A Trajectory Planning Algorithm for Autonomous Overtaking Maneuvers

2013 ◽  
Vol 347-350 ◽  
pp. 3494-3499 ◽  
Author(s):  
Su Min Zhang ◽  
Hao Sun ◽  
Yu Wang
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Dynamic Environment ◽  
Vehicle Model ◽  
Surrounding Environment ◽  
Planning Algorithm ◽  
Planning Algorithms ◽  
Short Time ◽  
Dynamic Trajectory ◽  
Traffic Rules

Autonomous overtaking maneuver is one of the toughest challenges in the field of autonomous vehicles. A key issue of autonomous overtaking maneuver is to find a dynamically feasible trajectory to avoid collision with the overtaken vehicle and surrounding hazards. Traditional trajectory planning algorithms assume that the initial and final vehicle states are given before and generate a trajectory for the whole overtaking process. However, overtaking maneuver is generally a time consuming process. Those assumptions may be invalid in highly dynamic environment. This paper tries to present a dynamic trajectory planning algorithm for autonomous overtaking maneuvers. The whole overtaking maneuver trajectory is made up of several short-time trajectories. Each short-time trajectory is generated by a kinematic vehicle model and taken into account of the surrounding environment and traffic rules. The concept presented in this paper is demonstrated through simulation and the results are discussed.

scholarly journals Application of Polynomial Transition Curves for Trajectory Planning on the Headlands

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 144
Author(s):  
Marek Boryga ◽  
Paweł Kołodziej ◽  
Krzysztof Gołacki
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Displacement Function ◽  
Transition Curve ◽  
The Third ◽  
Runge Kutta Method ◽  
Maximal Acceleration ◽  
Matlab Program ◽  
Planning Algorithm ◽  
Transition Curves

This paper presents a method of polynomial transition curve application for making agricultural aggregate movement paths during headland turn drives as well as within the field. Four types of agricultural aggregate paths in five different variant designs are discussed. Each path is composed of only two curves, making the so-called transition bi-curve. The curvature described by the linear function as well as the third, fifth, seventh, and ninth degree polynomials was designated. Moreover, a trajectory planning algorithm in which the movement proceeds along two transition curves composing the so-called bi-curve was proposed. The simulation was carried out applying the MATLAB program in which the 4th order Runge–Kutta method was used. The results were presented by means of figures showing the proposed paths and kinematic quantity courses in the displacement function. The obtained trajectories were compared regarding the size and kinematic quantities. The trajectories, whose curvature is described by the 3° polynomial, were found to possess the smallest absolute values of maximal acceleration and jerk and to lack jerk discontinuity. The proposed solutions can be applied for planning trajectory of not only agriculture machines and aggregates but also autonomous vehicles or mobile robots.

Leader–follower-based dynamic trajectory planning for multirobot formation

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1351-1359 ◽  
Author(s):  
Shuang Liu ◽  
Dong Sun
Keyword(s):  
Analytical Solution ◽  
Objective Function ◽  
Collision Avoidance ◽  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Optimal Trajectory ◽  
Dynamic Environment ◽  
Current Paper ◽  
New Approach ◽  
Dynamic Trajectory

SUMMARYThe present paper presents a new approach to a leader–follower-based dynamic trajectory planning for multirobot formation. A near-optimal trajectory is generated for each robot in a decentralized manner. The main contributions of the current paper are the proposal of a new objective function that considers both collision avoidance and formation requirement for the trajectory generation, and an analytical solution of trajectory parameters in the trajectory optimization. Simulations and experiments on multirobots are performed to demonstrate the effectiveness of the proposed approach to the multirobot formation in a dynamic environment.

A Bidirectional Quintuple Tree Node Expansion Algorithm for UAV Path Planning in Dynamic Environment

2013 ◽  
Vol 765-767 ◽  
pp. 413-416
Author(s):  
Jian Hong Gong ◽  
Bo Li ◽  
Xiao Guang Gao
Keyword(s):  
Path Planning ◽  
Trajectory Planning ◽  
Dynamic Environment ◽  
Security Issues ◽  
Tree Node ◽  
Search Mechanism ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Optimization Search ◽  
Uav Security

To deal with the problem of penetration trajectory planning for UAV security issues, an improved bidirectional quintuple tree node expansion algorithm is proposed. Compare to traditional quintuple tree node expansion algorithm, the proposed algorithm could reduce the number of the expanded tree node, and it makes the bidirectional quintuple tree node expansion algorithm more efficient in path planning. By combining the bidirectional quintuple tree node expansion algorithm with multi-step optimization search mechanism, a kind of real-time UAV path planning algorithm is presented.

