This paper focuses on the optimal motion planning problem for cooperative aerial manipulation. We use the rapidly exploring random trees star (RRT*) algorithm that finds feasible paths quickly and optimizes them. For local planning within RRT*, we developed a trajectory planner using Bezier-curve which utilizes the differential flatness property of the aerial manipulator. Time-parameterization ...

This paper presents a sampling-based method for path planning in robotic systems without known cost-to-go information. It uses trajectories generated from random search to heuristically learn the cost-to-go of regions within the configuration space. Gradually, the search is increasingly directed towards lower cost regions of the configuration space, thereby producing paths that converge towards...

There is a need for systems which can autonomously perform coverage tasks on large outdoor areas. Unfortunately, the state-of-theart is to use GPS based localization, which is not suitable for precise operations near trees and other obstructions. In this paper we present a robotic platform for autonomous coverage tasks. The system architecture integrates laser based localization and mapping usi...

This paper reviews P´olya urn models and their connection to random trees. Basic results are presented, together with proofs that underly the historical evolution of the accompanying thought process. Extensions and generalizations are given according to chronology: • P´olya-Eggenberger’s urn • Bernard Friedman’s urn • Generalized P´olya urns • Extended urn schemes • Invertible urn schemes ...

Many robotic tasks require solutions that maximize multiple performance objectives. For example, in path-planning, these objectives often include finding short paths that avoid risk and maximize the information obtained by the robot. Although there exist many algorithms for multi-objective optimization, few of these algorithms apply directly to robotic path-planning and fewer still are capable ...

This paper considers the problem of online informative motion planning for a network of heterogeneous mobile sensing agents, each subject to dynamic constraints, environmental constraints, and sensor limitations. Previouswork has not yielded algorithms that are amenable to such general constraint characterizations. In this paper, the information-rich rapidly-exploring random tree algorithm is p...

This paper presents an approach for learning robot navigation behaviors from demonstration using Optimal Rapidly-exploring Random Trees (RRT∗) as main planner. A new learning algorithm combining both Inverse Reinforcement Learning (IRL) and RRT∗ is developed in order to learn the RRT∗’s cost function from demonstrations. This cost function can be used later in a regular RRT∗ for robot planning ...