning algorithm is that it considers the consecutive positions of the global path trajectory, the embodiment of the robot and clusters the obstacles accordingly. Thus, the derived trajectory is a physically constrained path inasmuch as it considers the maximum margin notion of the SVM theory. Instead of providing a purely theoretical approach for local planning assessed using only artificial data, we integrate our local planner into an autonomous navigation system which is evaluated in real-world scenarios in order to show its efficacy. The latter framework firstly constructs a global 3D metric map of the perceived environment and then it converts it into a 2D map upon which a global path planner unrolls. The global map grows incrementally, by registering the collected point clouds over the robot’s route towards a goal position. Moreover, the navigation is supported by an obstacle detection strategy based on v-disparity images. The system–and, consequently, the presented local path planner–was evaluated in long range outdoors scenarios, navigating successfully within congestive environments.

Learning point-to-point movements on an elastic limb using dynamic movement primitives

Compliance is an important feature for robots in human–machine interaction to increase the safety of humans during such interactions. While active compliance is online adaptable and easy to control its dynamic response is determined by the sampling rate and the response rate of the controller. In contrast, passive compliance is inherent to the system. It responds naturally fast to any perturbation exerted on the robot independently of the controller frequency.This paper introduces an extension to the DMP framework which facilitates the generation of trajectories for passive compliant joint drives using reinforcement learning. The compliance of the limb is preserved entirely during motion as well as at the goal position. The proposed approach is evaluated with a simulation of a 2 DOF robot limb with passive compliant joint drives. Experiments are presented for point-to-point movements. The results demonstrate that the proposed approach is capable of generating trajectories for point-to-point movements for the gear-side of a compliant limb such that the drive-side follows a desired trajectory.

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

Simple and efficient geometric controllers, like Pure-Pursuit, have been widely used in various types of autonomous vehicles to solve tracking problems. In this paper, we have developed a new pursuit method, named CF-Pursuit, which has been based on Pure-Pursuit but with certain differences. In CF-Pursuit, in order to reduce fitting errors, we used a clothoid C1 curve to replace the circle employed in Pure-Pursuit. This improvement to the fitting method helps the Pursuit method to decrease tracking errors. As regards the selection of look-ahead distance, we employed a fuzzy system to directly consider the path's curvature. There are three input variables in this fuzzy system, 6 mcurvature, 9 mcurvature and 12 mcurvature, calculated from the clothoid fit with the current position and the goal position on the defined path. A Sugeno fuzzy model was adapted to output a reasonable look-ahead distance using the experiences of human drivers as well as our own tests. Compared with some other geometric controllers, CF-Pursuit performs better in robustness, cross track errors and stability. The results from field tests have proven the CF-Pursuit is a practical and efficient geometric method for the path tracking problems of autonomous vehicles.

Interactive dynamic tests for evaluating the development of spatial abilities in high school

In the last 10-15 years the number of studies examining spatial abilities of students has increased rapidly. The development of the spatial ability system components is important since these skills are used in everyday life and in order to reach our goal (position) people need good spatial perception in many cases. GeoGebra is a suitable and effective tool for developing these abilities. New methods for measuring these abilities can be developed that would be better adapted to today’s needs. The dynamic and interactive adaptation of available GeoGebra tests for measuring spatial abilities would place these measurements on a new ground. However, there are many unanswered questions. The technical background, results and experiences of pilot a test of these methods are presented here.

Integrated Motion Planning and Control for Multi Objectives Optimization and Multi Robots Navigation

This study proposes model of the optimal motion planner and controller for multi mobile robots. The proposed controller is responsible for avoiding the collision among multi robots after generating the optimal trajectory in term of multi objective for each robot. This work includes two stages: the first is to generate multi objective optimal path and trajectory for each robot independently from the start to the goal position using the modified genetic algorithm (MGA) with A∗ search algorithm (A∗). The second one is to establish a movement strategy to let the robots navigate with avoiding collision risk with each other by introducing Sugeno first order fuzzy controller. Five objective functions have been proposed in this study for minimizing travelling length, time, smoothness, security and trajectory and reducing the energy consumption for each mobile robot by using Cubic Spline interpolation curve fitting for optimal planned path. The simulation results show that the multi mobile robots navigate successfully with avoiding static obstacles as well as avoiding the collision between each other.

Conflict-based search for optimal multi-agent pathfinding

In the multi-agent pathfinding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions. Most previous work on solving this problem optimally has treated the individual agents as a single ‘joint agent’ and then applied single-agent search variants of the A* algorithm.In this paper we present the Conflict Based Search (CBS) a new optimal multi-agent pathfinding algorithm. CBS is a two-level algorithm that does not convert the problem into the single ‘joint agent’ model. At the high level, a search is performed on a Conflict Tree (CT) which is a tree based on conflicts between individual agents. Each node in the CT represents a set of constraints on the motion of the agents. At the low level, fast single-agent searches are performed to satisfy the constraints imposed by the high level CT node. In many cases this two-level formulation enables CBS to examine fewer states than A* while still maintaining optimality. We analyze CBS and show its benefits and drawbacks.Additionally we present the Meta-Agent CBS (MA-CBS) algorithm. MA-CBS is a generalization of CBS. Unlike basic CBS, MA-CBS is not restricted to single-agent searches at the low level. Instead, MA-CBS allows agents to be merged into small groups of joint agents. This mitigates some of the drawbacks of basic CBS and further improves performance. In fact, MA-CBS is a framework that can be built on top of any optimal and complete MAPF solver in order to enhance its performance. Experimental results on various problems show a speedup of up to an order of magnitude over previous approaches.

On generating the motion of industrial robot manipulators

In this study, an intelligent search algorithm is proposed to define the path that leads to the desired position and orientation of an industrial robot's manipulator end effector. The search algorithm gradually approaches the desired configuration by selecting and evaluating a number of alternative robot's configurations. A grid of the robot's alternative configurations is constructed using a set of parameters which are reducing the search space to minimize the computational time. In the evaluation of the alternatives, multiple criteria are used in order for the different requirements to be fulfilled. The alternative configurations are generated with emphasis being given to the robot's joints that mainly affect the position of the end effector. Grid resolution and size parameters are set on the basis of the desired output. High resolution is used for a smooth path and lower for a rough estimation, by providing only a number of the intermediate points to the goal position. The path derived is a series of robot configurations. This method provides an inexperienced robot programmer with flexibility to generate automatically a robotic path that would fulfill the desired criteria without having to record intermediate points to the goal position. Intelligent, grid based search algorithm for generating motion of industrial robot manipulators.The robot path is produced as the result of combining the joints values. The large search space is reduced by applying a set of parameters.The alternative configurations of the robot are generated giving emphasis to the robot's joints that mainly affect the desired target.Multiple-criteria are taken into account for evaluating the generated robot path towards the desired position and orientation.

Realization method for a rearrangement task by multiple mobile robots in consideration of map errors

Abstract In this paper, we propose a realization method for a rearrangement task involving multiple movable objects and mobile robots. All of the objects are transported from their initial positions to goal positions. It is important for mobile robots to cope with map errors in order to execute tasks in a real environment. However, a rearrangement task is a very complicated process involving constraints related to transportation order and scarcity of spatial resources. Therefore, it is difficult to develop an adequate method to deal with map errors and still maintain task performance. We primarily address two questions: what kinds of tactics are needed and when these tactics should be applied. Our method involves adopting a problem-partitioning structure that divides a complicated rearrangement problem into simple subproblems. Furthermore, we design a real mobile robot group. Using the realization method and real robot group, we conduct experiments involving a rearrangement task in an actual environment. The results show that our proposed method can cope with map errors quickly, while maintaining task realization performance.

