Optimal trajectory planning based on bidirectional spline-RRT* for wheeled mobile robot

Sudhakara, Priyanka; Ganapathy, Velappa; Karthika, S.

doi:10.1109/ssps.2017.8071566

Cited by 10 publications

(7 citation statements)

References 9 publications

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“…where y is the output matrix tracking, y des is the intended lateral position, v xdes is the desired speed, l des is the index number of the desired lane from the right, L w is the lane width, and ∆Y R is the lateral offset of the road relative to a straight road. The path planning nonlinear optimization problem can be expressed in the form: (10) u min < utþ i À 1jt ðÞ < u max (11) ∆u min < utþ i À 1jt ðÞ À utþ i À 2jt ðÞ < ∆u max (12) where (t + i|t) index indicate the values at future time t+iand predicted at current time t. N is the prediction horizon. The vector of slack variables at time t is denoted by s i .The tracking quadratic term, changes in inputs, inputs, potential field functions, and slack variables compose the objective function.…”

Section: Mpc Frameworkmentioning

confidence: 99%

“…(10). To satisfy the limitations of actuator, the inputs of control and their changes are constrained in (11) and (12) where u min and u max are the lower and upper bounds matrices of control input, and ∆u min and ∆u max are the lower and upper bounds matrices of the control inputs changes.…”

Section: Mpc Frameworkmentioning

confidence: 99%

“…The result of the simulation has shown that the proposed method performs better than the traditional one. In [12], the authors used a combination of RRT* and Spline techniques to generate a smooth path. The proposed bidirectional Spline-RRT* algorithm is based on the cubic curve and satisfies direction constraints for both start and target positions.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Vehicle Path Planning Using MPC and APF

Elmi¹,

Elmi²

2022

Motion Planning

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Autonomous vehicles have been at the forefront of academic and industrial research in recent decades. This study’s aim is to reduce traffic congestion, improve safety, and accidents. Path planning algorithms are one of the main elements in autonomous vehicles that make critical decisions. Motion planning methods are required when transporting passengers from one point to another. These methods have incorporated several methods such as generating the best trajectory while considering the constraints of vehicle dynamics and obstacles, searching a path to follow, and avoiding obstacles that guarantee comfort, safety, and efficiency. We suggested an effective path planning algorithm based on Model Predictive Controller that determines the maneuvers mode such as lane-keeping and lane-changing automatically. We utilized two different artificial potential field functions for the road boundary, obstacles, and lane center to ensure safety. On the four scenarios, we examined the proposed path planning controller. The obtained results show that when a path planning controller is used, the vehicle avoids colliding with obstacles and follows the rules of the road by adjusting the vehicle’s dynamics. An autonomous vehicle’s safety is ensured by the path planning controller.

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Section: Mpc Frameworkmentioning

confidence: 99%

Section: Mpc Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autonomous Vehicle Path Planning Using MPC and APF

Elmi¹,

Elmi²

2022

Motion Planning

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“…As opposed to exhaustively creating a full-cost function, the idea of this technique is to firstly sample random points regardless of interfering obstacles, then connect only collision free points to construct a map. This technique is typically used for a robotic manipulator [122][123][124][125][126][127], however is still sometimes used for a mobile vehicle [128][129][130][131][132][133][134]. The problem with this technique is that post-processing to smooth the path is required in order for the path to be realistically traversable for the vehicle [135][136][137].…”

Section: Sampling-based Path Planningmentioning

confidence: 99%

Path planning for autonomous bulldozers

et al. 2019

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With increasing demand and awareness from industries for enhanced safety, higher productivity, and better vehicle utilization, coupled with labour shortage, automating heavy equipment is becoming more and more important. Among the different types of heavy equipment, bulldozers fall into the category of machines that have a very high potential of achieving fully autonomous operation. Focusing on this fact, the aim of this research is to develop an automated path planning system for bulldozers. In general, an autonomous system is composed of terrain mapping, path planning and path-tracking. Existing commercial grade methodologies already solve terrain mapping and path-tracking, however path planning will be a key challenge for bulldozer automation.Conventional path planning algorithms are designed typically using a grid-based map, and are very versatile. However, in reality, a substantial effort is still required when applying to specific industry products. In contrast to this trend, the aim of this work is to develop a path planning algorithm specifically suitable for bulldozers, starting from the phase of theoretical development. The novel path planning methodology was developed by incorporating industry feedback, and has successfully resolved the issues which occurred when attempting to apply the pre-existing path planning methodologies. Results obtained from this newly developed methodology will demonstrate that efficient paths can be created. The methodology presented in this thesis has led to the development of automated path planning for bulldozers. This allows bulldozers to function without the need of an operator, contributing to enhanced safety, increased productivity, and solving the shortage of skilled operators.Declaration relating to disposition of project thesis/dissertation I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only). …………………………………………………………… Signature ……………………………………..……………… Witness Signature ……….……………………...…….… DateThe University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research.

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“…Differ from search methods, RRT (Rapidly-Exploring Random Tree) [13] represents the sampling-based method [14] that is extended to obtain nodes in continuous feature space. In order to improve the real-time performance, Bi-RRT (Bidirectional RRT) is derived as bidirectional expansion from the start node and target node [15] and H-RRT (Heuristic RRT) employs heuristic function to extend low cost nodes [16]. However, the sample-based algorithm always contains a random seed generator, so it inevitably contains zigzags [12] leading to the planning results should be postprocessed for smoothing [17].…”

Section: Introductionmentioning

confidence: 99%

$DBO$ Trajectory Planning and $HAHP$ Decision-Making for Autonomous Vehicle Driving on Urban Environment

Zeng

Liu

et al. 2019

IEEE Access

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A novel driving behaviour oriented (DBO) trajectory planner and hierarchical analytic hierarchy process (HAHP) decision maker are presented for intelligent vehicle. Since driving on structural road should satisfy actuator constraints and improve comfortableness as soon as possible, which strictly obeys traffic rules other than making traffic mess, it is rather than purely pursuing the shortest route/time. By analysis traffic rules, theDBO framework is employed to produce trajectories. To make trajectory drivable, cubic B-spline and clothoid curve are modeled to keep continuous curvature, and cubic polynomial curve is to schedule velocity profile satisfying stability and comfort. To pick out the best trajectory, HAHP decision maker is developed to evaluate the candidates. The first layer selects optimal paths considering smoothness and economy, and the second layer selects best trajectory taking smoothness, comfortableness and economy in account. Moreover, DBO rapidly exploring random tree (RRT) replanner is embedded to ensure algorithm completeness. Finally, several typical scenarios are designed to verify the real-time and reliability of the algorithm. The results illustrate that the algorithm has highly real-time and stability evaluated by Statistical Process Control method as the probability for the peak time less than 0.1s is 100% except three obstacles avoidance scenario is 59.31% in 1000 cycles. Since the planned trajectory is smooth enough and satisfy the constraints of the actuator, the mean lateral tracking error is less than 0.2m with 0.5m peak error, and the mean speed error less than 0.5km/h with 1.5km/h peak error for all scenarios.

show abstract

Optimal trajectory planning based on bidirectional spline-RRT* for wheeled mobile robot

Cited by 10 publications

References 9 publications

Autonomous Vehicle Path Planning Using MPC and APF

Autonomous Vehicle Path Planning Using MPC and APF

Path planning for autonomous bulldozers

$DBO$ Trajectory Planning and $HAHP$ Decision-Making for Autonomous Vehicle Driving on Urban Environment

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