74 research outputs found
Safety-Critical Ergodic Exploration in Cluttered Environments via Control Barrier Functions
In this paper, we address the problem of safe trajectory planning for
autonomous search and exploration in constrained, cluttered environments.
Guaranteeing safe navigation is a challenging problem that has garnered
significant attention. This work contributes a method that generates guaranteed
safety-critical search trajectories in a cluttered environment. Our approach
integrates safety-critical constraints using discrete control barrier functions
(DCBFs) with ergodic trajectory optimization to enable safe exploration.
Ergodic trajectory optimization plans continuous exploratory trajectories that
guarantee full coverage of a space. We demonstrate through simulated and
experimental results on a drone that our approach is able to generate
trajectories that enable safe and effective exploration. Furthermore, we show
the efficacy of our approach for safe exploration of real-world single- and
multi- drone platforms
Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field
Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent
ergodic motion control guided by a gradient of a potential field. A finite
element method is hereby implemented to obtain a solution of Helmholtz partial
differential equation, which models the potential field for surveying motion
control. This allows us to survey arbitrarily shaped domains and to include
obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental
idea. For a simple kinematic motion, the obstacles and boundary avoidance
constraints are successfully handled by directing the agent motion with the
gradient of the potential. However, including additional constraints, such as
the minimal clearance dsitance from stationary and moving obstacles and the
minimal path curvature radius, requires further alternations of the control
algorithm. We introduce a relatively simple yet robust approach for handling
these constraints by formulating a straightforward optimization problem based
on collision-free escapes route maneuvers. This approach provides a guaranteed
collision avoidance mechanism, while being computationally inexpensive as a
result of the optimization problem partitioning. The proposed motion control is
evaluated in three realistic surveying scenarios simulations, showing the
effectiveness of the surveying and the robustness of the control algorithm.
Furthermore, potential maneuvering difficulties due to improperly defined
surveying scenarios are highlighted and we provide guidelines on how to
overpass them. The results are promising and indiacate real-world applicability
of proposed constrained multi-agent motion control for autonomous surveying and
potentially other HEDAC utilizations.Comment: Revised manuscrip
On the Ergodicity of an Autonomous Robot for Efficient Environment Explorations
This paper addresses the autonomous robot ergodicity problem for efficient
environment exploration. The spatial distribution as a reference is given by a
mixture of Gaussian and the mass generation of the robot is assumed to be
skinny Gaussian. The main problem to solve is then to find out proper timing
for the robot to visit as well as leave each component-wise Gaussian for the
purpose of achieving the ergodicity. The novelty of the proposed method is that
no approximation is required for the developed method. Given the definition of
the ergodic function, a convergence condition is derived based on the timing
analysis. Also, a formal algorithm to achieve the ergodicity is provided. To
support the validity of the proposed algorithm, simulation results are
provided
Whole-Body Exploration with a Manipulator Using Heat Equation
This paper presents a whole-body robot control method for exploring and
probing a given region of interest. The ergodic control formalism behind such
an exploration behavior consists of matching the time-averaged statistics of a
robot trajectory with the spatial statistics of the target distribution. Most
existing ergodic control approaches assume the robots/sensors as individual
point agents moving in space. We introduce an approach exploiting multiple
kinematically constrained agents on the whole-body of a robotic manipulator,
where a consensus among the agents is found for generating control actions. To
do so, we exploit an existing ergodic control formulation called heat
equation-driven area coverage (HEDAC), combining local and global exploration
on a potential field resulting from heat diffusion. Our approach extends HEDAC
to applications where robots have multiple sensors on the whole-body (such as
tactile skin) and use all sensors to optimally explore the given region. We
show that our approach increases the exploration performance in terms of
ergodicity and scales well to real-world problems using agents distributed on
multiple robot links. We compare our method with HEDAC in kinematic simulation
and demonstrate the applicability of an online exploration task with a 7-axis
Franka Emika robot.Comment: Submitted to IEEE Robotics and Automation Letter
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