Intrinsically-Robust and Control-Aware Motion Planning for Robots in Real-World Conditions – CAMP

CAMP is structured along three research axes (WPs): the first axis (WP1) will focus on how to best quantify the effects of uncertainties on a sensor-robot/controller pair by leveraging a combination of the so-called «closed-loop sensitivity matrix« and of more classical uncertainty propagation metrics depending on whether the robot/sensing uncertainty can be reliably given a parametric form or not, the second axis (WP2) will deal with the generation of intrinsically-robust and control-aware motion plans by exploiting the metrics of WP1 and by borrowing from the literature on global sampling-based motion planners, and the third axis (WP3) will be devoted to the experimental validation of the proposed approach and comparison against variants (e.g., MPC). A final WP4 will deal with management, dissemination and exploitation of the project results.

We have started developing extensions of the previously introduced closed-loop sensitivity matrix, by considering other quantities such as the input sensitivity, as well as ways to include a model of the parametric uncertainty of the robot model in these quantities. We have also obtained an algorithm for evaluating the tubes of perturbed states/inputs given a model of parametric uncertainty. On the planning side we have proposed a first optimal sampling-based planning framework in order to (1) deal with complex kino-dynamic motions of systems with non trivial dynamics, and (2) optimize the sensitivity-based metrics of WP1 while accounting for the tubes of perturbed trajectories for producing collision-free reference motions that are robust against parameter’s uncertainties.

We are currently working towards an experimental validation of these approaches, as well as the development of more advanced planning schemes able to explicitly consider the presence of multiple objectives and constraints.

1. Christoph Bohm, Pascal Brault, Quentin Delamare, Paolo Robuffo Giordano, and Stephan Weiss. COP: Control & Observability-aware Planning. In 2022 IEEE Int. Conf. on Robotics and Automation (ICRA 2022), 2022

2. Ali Srour, Antonio Franchi, and Paolo Robuffo Giordano. Controller and Trajectory Optimization for a Quadrotor UAV with Parametric Uncertainty. In 2023 IEEE Int. Conf. on Robotics and Automation (ICRA 2023), 2023

3. S. Wasiela, P.Robuffo Giordano, J. Cortés and T. Siméon. A Sensitivity-Aware Motion Planner (SAMP) to Generate Intrinsically-Robust Trajectories. Submi ed to IEEE Int. conference on robotics and automation (ICRA), 2023

4. Pascal Brault, Q. Delamare, and P. Robuffo Giordano. Robust Trajectory Planning with Parametric Uncertainties. In 2021 IEEE Int. Conf. on Robotics and Automation (ICRA 2021), 2021

5. Pascal Brault and Paolo Robuffo Giordano. Tube-based trajectory optimization for robots with parametric uncertainty. Under preparation, available at
h p://rainbow-doc.irisa.fr/pdf/ICRA_2022_CLS.pdf

An effective way of dealing with the complexity of robots operating in
real (uncertain) environments is the paradigm of feedforward/feedback or
planning/control: in a first step a suitable nominal trajectory
(feedforward) for the robot states/controls is planned exploiting the
available information (e.g., a model of the robot and of the
environment). This step is usually executed offline and can take into
account presence of constraints (e.g., collision avoidance, limited
actuation) and optimality w.r.t. metrics of interest (e.g., time,
energy). An open-loop execution of this planned trajectory would,
however, fail in most practical cases because of the unavoidable
approximations and uncertainties affecting the model used for planning.
Therefore, the planned trajectory is in practice robustly executed by
making use of a motion controller that closes the loop between planned
and actual motion, providing a robustness layer against all the
unmodeled effects that could not be considered at the planning stage.
This sequential composition, treating planning and control as separated
components, has however several shortcomings: on the planning side,
modern planners can generate feasible and globally optimal paths for
high-dimensional systems and complex constraints/environments. However,
they do not typically consider (and, even less, exploit) the unavoidable
presence of a runtime feedback controller that will actually execute the
plan. On the control side, a large number of adaptive or robust (e.g.,
H-infinity or passivity-based) control schemes have been proposed over
the decades for delivering a good level of robustness against
uncertainties/disturbances. However, these approaches are mostly local
and, besides robustness, can hardly tackle other
constraints/requirements such as feasibility (e.g., limited actuation,
obstacle avoidance), performance and global optimality, which can
otherwise be effectively handled by traditional planners. While there
has been an effort in proposing robust planners or more global
controllers (e.g., Model Predictive Control), a truly unified approach
that fully exploits the techniques of the motion planning and robust
control communities is still missing and the existing state-of-the-art
has several important limitations, namely (1) lack of generality, (2)
lack of computational efficiency, and (3) poor robustness. All these
shortcomings are a major limiting factor in the autonomy and
decision-making capabilities of robots operating in all those complex
scenarios (real-world conditions, non-negligible effects of the
uncertainties, fast dynamics) which are instead the typical conditions
in which future robots are expected to operate. Therefore, the ambition
and scientific goal of CAMP is to (1) develop a general and unified
intrinsically-robust and control-aware motion planning framework able to
address all the above-mentioned issues, and to (2) demonstrate the
applicability of this new framework in a real-world challenging scenario
involving a pick-and-place task for a 7-dof robot arm, and a cooperative
manipulation task for a mobile robot and aerial manipulator. These tasks
are purposely chosen for showcasing at best the benefits of the proposed
planning framework since accuracy (implying low execution uncertainty)
and execution speed (resulting in a significant impact of unmodeled
effects and disturbances) are crucial. The consortium of CAMP regroups
three teams with a consolidated (and complementary) competence in
planning, estimation and control. If successful, the project will have
concrete benefits for any robotic application in which uncertainties
play a significant role in all areas of robotics.