Motion planning

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

Towards Reliable Grasping and Manipulation in Household Environments

Abstract: We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS).

A cooperative search framework for distributed agents

Abstract: This paper presents an approach for cooperative search of a team of distributed agents. We consider two or more agents, or vehicles, moving in a geographic environment, searching for targets of interest and avoiding obstacles or threats. The moving agents are equipped with sensors to view a limited region of the environment they are visiting, and are able to communicate with one another to enable cooperation. The agents are assumed to have some "physical" limitations including possibly maneuverability limitations, fuel/time constraints and sensor range and accuracy. The developed cooperative search framework is based on two inter-dependent tasks: (1) online learning of the environment and storing of the information in the form of a "search map"; and (2) utilization of the search map and other information to compute online a guidance trajectory for the agent to follow. The distributed learning and planning approach for cooperative search is illustrated by computer simulations.

How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction

Abstract: Reliable prediction of surround vehicle motion is a critical requirement for path planning for autonomous vehicles. In this paper, we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and intervehicle interaction. We report our results in terms of maneuver classification accuracy and mean and median absolute error of predicted trajectories against the ground truth for real traffic data collected using vehicle mounted sensors on freeways. An ablative analysis is performed to analyze the relative importance of each cue for trajectory prediction. Additionally, an analysis of execution time for the components of the framework is presented. Finally, we present multiple case studies analyzing the outputs of our model for complex traffic scenarios.

Receding horizon path planning with implicit safety guarantees

Abstract: This paper extends a recently developed approach to optimal path planning of autonomous vehicles, based on mixed integer linear programming (MILP), to account for safety. We consider the case of a single vehicle navigating through a cluttered environment which is only known within a certain detection radius around the vehicle. A receding horizon strategy is presented with hard terminal constraints that guarantee feasibility of the MILP problem at all future time steps. The trajectory computed at each iteration is constrained to end in a so called basis state, in which the vehicle can safely remain for an indefinite period of time. The principle is applied to the case of a UAV with limited turn rate and minimum speed requirements, for which safety conditions are derived in the form of loiter circles. The latter need not be known ahead of time and are implicitly computed online. An example scenario is presented that illustrates the necessity of these safety constraints when the knowledge of the environment is limited and/or hard real-time restrictions are given.

Motion planning for anguilliform locomotion

Abstract: We investigate issues of control and motion planning for a biomimetic robotic system. Previous work has shown that a successful approach to solving the motion planning problem is to decouple it into the two subproblems of trajectory generation (feedforward controls) and feedback regulation. In this paper, we investigate basic issues of momentum generation for a class of dynamic mobile robots, focusing on eel-like swimming robots. We develop theoretical justification for a forward gait that has been observed in nature, and for a turning gait, used in our control laws, that has not been extensively studied in the biological literature. We also explore theoretical predictions for novel gaits for turning and sideways swimming. Finally, we present results from experiments in motion planning for a biomimetic robotic system. We show good agreement with theory for both open and closed-loop control of our modular, five-link, underwater swimming robot using image-based position sensing in an aquatic environment.