Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

A unified motion planning method for parking an autonomous vehicle in the presence of irregularly placed obstacles

Abstract: This paper proposes a motion planner for autonomous parking. Compared to the prevailing and emerging studies that handle specific or regular parking scenarios only, our method describes various kinds of parking cases in a unified way regardless they are regular parking scenarios (e.g., parallel, perpendicular or echelon parking cases) or not. First, we formulate a time-optimal dynamic optimization problem with vehicle kinematics, collision-avoidance conditions and mechanical constraints strictly described. Thereafter, an interior-point simultaneous approach is introduced to solve that formulated dynamic optimization problem. Simulation results validate that our proposed motion planning method can tackle general parking scenarios. The tested parking scenarios in this paper can be regarded as benchmark cases to evaluate the efficiency of methods that may emerge in the future. Our established dynamic optimization problem is an open and unified framework, where other complicated user-specific constraints/optimization criteria can be handled without additional difficulty, provided that they are expressed through inequalities/polynomial explicitly. This proposed motion planner may be suitable for the next-generation intelligent parking-garage system.

Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs

Abstract: With the increased use of high degree-of-freedom robots that must perform tasks in real-time, there is a need for fast algorithms for motion planning. In this work, we view motion planning from a probabilistic perspective. We consider smooth continuous-time trajectories as samples from a Gaussian process (GP) and formulate the planning problem as probabilistic inference. We use factor graphs and numerical optimization to perform inference quickly, and we show how GP interpolation can further increase the speed of the algorithm. Our framework also allows us to incrementally update the solution of the planning problem to contend with changing conditions. We benchmark our algorithm against several recent trajectory optimization algorithms on planning problems in multiple environments. Our evaluation reveals that our approach is several times faster than previous algorithms while retaining robustness. Finally, we demonstrate the incremental version of our algorithm on replanning problems, and show that it often can find successful solutions in a fraction of the time required to replan from scratch.

G2-Type SRMPC Scheme for Synchronous Manipulation of Two Redundant Robot Arms

Abstract: In this paper, to remedy the joint-angle drift phenomenon for manipulation of two redundant robot arms, a novel scheme for simultaneous repetitive motion planning and control (SRMPC) at the joint-acceleration level is proposed, which consists of two subschemes. To do so, the performance index of each SRMPC subscheme is derived and designed by employing the gradient dynamics twice, of which a convergence theorem and its proof are presented. In addition, for improving the accuracy of the motion planning and control, position error, and velocity, error feedbacks are incorporated into the forward kinematics equation and analyzed via Zhang neural-dynamics method. Then the two subschemes are simultaneously reformulated as two quadratic programs (QPs), which are finally unified into one QP problem. Furthermore, a piecewise-linear projection equation-based neural network (PLPENN) is used to solve the unified QP problem, which can handle the strictly convex QP problem in an inverse-free manner. More importantly, via such a unified QP formulation and the corresponding PLPENN solver, the synchronism of two redundant robot arms is guaranteed. Finally, two given tasks are fulfilled by 2 three-link and 2 five-link planar robot arms, respectively. Computer-simulation results validate the efficacy and accuracy of the SRMPC scheme and the corresponding PLPENN solver for synchronous manipulation of two redundant robot arms.

Localization and follow-the-leader control of a heterogeneous group of mobile robots

Abstract: This paper investigates the control and localization of a heterogeneous (e.g., different sensing, mechanical, computational capabilities) group of mobile robots. The group considered here has several inexpensive sensor-limited and computationally limited robots, which follow a leader robot in a desired formation over long distances. This situation is similar to a search, demining, or planetary exploration situation where there are several deployable/disposable robots led by a more sophisticated leader. Specifically, the robots in this paper are designed for highway safety applications where they automatically deploy and maneuver safety barrels commonly used to control traffic in highway work zones. Complex sensing and computation are performed by the leader, while the followers perform simple operations under the leader's guidance. This architecture allows followers to be simple, inexpensive, and have minimal sensors. Theoretical and statistical analysis of a tracking-based localization method is provided. A simple follow-the-leader control method is also presented, including a method for changing follower's configuration. Experimental results of localization and follow-the-leader formation-motion are included.

Perception-less terrain adaptation through whole body control and hierarchical optimization

Abstract: This paper presents a control approach based on a whole body control framework combined with hierarchical optimization. Locomotion is formulated as multiple tasks (e.g. maintaining balance or tracking a desired motion of one of the limbs) which are solved in a prioritized way using QP solvers. It is shown how complex locomotion behaviors can purely emerge from robot-specific inequality tasks (i.e. torque or reaching limits) together with the optimization of balance and system manipulability. Without any specific motion planning, this prioritized task optimization leads to a natural adaption of the robot to the terrain while walking and hence enables blind locomotion over rough grounds. The presented framework is implemented and successfully tested on ANYmal, a torque controllable quadrupedal robot. It enables the machine to walk while accounting for slippage and torque limitation constraints, and even step down from an unperceived 14 cm obstacle. Thereby, ANYmal exploits the maximum reach of the limbs and automatically adapts the body posture and height.