Motion Planning Research Papers - Academia.edu (original) (raw)

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- Applied Mathematics, Motion Planning, Lower Bound
- by Dorothée Normand-Cyrot
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- Motion control, Motion Planning, Digital Control, Control Systems
- by Bálint Kiss
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- Mechanical Engineering, Motion Planning, Robotic, Mechanical systems
- by Stefan Kopp
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- Artificial Intelligence, Animation, Computer Animation, Motion control

Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-today activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.

The authors discuss the overall structural design of a self-contained hydraulic six-legged machine, called MECANT I, which has been constructed for studying possible applications of legged vehicles in natural environments, like forests. First, the main mechanical solutions and control hardware of the machine are presented. The systematic approach to the motion planning of a legged vehicle and some algorithmical features are discussed. The special features of the leg control are presented. The use of a simulator in the design work is discussed, and the main principles of the control software structure, which supports fast software design and testing, are presented. Some continuing research topics and the present situation are discussed

- by Hannu Lehtinen
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- Motion Planning, Structural Design, Trajectory Planning, Terrain analysis
- by Herbert Tanner
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- Optimal Control, Model Predictive Control, Motion Planning, Stability
- by Joshua Aaron
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- Mechanical Engineering, Applied Mathematics, Motion Planning, Distance metric

... Sensory information comes from a Laser Range Finder also developed by the author [6]. Range data are made up of hundreds of 2D relative coordinate points of targets in the field of view of the sensor camera, already filtered and... more

- by Marta Marron-Romera
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- Computer Science, Motor Control, Motion Planning, Computer Hardware
- by Jonathan Chauvin
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- Mechanical Engineering, Applied Mathematics, System Identification, Modeling
- by Ellips Masehian
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- Robotics, Autonomous Robotics, Motion Planning, Mobile Robot Navigation
- by Ming Lin
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- Motion Planning, Adaptive Dynamics, Route Planning
- by Eduardo Miranda
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- Motion Planning, Virtual Environment, Inverse Kinematics, Design for Manufacture
- by jason campbell
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- Motion Planning, Automation, Dimensional

We present the exploring/exploiting tree (EET) algorithm for motion planning. The EET planner deliberately trades probabilistic completeness for computational efficiency. This tradeoff enables the EET planner to outperform state-of-the-art sampling-based planners by up to three orders of magnitude. We show that these considerable speedups apply for a variety of challenging real-world motion planning problems. The performance improvements are achieved by leveraging work space information to continuously adjust the sampling behavior of the planner. When the available information captures the planning problem's inherent structure, the planner's sampler becomes increasingly exploitative. When the available information is less accurate, the planner automatically compensates by increasing local configuration space exploration. We show that active balancing of exploration and exploitation based on workspace information can be a key ingredient to enabling highly efficient motion planning in practical scenarios.

- by Markus Rickert and +1
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- Robotics, Robotics (Computer Science), Motion Planning, Path planning
- by Daniel Thalmann
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- Cognitive Science, Group Behavior, Motion Planning, Path planning

The DARPA Urban Challenge was a competition to develop autonomous vehicles capable of safely, reliably and robustly driving in traffic. In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans in both on roads and in unstructured zones. An essential part of Boss’ success stems from its ability to safely handle both abnormal situations and system glitches.

- by John Dolan
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- Robotics, Cognitive Science, Artificial Intelligence, Modeling
- by Jüri Vain and +1
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- Engineering, Model Checking, Motion Analysis, Motion Planning

In this paper we consider the problem of controlling multiple robots manipulating and transporting an object in three dimensions via cables. We develop robot configurations that ensure static equilibrium of the object at a desired pose while respecting bounded tension constraints. Workspace and stability considerations are discussed as well as the effects of actuation and sensing errors on the performance

- by Vijay Kumar
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- Mechanical Engineering, Robotics, Cognitive Science, Computer Science

A key trait of an autonomous vehicle is the ability to handle multiple objectives, such as planning to guide the autonomous vehicle from an initial to a goal configuration, avoiding obstacles, and decision making to choose an optimal action policy. Moreover, the autonomous vehicle should act in ways that are robust to sorts of uncertainties, such as wheel slip, sensors affected by noise, obstacle move unpredictably, etc. In this paper we propose a decision making framework for autonomous vehicle conducting a nonholonomic motion planner, localization, obstacle avoidance, and also dealing with the uncertainties. The decision making framework manages the safety and task related assignment by adopting the partially observable Markov decision process model. We predict N-steps belief states from the action sequence candidate and use them as inputs of a proposed reward value function. The dimensionality problem in the searching of an action sequence from the action space (linear and rotational velocities) is simplified by applying a nonholonomic motion planner as a reference. The simulation results show the decision path resulting from decision making framework depends on the initial setting of belief state of its position and orientation, and also the determination of uncertainties of the sensors and the actuators of the vehicle.

