6,348 research outputs found
Dynamic graph-based search in unknown environments
A novel graph-based approach to search in unknown environments is presented. A virtual geometric structure is imposed on the environment represented in computer memory by a graph. Algorithms use this representation to coordinate a team of robots (or entities). Local discovery of environmental features cause dynamic expansion of the graph resulting in global exploration of the unknown environment. The algorithm is shown to have O(k.nH) time
complexity, where nH is the number of vertices of the discovered environment and 1 <= k <= nH. A maximum bound on the length of the resulting walk is given
Reactive Trajectory Generation in an Unknown Environment
Autonomous trajectory generation for unmanned aerial vehicles (UAVs) in
unknown environments continues to be an important research area as UAVs become
more prolific. We define a trajectory generation algorithm for a vehicle in an
unknown environment with wind disturbances, that relies only on the vehicle's
on-board distance sensors and communication with other vehicles within a finite
region to generate a smooth, collision-free trajectory up to the fourth
derivative. The proposed trajectory generation algorithm can be used in
conjunction with high-level planners and low-level motion controllers. The
algorithm provides guarantees that the trajectory does not violate the
vehicle's thrust limitation, sensor constraints, or a user-defined clearance
radius around other vehicles and obstacles. Simulation results of a quadrotor
moving through an unknown environment with a moving obstacle demonstrates the
trajectory generation performance.Comment: Revised version with minor text updates and more representative
simulation results for IROS 2017 conferenc
Efficient Multi-Robot Coverage of a Known Environment
This paper addresses the complete area coverage problem of a known
environment by multiple-robots. Complete area coverage is the problem of moving
an end-effector over all available space while avoiding existing obstacles. In
such tasks, using multiple robots can increase the efficiency of the area
coverage in terms of minimizing the operational time and increase the
robustness in the face of robot attrition. Unfortunately, the problem of
finding an optimal solution for such an area coverage problem with multiple
robots is known to be NP-complete. In this paper we present two approximation
heuristics for solving the multi-robot coverage problem. The first solution
presented is a direct extension of an efficient single robot area coverage
algorithm, based on an exact cellular decomposition. The second algorithm is a
greedy approach that divides the area into equal regions and applies an
efficient single-robot coverage algorithm to each region. We present
experimental results for two algorithms. Results indicate that our approaches
provide good coverage distribution between robots and minimize the workload per
robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), 201
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