Path Planning for Planetary Exploration

Rekleitis, Ioannis; Bedwani, Jean-Luc; Dupuis, Érick; Allard, Pierre

doi:10.1109/crv.2008.46

Cited by 18 publications

(6 citation statements)

References 24 publications

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“…[7] Future autonomous rovers call for a localization capability on board. [10] Various types of sensors can be put on the rover to get critical data, which can be classified into two major categories. The first category represents the proprioceptive sensors that provide "relative" position from the starting point, such as the heading sensor and wheel encoder used on Sojourner rover to estimate changes in position.…”

Section: Multi-rover Localizationmentioning

confidence: 99%

Multi-rover collaboration

Skelton

Pang

2009

IEEE Southeastcon 2009

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This paper discusses the ongoing research being conducted on the use of multi-rover teams for planetary exploration.The value of a multi-rover system is presented, along with the challenges faced with trying to coordinate multiple rovers in a consolidated effort to complete a given mission.The paper examines the issues affecting planetary exploration and communication and presents the case for employing a set of rovers in order to address those concerns. In addressing the given weaknesses of a earth controlled single rover, a number of additional obstacles arise.Those obstacles are discussed along with recommended means for overcoming each.The paper discusses the advantages and concerns related to the deployment and control of a set of autonomous rovers, A system architecture is illustrated and related simulations are recommended for testing the suggested solutions to each of the concerns..

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Section: Multi-rover Localizationmentioning

confidence: 99%

Multi-rover collaboration

Skelton

Pang

2009

IEEE Southeastcon 2009

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“…Over the past decade, mobile robots have been effectively adapted to carry out vital unmanned tasks in various fields. Application areas of path-planning algorithms include but are not confined to security, vigilance [1], planetary exploration [2], route planning of Unmanned Aerial Vehicle (UAV) [3,4], and molecular simulation [5]. Path-planning for mobile robots deals with feasible path generation from a starting position to a goal position by avoiding collision with obstacles in an environment [6].…”

Section: Introductionmentioning

confidence: 99%

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

et al. 2019

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With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

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“…As noted earlier, a path was always found if a feasible path, for a given cost function, existed. For an in-depth discussion of the CSA's path-planning approach, including the implementation of different cost functions, please refer to [21].…”

Section: Overviewmentioning

confidence: 99%

Autonomous planetary exploration using LIDAR data

Rekleitis

Bedwani

Dupuis

2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-In this paper we present the approach for autonomous planetary exploration developed at the Canadian Space Agency. The goal of this work is to autonomously navigate to remote locations, well beyond the sensing horizon of the rover, with minimal interaction with a human operator. We employ LIDAR range sensors due to their accuracy, long range and robustness in the harsh lighting conditions of space. Irregular Triangular Meshes (ITMs) are used for representing the environment providing an accurate yet compact spatial representation. In this paper a novel path-planning technique through the ITM is introduced, which guides the rover through flatter terrain and safely away from obstacles. Experiments performed in CSA's Mars emulation terrain that validate our approach are also presented. I. INTRODUCTIONMobile robotics has enabled scientific breakthroughs in planetary exploration [1]. Recent accomplishments have demonstrated beyond doubt the necessity and feasibility of semi-autonomous rovers for conducting scientific exploration on other planets. Both Mars Exploration Rovers (MERs) "Spirit" and "Opportunity" have the ability to detect and avoid obstacles, picking a path that would take them along a safe trajectory. MER's have reached traverses of 300m/sol. On occasion, the rovers have had to travel to locations that were at the fringe of the horizon of their sensors or even slightly beyond.The next rover missions to Mars are the "Mars Science Laboratory" (MSL) [2] and ESA's ExoMars [3]. Both of these missions have set target traverse distances on the order of one kilometer per day. Both the MSL and ExoMars rovers are therefore expected to drive regularly a significant distance beyond the horizon of their environment sensors. Earthbased operators will therefore not know a-priori the detailed geometry of the environment and will thus not be able to select via-points for the rovers throughout their traverses.One of the key technologies that will be required is the ability to sense and model the 3D environment in which the rover has to navigate. To address the above mentioned issues, the Canadian Space Agency is developing a suite of technologies for long-range rover navigation. For the purposes of this paper, "long-range" is defined as a traverse that takes the rover beyond the horizon of the rover's environment sensors.In the next Section we discuss the state-of-the-art in robotic planetary exploration. Section II presents the overall process for planetary exploration together with a short description of our test-bed. Next we present a summary of our approach to environmental modelling, Section IV

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Path Planning for Planetary Exploration

Cited by 18 publications

References 24 publications

Multi-rover collaboration

Multi-rover collaboration

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

Autonomous planetary exploration using LIDAR data

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Path Planning for Planetary Exploration

Cited by 18 publications

References 24 publications

Multi-rover collaboration

Multi-rover collaboration

A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

Autonomous planetary exploration using LIDAR data

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Product

Resources

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A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment