Closed-Loop Linear Covariance Framework for Path Planning in Static Uncertain Obstacle Fields

2021 ◽  
pp. 1-15
Author(s):  
Randall S. Christensen ◽  
Greg Droge ◽  
Robert C. Leishman
Author(s):  
Nino Pereira ◽  
A.Fernando Ribeiro ◽  
Gil Lopes ◽  
Jorge Lino

Purpose The purpose of this paper is to characterise the TWIN-RRT* algorithm which solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory and retrieves feasible, “low cost”, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinematic constraints in a 2D environment. Design/methodology/approach It was designed to work equally well with higher degree of freedom agents in different applications. It provides a practical implementation of feasible and fast planning, namely where a closed loop is required. Initial and final configurations are allowed to be exactly the same. Findings The TWIN-RRT* algorithm computes an efficient path for a single agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*. Research limitations/implications It uses efficiency as cost function, which can be adjusted to the requirements of any given application. TWIN-RRT also shows compliance with kinematic constraints. Practical implications The practical application where this work has been used consists of an autonomous mobile robot that picks up golf balls in a driving range. The multiple targets are the golf balls and the optimum path is a requirement to reduce the time and energy to refill as quickly as possible the balls dispensing machine. Originality/value The new random sampling algorithm – TWIN-RRT* – is able to generate feasible efficient paths towards multiple targets retrieving closed-loop paths starting and finishing at the same configuration.


2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668750 ◽  
Author(s):  
Jianjun Luo ◽  
Kai Jin ◽  
Mingming Wang ◽  
Jianping Yuan ◽  
Gefei Li

For atmospheric entry vehicles, guidance design can be accomplished by solving an optimal issue using optimal control theories. However, traditional design methods generally focus on the nominal performance and do not include considerations of the robustness in the design process. This paper proposes a linear covariance-based model predictive control method for robust entry guidance design. Firstly, linear covariance analysis is employed to directly incorporate the robustness into the guidance design. The closed-loop covariance with the feedback updated control command is initially formulated to provide the expected errors of the nominal state variables in the presence of uncertainties. Then, the closed-loop covariance is innovatively used as a component of the cost function to guarantee the robustness to reduce its sensitivity to uncertainties. After that, the models predictive control is used to solve the optimal problem, and the control commands (bank angles) are calculated. Finally, a series of simulations for different missions have been completed to demonstrate the high performance in precision and the robustness with respect to initial perturbations as well as uncertainties in the entry process. The 3σ confidence region results in the presence of uncertainties which show that the robustness of the guidance has been improved, and the errors of the state variables are decreased by approximately 35%.


2019 ◽  
Vol 83 ◽  
pp. 48-64 ◽  
Author(s):  
Ehsan Taheri ◽  
Mohammad Hossein Ferdowsi ◽  
Mohammad Danesh

Robotica ◽  
2018 ◽  
Vol 36 (12) ◽  
pp. 1857-1873
Author(s):  
Benyan Huo ◽  
Xingang Zhao ◽  
Jianda Han ◽  
Weiliang Xu

SUMMARYBevel-tip needles have the potential to improve paracentetic precision and decrease paracentetic traumas. In order to drive bevel-tip needles precisely with the constrains of path length and path dangerousness, we propose a closed-loop control method that only requires the position of the needle tip and can be easily applied in a clinical setting. The control method is based on the path planning method proposed in this paper. To establish the closed-loop control method, a kinematic model of bevel-tip needles is first presented, and the relationship between the puncture path and controlled variables is established. Second, we transform the path planning method into a multi-objective optimization problem, which takes the path error, path length and path dangerousness into account. Multi-objective particle swarm optimization is employed to solve the optimization problem. Then, a control method based on path planning is presented. The current needle tip attitude is essential to plan an insertion path. We analyze two methods to obtain the tip attitude and compare their effects using both simulations and experiments. In the end, simulations and experiments in phantom tissue are executed and analyzed, the results show that our methods have high accuracy and have the ability to deal with the model parameter uncertainty.


2018 ◽  
Vol 41 (10) ◽  
pp. 2133-2143 ◽  
Author(s):  
Randall S. Christensen ◽  
David K. Geller

Author(s):  
Laurent Sabourin ◽  
Kévin Subrin ◽  
Richard Cousturier ◽  
Grigoré Gogu ◽  
Youcef Mezouar

Purpose – The robot offers interesting capabilities, but suffers from a lack of stiffness. The proposed solution is to introduce redundancies for the overall improvement of different capabilities. The management of redundancy associated with the definition of a set of kinematic, mechanical and stiffness criteria enables path planning to be optimized. Design/methodology/approach – The resolution method is based on the projection onto the kernel of the Jacobian matrix of the gradient of an objective function constructed by aggregating kinematic, mechanical and stiffness weighted criteria. Optimized redundancy management is applied to the 11-DoF (degrees of freedom) cells to provide an efficient placement of turntable and track. The final part presents the improvement of the various criteria applied to both 9-DoF and 11-DoF robotic cells. Findings – The first application concerns the optimized placement of a turntable and a linear track using 11-DoF architecture. Improved criteria for two 9-DoF robotic cells, a robot with parallelogram closed loop and a Tricept are also presented. Simulation results present the contributions of redundancies and the leading role of the track. Research limitations/implications – The redundancy-based optimization presented and the associated simulation approach must be completed by the experimental determination of the optimization criteria to take into account each machining strategy. Practical implications – This work in robotics machining relates to milling operations for automotive and aerospace equipment. The study is carried out within the framework of the RobotEx Equipment of Excellence programme. Originality/value – The resolution method to optimized path planning is applied to 9- and 11-DoF robotic cells, including a hybrid robot with a parallelogram closed loop and a Tricept PKM.


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