Enable faster and smoother spatio-temporal trajectory planning for autonomous vehicles in constrained dynamic environment

2020 ◽  
pp. 095440702090662
Author(s):  
Long Xin ◽  
Yiting Kong ◽  
Shengbo Eben Li ◽  
Jianyu Chen ◽  
Yang Guan ◽  
...  
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Dynamic Environment ◽  
Graph Search ◽  
A Algorithm ◽  
Planning Time ◽  
Planning Methods ◽  
Spatio Temporal ◽  
Popular Classes ◽  
Temporal Trajectory

Trajectory planning is of vital importance to decision-making for autonomous vehicles. Currently, there are three popular classes of cost-based trajectory planning methods: sampling-based, graph-search-based, and optimization-based. However, each of them has its own shortcomings, for example, high computational expense for sampling-based methods, low resolution for graph-search-based methods, and lack of global awareness for optimization-based methods. It leads to one of the challenges for trajectory planning for autonomous vehicles, which is improving planning efficiency while guaranteeing model feasibility. Therefore, this paper proposes a hybrid planning framework composed of two modules, which preserves the strength of both graph-search-based methods and optimization-based methods, thus enabling faster and smoother spatio-temporal trajectory planning in constrained dynamic environment. The proposed method first constructs spatio-temporal driving space based on directed acyclic graph and efficiently searches a spatio-temporal trajectory using the improved A* algorithm. Then taking the search result as reference, locally convex feasible driving area is designed and model predictive control is applied to further optimize the trajectory with a comprehensive consideration of vehicle kinematics and moving obstacles. Results simulated in four different scenarios all demonstrated feasible trajectories without emergency stop or abrupt steering change, which is kinematic-smooth to follow. Moreover, the average planning time was 31 ms, which only took 59.05%, 18.87%, and 0.69%, respectively, of that consumed by other state-of-the-art trajectory planning methods, namely, maximum interaction defensive policy, sampling-based method with iterative optimizations, and Graph-search-based method with Dynamic Programming.

scholarly journals Control system of 4-DOF palletizing robot based on improved R control multi-objective trajectory planning

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110027
Author(s):  
Jianqiang Wang ◽  
Yanmin Zhang ◽  
Xintong Liu
Keyword(s):  
Control System ◽  
Trajectory Planning ◽  
Robot Control ◽  
Machining Process ◽  
Planning System ◽  
Positioning Accuracy ◽  
Multi Objective ◽  
Planning Algorithm ◽  
Function Testing ◽  
Robot Control System

To realize efficient palletizing robot trajectory planning and ensure ultimate robot control system universality and extensibility, the B-spline trajectory planning algorithm is used to establish a palletizing robot control system and the system is tested and analyzed. Simultaneously, to improve trajectory planning speeds, R control trajectory planning is used. Through improved algorithm design, a trajectory interpolation algorithm is established. The robot control system is based on R-dominated multi-objective trajectory planning. System stack function testing and system accuracy testing are conducted in a production environment. During palletizing function testing, the system’s single-step code packet time is stable at approximately 5.8 s and the average evolutionary algebra for each layer ranges between 32.49 and 45.66, which can save trajectory planning time. During system accuracy testing, the palletizing robot system’s repeated positioning accuracy is tested. The repeated positioning accuracy error is currently 10−1 mm and is mainly caused by friction and the machining process. By studying the control system of a four-degrees-of-freedom (4-DOF) palletizing robot based on the trajectory planning algorithm, the design predictions and effects are realized, thus providing a reference for more efficient future palletizing robot design. Although the working process still has some shortcomings, the research has major practical significance.

Dynamic trajectory planning for unmanned ship under multi-object environment

2021 ◽  
Author(s):  
Mengxia Li ◽  
Junmin Mou ◽  
Yixiong He ◽  
Xiaohan Zhang ◽  
Qinqiong Xie ◽  
...  
Keyword(s):  
Trajectory Planning ◽  
Unmanned Ship ◽  
Dynamic Trajectory

Trajectory planning for coordinately operating robots

1990 ◽  
Vol 4 (3) ◽  
pp. 165-177 ◽  
Author(s):  
Y. P. Chien ◽  
Qing Xue
Keyword(s):  
Trajectory Planning ◽  
Three Dimensional ◽  
Initial Position ◽  
Minimum Time ◽  
Future Research ◽  
Rigid Object ◽  
Redundant Robots ◽  
Planning Algorithm ◽  
The Common ◽  
Joint Velocity

An efficient locally minimum-time trajectory planning algorithm for coordinately operating multiple robots is introduced. The task of the robots is to carry a common rigid object from an initial position to a final position along a given path in three-dimensional workspace in minimum time. The number of robots in the system is arbitrary. In the proposed algorithm, the desired motion of the common object carried by the robots is used as the key to planning of the trajectories of all the non-redundant robots involved. The search method is used in the trajectory planning. The planned robot trajectories satisfy the joint velocity, acceleration and torque constraints as well as the path constraints. The other constraints such as collision-free constraints, can be easily incorporated into the trajectory planning in future research.

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