Regulated pure pursuit for robot path tracking

Macenski, Steve; Singh, Shrijit; Rico, Francisco Martín; Ginés, Jonatan

doi:10.1007/s10514-023-10097-6

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…Figure 7a illustrates that when v = 1 m/s, the tracking effect is ranked as Npre(0) < Npre(1) > Npre(2) > Npre (3), where Npre = 1 indicates the tracking effect s optimality. Figure 7b illustrates that when v = 3 m/s, the tracking effect is ranked as Npre(1) < Npre( 2 From Figure 6 (set N p = 10 and N c = 3), N pre should be increased appropriately but not excessively as the curvature grows.…”

Section: Impact Of Velocity On the Value Of Nprementioning

confidence: 99%

“…Figure 7a illustrates that when v = 1 m/s, the tracking effect is ranked as Np Npre(1) > Npre(2) > Npre (3), where Npre = 1 indicates the tracking effect s optimality. 7b illustrates that when v = 3 m/s, the tracking effect is ranked as Npre(1) < Npre( 2 Take the U-shaped path (the commonly used agricultural machinery operation path) as the reference path and set N p = 10 and N c = 3.…”

Section: Impact Of Velocity On the Value Of Nprementioning

confidence: 99%

“…7b illustrates that when v = 3 m/s, the tracking effect is ranked as Npre(1) < Npre( 2 Take the U-shaped path (the commonly used agricultural machinery operation path) as the reference path and set N p = 10 and N c = 3. Figure 7a illustrates that when v = 1 m/s, the tracking effect is ranked as N pre (0) < N pre (1) > N pre (2) > N pre (3), where N pre = 1 indicates the tracking effect's optimality. Figure 7b illustrates that when v = 3 m/s, the tracking effect is ranked as N pre (1) < N pre (2) > N pre (3) > N pre (4), where N pre = 2 indicates the tracking effect's optimality.…”

Section: Impact Of Velocity On the Value Of Nprementioning

confidence: 99%

“…Path tracking control is at the heart of autonomous driving technology, which is widely used in farming, planting, management, harvesting, transportation, and other aspects of agricultural production to improve work quality and efficiency and to reduce the waste of agricultural production materials [2]. Typical path tracking control methods include geometric model-based PP [3] and Stanley [4], etc., kinematic and dynamic model-based PID [5], LQR [6], SMC [7], and MPC [8], etc., as well as model-free based fuzzy control [9], reinforcement learning [10], and neural networks [11], etc.…”

Section: Introductionmentioning

confidence: 99%

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Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

Wang,

Niu,

Wang

et al. 2024

Agriculture

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To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

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Section: Impact Of Velocity On the Value Of Nprementioning

confidence: 99%

Section: Impact Of Velocity On the Value Of Nprementioning

confidence: 99%

Section: Impact Of Velocity On the Value Of Nprementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

Wang,

Niu,

Wang

et al. 2024

Agriculture

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“…Another recent study proposed steer guidance using pure pursuit algorithm (PPA) which is also assisted by vector field histogram (VFH) based on 2-dimensional light detection and ranging (LiDAR) sensors data for obstacle avoidance [6]. A study has presented experiments industrial-grade service robots with regulated PurePursuit adjusting linear velocities with particular focus on safety in constrained and partially observable indoor spaces [7]. A related study has applied MPC based lookahead distance optimization for pure pursuit tracking control to improve cutting corner problems in simulation and experiments on various roads, including handling course, double lane change, and 90-degree turn [8].…”

Section: Introductionmentioning

confidence: 99%

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Latif,

Lindbäck,

Lideskog

et al. 2024

IEEE Access

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This work presents outdoor test results of autonomous navigation system based on two different reference points of PurePursuit algorithm for a 10-ton articulated research platform. PurePursit is commonly used simplified tracking algorithm based on geometric calculation of desired steering angle by pursuing certain number of points/distance ahead on given path from a fixed reference point of vehicle. Choice of fixed reference point effects the tracking accuracy particularly for articulated/center-steered vehicles. In this experimental work, PurePursuit algorithm with a virtual reference point(PPV) and commonly used front-axle reference point(PPF) is evaluated for heavy duty articulated vehicles in outdoor experiments .Experimental data shows that choice of reference point in PurePursuit algorithm for articulated vehicle has impact on tracking accuracy in terms of crosstrack errors and heading errors. Navigation tests were performed on a flat asphalt surface for paths of sharp complexities i-e a path with continuous curvature (circular path) and a path with sharp turns (zigzag path) with different initial conditions i-e initial position of vehicle. In general, it can be concluded that PurePursuit algorithm with front reference point (PPF) produced fewer crosstrack errors while PurePursuit algorithm with virtual midpoint reference (PPV) produced fewer heading errors.

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Instance Segmentation to Path Planning in a Simulated Industrial Environment

Schneider,

Stemmer

2024

Synergetic Cooperation Between Robots and Humans

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Regulated pure pursuit for robot path tracking

Cited by 14 publications

References 10 publications

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Instance Segmentation to Path Planning in a Simulated Industrial Environment

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