Motion planning

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

A dynamic path planning approach for multi-robot sensor-based coverage considering energy constraints

Abstract: Multirobot sensor-based coverage path planning determines a tour for each robot in a team such that every point in a given workspace is covered by at least one robot using its sensors. In sensor-based coverage of narrow spaces, i.e., obstacles lie within the sensor range, a generalized Voronoi diagram (GVD)-based graph can be used to model the environment. A complete sensor-based coverage path plan for the robot team can be obtained by using the capacitated arc routing problem solution methods on the GVD-based graph. Unlike capacitated arc routing problem, sensor-based coverage problem requires to consider two types of edge demands. Therefore, modified Ulusoy algorithm is used to obtain mobile robot tours by taking into account two different energy consumption cases during sensor-based coverage. However, due to the partially unknown nature of the environment, the robots may encounter obstacles on their tours. This requires a replanning process that considers the remaining energy capacities and the current positions of the robots. In this paper, the modified Ulusoy algorithm is extended to incorporate this dynamic planning problem. A dynamic path-planning approach is proposed for multirobot sensor-based coverage of narrow environments by considering the energy capacities of the mobile robots. The approach is tested in a laboratory environment using Pioneer 3-DX mobile robots. Simulations are also conducted for a larger test environment.

UAV Route Planning for Joint Search and Track MissionsAn Information-Value Approach

Abstract: A new approach to route planning for joint search and track missions by coordinated unmanned aerial vehicles (UAVs) is presented. The cornerstone is a novel objective function that integrates naturally and coherently the conflicting objectives of target detection, target tracking, and vehicle survivability into a single scalar index for path optimization. This objective function is the value of information gained by the mission on average in terms of a summation, where the number of terms reflects the number of targets detected while how large each term is reflects how well each detected target is tracked. The UAV following the path that maximizes this objective function is expected to gain the most valuable information by detecting the most important targets and tracking them during the most critical times. Although many optimization algorithms exist, we use a modified particle swarm optimization algorithm along with our proposed objective function to determine which trajectory is the best on the average at detecting and tracking targets. For simplicity, perfect communication with centralized fusion is assumed and the problems of false alarm, data association, and model mismatch are not considered. For analysis, we provide several simplified examples along with a more realistic simulation. Simulation results show that by adjusting the parameters of the objective function, solutions can be optimized according to the desired tradeoff between the conflicting objectives of detecting new targets and tracking previously detected targets. Our approach can also be used to update plans in real time by incorporating the information obtained up to the time (and then reusing our approach).

Optimal Paths for Landmark-Based Navigation by Differential-Drive Vehicles With Field-of-View Constraints

Abstract: In this paper, we consider the problem of planning optimal paths for a differential-drive robot with limited sensing, that must maintain visibility of a fixed landmark as it navigates in its environment. In particular, we assume that the robot's vision sensor has a limited field of view (FOV), and that the fixed landmark must remain within the FOV throughout the robot's motion. We first investigate the nature of extremal paths that satisfy the FOV constraint. These extremal paths saturate the camera pan angle. We then show that optimal paths are composed of straight-line segments and sections of these these extremal paths. We provide the complete characterization of the shortest paths for the system by partitioning the plane into a set of disjoint regions, such that the structure of the optimal path is invariant over the individual regions

Survey on model-based manipulation planning of deformable objects

Abstract: A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modeling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.

Vision-based motion planning and exploration algorithms for mobile robots

Abstract: This paper considers the problem of systematically exploring an unfamiliar environment in search of one or more recognizable targets. The proposed exploration algorithm is based on a novel representation of environments containing visual landmarks, called the boundary place graph. This representation records the set of recognizable objects (landmarks) that are visible from the boundary of each configuration space obstacle. The exploration algorithm constructs the boundary place graph incrementally from sensor data. Once the robot has completely explored an environment, it can use the constructed representation to carry out further navigation tasks. We provide a necessary and sufficient condition under which the algorithm is guaranteed to discover all landmarks. This algorithm has been implemented on our mobile robot platform RJ, and results from these experiments are presented.