Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains

Jun, Jae-Yun; Saut, Jean-Philippe; Benamar, Faiz

doi:10.1016/j.robot.2015.09.014

Cited by 20 publications

(14 citation statements)

References 44 publications

(96 reference statements)

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“…So, a set Θ * P ⊆ Θ P is -stop only if ∀θ 1 , θ 2 ∈ Θ * P , θ 1 = θ 2 , box(θ 1 ) = box(θ 2 ) and box(θ 1 ) ⊀ box(θ 2 ). (9) Thus, a Θ * P is reached with the greatest possible number of solutions that adequately characterize Pareto's front and whose number of possible solutions depend on n_box i , and will not exceed the following level.…”

Section: Multiobjetive Optimizationmentioning

confidence: 99%

“…Autonomous vehicles with non-holonomic characteristics [6][7][8] can be technologically adapted to different environments, and so, produce unmanned ground vehicles (UGVs) [9][10][11], unmanned underwater vehicles (UUVs) [12][13][14], and unmanned aerial vehicles (UAVs) [15][16][17][18]. Obviously, each of the above categories presents its own scientific and technological challenges, including path planning, navigation, and guidance.…”

Section: Introductionmentioning

confidence: 99%

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Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

et al. 2019

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Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.

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Section: Multiobjetive Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

et al. 2019

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“…The terrain settling technique is used in the current ground operation of Mars rovers to check safety before sending mobility commands (Wright et al, ; Yen, Cooper, Hartman, Maxwell, & Wright, ). The kinematic settling is also effective for other types of vehicles, such as tracked vehicles (Jun, Saut, & Benamar, ). The kinematics approach is generally faster than dynamics‐based approach, but still computationally demanding for onboard execution on spacecraft computers.…”

Section: Introductionmentioning

confidence: 99%

Fast approximate clearance evaluation for rovers with articulated suspension systems

Otsu

Matheron

Ghosh

et al. 2019

Journal of Field Robotics

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We present a light-weight body-terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst-case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments.

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“…The result showed that the method was fast enough for real-time operation. Jae-Yun Jun [25] proposed a novel path-planning algorithm as a tracked mobile robot to traverse uneven terrains, which could efficiently search for stability sub-optimal paths. The method demonstrated that the proposed algorithm could be advantageous over its counterparts in various aspects of the planning performance.…”

Section: Introductionmentioning

confidence: 99%

Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor

Zhang

et al. 2019

Sensors

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Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle’s pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.

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Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains

Cited by 20 publications

References 44 publications

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

Fast approximate clearance evaluation for rovers with articulated suspension systems

Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor

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