- by Augie Widyotriatmo
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- Markov Processes, Motion Planning, Mobile Robots, Obstacle Avoidance

Multi-robot exploration problem is generally constituted of a determination of next-best-views (NBVs), path planning, and coordination algorithm. This paper presents a unified 3D multi-robot exploration algorithm in order to solve the inefficiency that takes place when the aforementioned three components are constructed individually. The proposed algorithm is composed of two parts: an allocation of exploration regions and a determination of the best path. For the allocation of the region to explore, each robot generates a sampling-based tree, e.g. RRT, which composes a Voronoi-biased forest (VBF). A VBF, a new data structure introduced within this work, assigns a region for each robot to explore in a probabilistic manner. The amount of the space a VBF covers is quantitatively analyzed depending on its parameters. From the generated VBF, each robot determines the best path from branches of its tree based on the amount of information can be gained along with the paths of peer robots. Only the first edge of the best branch of each tree is executed in a receding horizon scheme. The overall exploration algorithm is evaluated in a computer simulated environment. The results demonstrate that our coordination algorithm allows robots to quickly and reliably explore the environment.

- by Jaeho Lee
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- Robotics, Mobile Robotics, Autonomous Robotics, Motion Planning
- by Ali Mp
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- Motion Planning, Genetic Algorithm, Trajectory Planning, Travel Time
- by Amir-Homayoun Javadi
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- Motion Planning, Propulsion, Stability, Path planning
- by Brice Rebsamen and +1
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- Motion control, Motion Planning, Control Systems, Automation

The bottleneck in Artificial Intelligence in the past was missing interacting capabilities and the absence of a reward function. Both elements are needed before the AI can be created. In the following paper, the problem of a robot control system for a synthetic horse is introduced. The human operator sends commands in natural language to the horse and then the horse has to determine the next action. How to create the source code is out of reach of this paper.

- by Manuel Rodriguez
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- Motion Planning, Potential Function, Voice Controlled

A collision free path to a target location in a random farm is computed by employing a probabilistic roadmap (PRM) that can handle static and dynamic obstacles. The location of ripened mushrooms is an input obtained by image processing. A mushroom harvesting robot is discussed that employs inverse kinematics (IK) at the target location to compute the state of a robotic hand for holding a ripened mushroom and plucking it. Kinematic model of a two-finger dexterous hand with 3 degrees of freedom for plucking mushrooms was developed using the Denavit-Hartenberg method. Unlike previous research in mushroom harvesting, mushrooms are not planted in a grid or some pattern, but are randomly distributed. No human intervention is required at any stage of harvesting.

- by Roland Lenain
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- Model Predictive Control, Motion control, Automatic Control, Motion Planning
- by N. Petit
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- Applied Mathematics, Calculus, Motion Planning, Free boundary Problem
- by Diederik Verscheure
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- Optimal Control, Convex Optimization, Motion Planning, Path planning
- by Nikos Aspragathos and +1
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- Mechanical Engineering, Motion Planning, Genetic Algorithm, Path planning

Robot motion planning has always been one of the fundamental issues in robotic area. In this paper three motion planning algorithms, A*, Rapidly Exploring Random tree (RRT) and RRT*, are simulated and compared. A* algorithm is a well-known method in motion planning problems which can find the optimum path between two point in a finite time. In contrast the RRT family algorithm, by random sampling from the environment, converges to a collision-free path. By simulating these algorithms in complex environments by using java language, it is concluded that RRT family algorithms are significantly faster than A* algorithm however the paths which are found by RRT algorithms are longer than A*. Also the influence of different distance measurement methods such as Manhattan distance and Euclidean distance, on the number of seen nodes and the path length is studied.

- by Mohammadreza Asghari Oskoei
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- Motion Planning, Robot Path Planning
- by Selcuk Bayraktar
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- Hybrid Systems, System Integration, Motion Planning, Control Systems

An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is problematic for real traffic scenarios, we introduce in this paper a novel neural network-based method for path planning, which employs a gradient-based self-supervised learning algorithm to predict feasible paths. This approach strongly exploits the experience gained in the past and rapidly yields feasible maneuver plans for car-like vehicles with limited steering-angle. The effectiveness of suc...

