A survey on active simultaneous localization and map**: State of the art and new frontiers
Active simultaneous localization and map** (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …
controlling the motion of a robot to build the most accurate and complete model of the …
MR-TopoMap: Multi-robot exploration based on topological map in communication restricted environment
Multi-robot exploration in unknown environments is a fundamental task for a multi-robot
system, involving inter-robot communication through messages among the robots. However …
system, involving inter-robot communication through messages among the robots. However …
Distributed multi-robot potential-field-based exploration with submap-based map** and noise-augmented strategy
Multi-robot collaboration has become a needed component in unknown environment
exploration due to its ability to accomplish various challenging situations. Potential-field …
exploration due to its ability to accomplish various challenging situations. Potential-field …
Hierarchical path planner for unknown space exploration using reinforcement learning-based intelligent frontier selection
J Fan, X Zhang, Y Zou - Expert Systems with Applications, 2023 - Elsevier
Path planning in unknown environments is extremely useful for some specific tasks, such as
exploration of outer space planets, search and rescue in disaster areas, home swee** …
exploration of outer space planets, search and rescue in disaster areas, home swee** …
Learning coverage paths in unknown environments with deep reinforcement learning
Coverage path planning (CPP) is the problem of finding a path that covers the entire free
space of a confined area, with applications ranging from robotic lawn mowing to search-and …
space of a confined area, with applications ranging from robotic lawn mowing to search-and …
HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks
In this paper, we introduce HDPlanner, a deep reinforcement learning (DRL) based
framework designed to tackle two core and challenging tasks for mobile robots: autonomous …
framework designed to tackle two core and challenging tasks for mobile robots: autonomous …
A framework to co-optimize robot exploration and task planning in unknown environments
Robots often need to accomplish complex tasks in unknown environments, which is a
challenging problem, involving autonomous exploration for acquiring necessary scene …
challenging problem, involving autonomous exploration for acquiring necessary scene …
IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity
Information sharing is critical in time-sensitive and realistic multi-robot exploration,
especially for smaller robotic teams in large-scale environments where connectivity may be …
especially for smaller robotic teams in large-scale environments where connectivity may be …
EMExplorer: an episodic memory enhanced autonomous exploration strategy with Voronoi domain conversion and invalid action masking
Autonomous exploration is a critical technology to realize robotic intelligence as it allows
unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a …
unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a …