7 research outputs found
Sampling-based Motion Planning via Control Barrier Functions
Robot motion planning is central to real-world autonomous applications, such
as self-driving cars, persistence surveillance, and robotic arm manipulation.
One challenge in motion planning is generating control signals for nonlinear
systems that result in obstacle free paths through dynamic environments. In
this paper, we propose Control Barrier Function guided Rapidly-exploring Random
Trees (CBF-RRT), a sampling-based motion planning algorithm for continuous-time
nonlinear systems in dynamic environments. The algorithm focuses on two
objectives: efficiently generating feasible controls that steer the system
toward a goal region, and handling environments with dynamical obstacles in
continuous time. We formulate the control synthesis problem as a Quadratic
Program (QP) that enforces Control Barrier Function (CBF) constraints to
achieve obstacle avoidance. Additionally, CBF-RRT does not require nearest
neighbor or collision checks when sampling, which greatly reduce the run-time
overhead when compared to standard RRT variants
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
Safe Path Planning for Polynomial Shape Obstacles via Control Barrier Functions and Logistic Regression
Safe path planning is critical for bipedal robots to operate in
safety-critical environments. Common path planning algorithms, such as RRT or
RRT*, typically use geometric or kinematic collision check algorithms to ensure
collision-free paths toward the target position. However, such approaches may
generate non-smooth paths that do not comply with the dynamics constraints of
walking robots. It has been shown that the control barrier function (CBF) can
be integrated with RRT/RRT* to synthesize dynamically feasible collision-free
paths. Yet, existing work has been limited to simple circular or elliptical
shape obstacles due to the challenging nature of constructing appropriate
barrier functions to represent irregular-shaped obstacles. In this paper, we
present a CBF-based RRT* algorithm for bipedal robots to generate a
collision-free path through complex space with polynomial-shaped obstacles. In
particular, we used logistic regression to construct polynomial barrier
functions from a grid map of the environment to represent arbitrarily shaped
obstacles. Moreover, we developed a multi-step CBF steering controller to
ensure the efficiency of free space exploration. The proposed approach was
first validated in simulation for a differential drive model, and then
experimentally evaluated with a 3D humanoid robot, Digit, in a lab setting with
randomly placed obstacles.Comment: 7 pages, 8 figures. Supplemental Video: https://youtu.be/r_hkuK5cMw