- by Piotr Kicki
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- Robotics, Computer Science, Artificial Intelligence, Machine Learning

Model Predictive Control (MPC) was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control inputs in real time for processes with fast dynamics. In this paper a novel method called Sam- pling Based Model Predictive Control (SBMPC) is proposed as a "fast" MPC algorithm to generate control

Landmines are dangerous military equipments that threat people’s lives and cause economic problems. They are harmful because of their unknown locations and difficulty to detect. Detecting and clearing mines demand special expertise. In this paper, we present a low-cost system for landmines detection. We describe the strategy that can help to detect mines using a multisensor robot and path planning algorithm for searching mines. We present how the robot can get information from different sensors to guide soldiers to detect landmines. The purpose is to give an efficient solution of the landmines problem by using a tele mobile robots that are capable of exploring and destroying buried landmines. The proposed robot is using sensor fusion technique to increase the probability of mine detection. We developed decision level fusion to decrease false alarms.

- by Ahmed Ismail Ebada and +1
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- Motion Planning, Sensor Fusion, Landmine Detection, Low Cost Robotics

- by pooria lajevardy
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- Motion Planning, Robot Path Planning
- by Paolo Fiorini
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- Robotics, Autonomous Robotics, Motion Planning, Mobile Robot Navigation
- by Ryan Eustice
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- Mechanical Engineering, Robot Vision, Information Filtering, Machine Vision
- by Gyula Hermann
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- Distributed Computing, Motion Planning, CAD, CAM
- by Asela Kulatunga
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- Ant Colony Optimization, Motion Planning, Path planning, Autonomous Vehicle

- by Piotr Kicki
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- Robotics, Computer Science, Artificial Intelligence, Machine Learning

In this study, the method of direct-motion diagonal and perpendicular parking is introduced both in theo-ry and in simulations for the vehicle with and without a trailer. The results show that not only a vehicle with a trailer park in diagonal or perpendicular using direct motion, but also a vehicle with a trailer park in diagonal and perpendicular in direct motion after the final value of the orientation angle is satisfied. Simulation results are given to verify the effec-tiveness of the proposed method. Keywords: diagonal, perpendicular, direct motion, parking, trailer, car-like vehicle Paper videos can be reached from this URL: http://www.youtube.com/playlist?list=PLENSkat0854vGceT1fX4Yr6Yw0UqqW_vl

- by Selcuk Bayraktar
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- Hybrid Systems, System Integration, Motion Planning, Control Systems

The advancement of artificial intelligence (AI) has truly stimulated the development and deployment of autonomous vehicles (AVs) in the transportation industry. Fueled by big data from various sensing devices and advanced computing resources, AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion. To achieve goal of full automation (i.e., self-driving), it is important to know how AI works in AV systems. Existing research have made great efforts in investigating different aspects of applying AI in AV development. However, few studies have offered the research community a thorough examination of current practices in implementing AI in AVs. Thus, this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue. Specifically, it intends to analyze their use of AIs in supporting the primary applications in AVs: 1) perception; 2) localization and mapping; and 3) decision making. It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation. Based on the exploration of current practices and technology advances, this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1) high definition maps, big data, and high performance computing; 2) augmented reality (AR)/virtual reality (VR) enhanced simulation platform; and 3) 5G communication for connected AVs. This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

- by IEEE/CAA J. Autom. Sinica
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- Artificial Intelligence, Perception, Motion Planning, Deep Learning

The purpose of Motion planning in Robot Manipulators is to enable the robot to move from one location to the other without colliding with any obstacles in its neighbourhood.
In order to perform motion planning successfully, one needs to find solutions to
the following modules: Forward kinematics, Inverse Kinematics, Workspace to Configuration space transformation, and path planning. In this thesis, we try to find AI (Artificial Intelligence) based solutions, wherever possible to solve these modules.

- by RACHIT SAPRA
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- Artificial Intelligence, Motion Planning, Particle filters, Robot Path Planning
- by Moshe Mann and +1
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- Mechanical Engineering, Vehicle Dynamics, Motion Planning, Mobile Robots
- by Francesco Chinello
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- Motion control, Motion Planning, User interfaces, Inverse Kinematics