3D local trajectory planner

Sąsiadek, Jurek Z.; Dulȩba, Ignacy

doi:10.2514/6.1997-3555

Cited by 21 publications

(19 citation statements)

References 9 publications

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“…An advanced knowledge of the terrain surface could allow for fully 3D navigation and control (Sasiadek and Duleba, 2000). Since the vertical forces between the wheels and the ground stay in positive area, no loss of contact occurs (see figures 10, 16 and 17).…”

Section: Resultsmentioning

confidence: 97%

Autonomous mobile robot model predictive control

Kim¹,

Necsulescu²,

Sąsiadek³

2004

International Journal of Control

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This paper presents model predictive control of an autonomous vehicle. Simulation and experimental results have been shown and compared with input-output linearization method. The results obtained show that the MPC is an efficient method that allows for accurate control and navigation of an autonomous vehicle. Model based predictive control is tested in simulations for motion on an inclined plane. This is done to prepare future work regarding the avoidance of the violation of the smoothness condition for exact linearization, while at the same time by modifying the input commands the geometric path planning results are conserved. The approach is presented for the wheel-ground slippage and tip-over avoidance of the three-wheeled vehicle for inclined plane motion. A complete three-dimensional dynamic model using Newtonian dynamics is also presented. Simulation results using a three-wheeled vehicle built in our laboratory illustrate the performance of the proposed controller.

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Section: Resultsmentioning

confidence: 97%

Autonomous mobile robot model predictive control

Kim¹,

Necsulescu²,

Sąsiadek³

2004

International Journal of Control

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“…Security margin (Dist safe ) is calculated by taking into account the speed of production tool in the scene by using the following formula (2) [28]:…”

Section: V-trust Algorithmmentioning

confidence: 99%

Generation of safe tool-path for 2.5D milling/drilling machine-tool using 3D ToF sensor

Plapper

2015

CIRP Journal of Manufacturing Science and Technology

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“…As in the case of Ravi et al (2004), the algorithm produces feasible paths after a small number of generations (less than 10, much less than in Ravi et al 2004). Sasiadeck and Duleba (2000) suggest a 3D heuristic-based planner for local trajectory planning in a partially known environment with moving obstacles and a predefined global path. The route planners discussed make use of continuous space.…”

Section: Review Of the Literature And Problem Statementmentioning

confidence: 99%

An Automated Network Generation Procedure for Routing of Unmanned Aerial Vehicles (UAVs) in a GIS Environment

Casas¹,

Malik

Delmelle

et al. 2006

Netw Spat Econ

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In this paper, the problem of creating a network to facilitate planning of an air reconnaissance mission is investigated. A comprehensive review of the literature shows that limited work has been done on the design of such networks in a hostile environment and that little research has accounted for terrain information. Ideally, in any routing problem a sample network is used as reference to determine paths to achieve a specified goal. The objective of this paper is to propose an automated generalized procedure developed within a Geographical Information System environment, to identify the nodes and links of a network based on the topographic information of the enemy terrain. This information is made available in the form of a Triangulated Irregular Network (TIN). Inputs to the network generation process include (I) a user-specified threshold altitude value to avoid radar detection and terrain collisions, (II) a specification of the node density in the study area and (III) the minimum/maximum lengths for the links in the desired network. The generated networks are evaluated based on traditional network analysis measures and on the area covered based on a visibility analysis. The methodology is particularly useful for air mission of Unmanned Aerial Vehicles (UAVs) and robot motion planning. As an example, a case study using elevation data from the state of Colorado is presented. Copyright Springer Science+Business Media, LLC 2007Unmanned aerial vehicles, Network generation, Geographical Information Systems (GIS), Triangulated Irregular Network (TIN), Detection altitude,

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3D local trajectory planner

Cited by 21 publications

References 9 publications

Autonomous mobile robot model predictive control

Autonomous mobile robot model predictive control

Generation of safe tool-path for 2.5D milling/drilling machine-tool using 3D ToF sensor

An Automated Network Generation Procedure for Routing of Unmanned Aerial Vehicles (UAVs) in a GIS Environment

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