Accurate and stable mobile robot path tracking: An integrated solution for off-road and high speed context

Lenain, Roland; Lucet, Éric; Grand, Christophe; Thuilot, Benoı̂t; Amar, Faïz Ben

doi:10.1109/iros.2010.5650213

Cited by 8 publications

(11 citation statements)

References 15 publications

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“…The given computations to obtain the replanning region is based on the position of the robot at the time of obstacle detection in the workspace. To address pose errors of the robot during navigation, the pose of the robot is checked against the computed path using path tracking algorithm presented in [34]. This check ensures that the angle of the robot, θ r, and that of the path, θ p, are the same.…”

Section: B Computation Of the Replanning Areamentioning

confidence: 99%

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

et al. 2019

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This paper presents a novel path planning method for mobile robots in complex and dynamic environments using Voronoi diagram (VD) and computation geometry technique (CGT) termed, VD-CGT. An algorithm to categorize moving obstacles based on their positions, velocities, distances, and directions to ascertain their collision threat level and possible replanning decision is introduced. The initial path computation is done using morphological dilation, VD, A-star, and cubic spline algorithms. Instead of considering the entire map of the environment, CGT is used to compute a small rectangular region estimated to enclose a detected collision-threat obstacle and the current position of the robot. The roadmap is computed in the geometrical shape using VD and nodes are added to the initial roadmap nodes to compute a new path for replanning. To avoid increasing time and space requirements, these nodes are discarded before subsequent replanning is done. The results indicate better path replanning performance in complex and dynamic environments in terms of success path computation rate, path cost, time, and the number of replanning computations compared with other five popular related path planning approaches. The proposed method is efficient, and it computes safe and shortest replan path to goal with low computation time requirement. Unnecessary replanning computations are avoided which aid in reducing time and distance to get to the goal. With the performance results, the proposed method is a promising method for achieving safe, less path cost, and time in path replanning computations in complex and dynamic environments.INDEX TERMS Mobile robot, motion planning, path planning, unmanned autonomous vehicles, Voronoi diagram.

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Section: B Computation Of the Replanning Areamentioning

confidence: 99%

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

et al. 2019

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“…Some work on aggressive driving with car-like vehicles based on model predictive control (MPC) is presented in Williams et al (2016) and Williams et al (2017). Off-road control of car-like vehicles at high speeds using observer-based control can be found in Lenain et al (2011), Lenain et al (2010), and Deremetz et al (2017). Similar work on double-steering vehicles can be found in Lucet et al (2009).…”

Section: Introductionmentioning

confidence: 99%

High-speed path following control of skid-steered vehicles

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et al. 2019

The International Journal of Robotics Research

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We present a robust control scheme for skid-steered vehicles that enables high-speed path following on challenging terrains. First, a kinematic model with experimentally identified parameters is constructed to describe the terrain-dependent motion of skid-steered vehicles. Using Lyapunov theory, a nonlinear control law is defined, guaranteeing the convergence of the vehicle to the path. To allow smooth and accurate motion at higher speeds, an additional linear velocity control scheme is proposed, which takes actuator saturation, path following error, and reachable curvatures into account. The combined solution is experimentally evaluated and compared against two state-of-the-art algorithms, by using two different robots on several different terrain types, at different speeds. A Robotnik Summit XL robot is tested on three different terrain types and two different paths at speeds up to [Formula: see text] m/s. A Segway RMP 440 robot is tested on three different terrain types and two different path types at speeds up to [Formula: see text] m/s.

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“…However, it does not permit us to achieve a highly accurate path tracking when used alone. Therefore, a global controller, based on research lines presented above, has been designed and successfully implemented in Lenain, Lucet, Grand, Benoit Thuilot, and Ben Amar (2010), showing the feasibility of this approach. Based on a simple dynamic model, it was dedicated to vehicles with only one steering axle.…”

Section: Proposed Control Strategy and Previous Workmentioning

confidence: 99%

“…Thus, in this paper a new control architecture based on previous contributions (Cariou, Lenain, Thuilot, & Berducat, 2009;Lenain, Lucet, et al, 2010;Lucet et al, 2008) is presented by considering the independent control of the four wheel velocities and the use of a double-axle steering system. This control architecture can be divided into two cascaded sub-systems (see Fig.…”

Section: Proposed Control Strategy and Previous Workmentioning

confidence: 99%

“…In Section 2, major extensions of previous work (Cariou et al, 2009;Lenain, Lucet, et al, 2010; to a double Ackermann steering system are investigated, while a new backstepping observer of the sideslip angles is proposed with the following contributions: (i) the lateral slope in equations is now considered and (ii) a new adaptation algorithm for grip conditions estimation has been developed avoiding singularities. This allows further investigation of the independent wheel control by considering a simplified dynamical model adapted to real-time conditions, ensuring the relevance of this approach when the grip conditions are varying and non-measurable.…”

Section: Contributionsmentioning

confidence: 99%

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