Path Planning Problem

Path planning is the way of determination of a collision free path between start and goal position through obstacles cluttered in a workspace. Though it is a complex problem, but it is an essential task for the navigation and controlling the motion of autonomous robot manipulators. This NP-complete problem (those problems is difficult to solve specially in a dynamic environment where the optimal path needs to be rerouted in real time when a new obstacle appears. This paper provides two categories of path planning approaches:Deterministic and Probabilistic approaches. Deterministic methods allow achieving the same result in each execution with the same initial conditions. They are perfectly predictable, hence suitable for static environment, but not effective when they are used in a real time environment as there could be sudden changes in environment. The most used solution to overcome the problem of real time environment are the probabilistic methods such as Particle swarm optimization[pso], Ant colony optimization[aco], genetic algorithm[ga], multi agent path planning,etc. General Terms Deterministic algorithm, probabilistic algorithm, aco, pso, ga, A*algorithm, dijkstra, multi agent path planning.

Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.

Passivity-based control of robotic manipulators for safe cooperation with humans

This paper presents a novel approach to the control of articulated robots in unstructured environments. The proposed control ensures several properties. First, the controller guarantees the achievement of a goal position without getting stuck in local minima. Then, the controller makes the closed-loop system passive, which renders the approach attractive for applications where the robot needs to safely interact with humans. Finally, the control law is explicitly shaped by the safety measure – the danger field. The proposed control law has been implemented and validated in a realistic experimental scenario, demonstrating the effectiveness in driving the robot to a given configuration in a cluttered environment, without any offline planning phase. Furthermore, the passivity of the system enables the robot to easily accommodate external forces on the tool, when a physical contact between the robot and the environment is established.

Development of flexible joints for a humanoid robot that walks on an oscillating plane

In this study, we develop flexible joints for a humanoid robot that walks on an oscillating plane and discuss their effectiveness in compensating disturbances. Conventional robots have a rigid frame and are composed of rigid joints driven by geared motors. Therefore, disturbances, which may be caused by external forces from other robots, obstacles, vibration and oscillation of the surface upon which the robot is walking, and so on, are transmitted directly to the robot body, causing the robot to fall. To address this problem, we focus on a flexible mechanism. We develop flexible joints and incorporate them in the waist of a humanoid robot; the experimental task of the robot is to walk on a horizontally oscillating plane until it reaches the desired position. The robot with the proposed flexible joints, reached the goal position despite the fact that the controller was the same as that used for a conventional robot walking on a static plane. From these results, we conclude that our proposed mechanism is effective for humanoid robots that walk on an oscillating plane.

비용함수와 서브 골을 이용한 비선형 최적화 방법 기반의 이동로봇 장애물 회피 주행

We define the mobile robot navigation problem as an optimization problem to minimize the cost function with the pose error between the goal position and the position of a mobile robot. Using Gauss-Newton method for the optimization, the optimal speeds of the left and right wheels can be found as the solution of the optimization problem. Especially, the rotational speed of wheels of a mobile robot can be directly related to the overall speed of a mobile robot using the Jacobian derived from the kinematic model. When the robot detects the obstacle using sensors, the sub-goal switching method is adopted for the efficient obstacle avoidance during the navigation. The performance was evaluated using the simulation and the simulation results show the validity of the proposed method.

1D virtual force field algorithm for reflexive local path planning of mobile robots

A one-dimensional (1D) virtual force field (VFF) algorithm for real-time reflexive local path planning of mobile robots is proposed. The 1D-VFF is composed of the virtual steering, obstacle and integrated force fields (IFFs). The steering force field (SFF) is generated by the local or global goal position. This SFF leads a mobile robot to the goal. The obstacle force field (OFF) is created by the raw data of a range measurement sensor (RMS). By this OFF, a mobile robot avoids obstacles. The IFF is produced by combining the steering and OFFs in which weights between 0 and 1 are multiplied. Through this IFF, a final steering command by which a mobile robot reaches a goal by avoiding obstacles is generated. Various simulations compare the performance of the proposed 1D-VFF with the weighted virtual tangential vector (WVTV), which is the recently suggested local path planning method to overcome the U-shaped enclosure problem.