837 research outputs found
Long-range navigation in complex and dynamic environments with Full-Stack S-DOVS
Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios
When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning
The hierarchy of global and local planners is one of the most commonly
utilized system designs in autonomous robot navigation. While the global
planner generates a reference path from the current to goal locations based on
the pre-built static map, the local planner produces a kinodynamic trajectory
to follow the reference path while avoiding perceived obstacles. To account for
unforeseen or dynamic obstacles not present on the pre-built map, ``when to
replan'' the reference path is critical for the success of safe and efficient
navigation. However, determining the ideal timing to execute replanning in such
partially unknown environments still remains an open question. In this work, we
first conduct an extensive simulation experiment to compare several common
replanning strategies and confirm that effective strategies are highly
dependent on the environment as well as the global and local planners. Based on
this insight, we derive a new adaptive replanning strategy based on deep
reinforcement learning, which can learn from experience to decide appropriate
replanning timings in the given environment and planning setups. Our
experimental results demonstrate that the proposed replanner can perform on par
or even better than the current best-performing strategies in multiple
situations regarding navigation robustness and efficiency.Comment: 7 pages, 3 figure
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
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