Topic
Motion planning
About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.
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TL;DR: This paper develops a new algorithm based on Bacterial Foraging Optimization (BFO) technique which finds a path towards the target and avoiding the obstacles using particles which are randomly distributed on a circle around a robot.
151 citations
TL;DR: The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.
Abstract: A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.
151 citations
TL;DR: In the proposed method, a chaos-based Logistic map is firstly adopted to improve the particle initial distribution and a mutation strategy that undesired particles are replaced by those desired ones is proposed and the algorithm convergence speed is accelerated.
Abstract: Automatic generation of optimized flyable path is a key technology and challenge for autonomous unmanned aerial vehicle (UAV) formation system. Aiming to improve the rapidity and optimality of automatic path planner, this paper presents a three dimensional path planning algorithm for UAV formation based on comprehensively improved particle swarm optimization (PSO). In the proposed method, a chaos-based Logistic map is firstly adopted to improve the particle initial distribution. Then, the common used constant acceleration coefficients and maximum velocity are designed to adaptive linear-varying ones, which adjusts to the optimization process and meanwhile improves solution optimality. Besides, a mutation strategy that undesired particles are replaced by those desired ones is also proposed and the algorithm convergence speed is accelerated. Theoretically, the comprehensively improved PSO not only speeds up the convergence but also improves the solution optimality. Finally, Monte-Carlo simulation for UAV formation under terrain and threat constraints are carried out and the results illustrate the rapidity and optimality of the proposed method.
151 citations
10 Dec 2002
TL;DR: A new approach is presented that integrates path planning with sensor-based collision avoidance that simultaneously considers the robot's pose and velocities during the planning process and can reliably control mobile robots moving at high speeds.
Abstract: Whenever robots are installed in populated environments, they need appropriate techniques to avoid collisions with unexpected obstacles. Over the past years several reactive techniques have been developed that use heuristic evaluation functions to choose appropriate actions whenever a robot encounters an unforeseen obstacle. Whereas the majority of these approaches determines only the next steering command, some additionally consider sequences of possible poses. However, they generally do not consider sequences of actions in the velocity space. Accordingly, these methods are not able to slow down the robot early enough before it has to enter a narrow passage. In this paper we present a new approach that integrates path planning with sensor-based collision avoidance. Our algorithm simultaneously considers the robot's pose and velocities during the planning process. We employ different strategies to deal with the huge state space that has to be explored. Our method has been implemented and tested on real robots and in simulation runs. Extensive experiments demonstrate that our technique can reliably control mobile robots moving at high speeds.
150 citations
01 Oct 1987
TL;DR: A controller for redundant manipulators with a small fast manipulator mounted on a positioning part has been developed and it was shown that a high bandwidth was possible with moderate actuator torques.
Abstract: A controller for redundant manipulators with a small fast manipulator mounted on a positioning part has been developed The controller distributes the fast motion to the small fast manipulator and the slow gross motion to the positioning part A position reference is generated on-line to the positioning part to avoid singularities and the loss of degrees of freedom The task-space position vector is augmented by the generalized coordinates of the positioning part Feedback linearization and decoupling are then applied in the augmented task space to obtain a model consisting of decoupled double integrators These decoupled double integrators are controlled by the use of linear quadratic optimal control In the optimal control problem the performance index is chosen so that the task-space position reference is tracked with a high bandwidth while the reference to the positioning part is tracked with a low bandwidth The controller has been applied to a simple planar redundant manipulator and an eight-link spray painting robot in simulation experiments These simulations showed that a high bandwidth was possible with moderate actuator torques
150 citations