Trajectory planning with task constraints in densely filled environments

In this paper the problem of computing a rigid object trajectory in an environment populated with deformable objects is addressed. The problem arises in Minimally Invasive Robotic Surgery (MIRS) from the needs of reaching a point of interest inside the anatomy with rigid laparoscopic instruments. We address the case of abdominal surgery. The abdomen is a densely populated soft environment and it is not possible to apply classical techniques for obstacle avoidance because a collision free solution is, most of the time, not feasible. In order to have a convergent algorithm with, at least, one possible solution we have to relax the constraints and allow collision under a specific contact threshold to avoid tissue damaging. In this work a new approach for trajectory planning under these peculiar conditions is implemented. The method computes offline the path which is then tested in a surgical simulator as part of a pre-operative surgical plan

Efficient Path Planning for Mobile Robots in Environments with Deformable Objects

Abstract — The ability to reliably navigate through the environment is an important prerequisite for truly autonomous robots. In this paper, we consider the problem of path planning in environments with non-rigid obstacles such as curtains or plants. We present an approach that combines probabilistic roadmaps with a physical simulation of object deformations to determine a path that optimizes the trade-off between the deformation cost and the distance to be traveled. We describe how our approach utilizes Finite Element theory for calculating the deformation cost. Since the high computational requirements of the corresponding simulation prevent this method from being applicable online, we present an approximation that uses a preprocessing step to determine a deformation cost function for each object. This cost function allows us to estimate the deformation costs of arbitrary paths through the objects and is used to evaluate the trajectories generated by the roadmap planner online. We present experiments which demonstrate that the resulting algorithm plans nearly identical trajectories compared to the method that relies on computationally intense simulations. At the same time, our approach allows the robot to quickly calculate paths in environments with deformable objects. I

Closed-Loop Planning and Control of Steerable Medical Needles

Steerable needles have the potential to increase the effectiveness of needle-based clinical procedures such as biopsy, drug delivery, and radioactive seed implantation for cancer treatment. These needles can trace curved paths when inserted into tissue, thereby increasing maneuverability and targeting accuracy while reaching previously inaccessible targets that are behind sensitive or impenetrable anatomical regions. Guiding these flexible needles along an intended path requires continuously inserting and twisting the needle at its base, which is not intuitive for a human operator. In addition, the needle often deviates from its intended trajectory due to factors such as tissue deformation, needle-tissue interaction, noisy actuation and sensing, modeling errors, and involuntary patient motions. These challenges can be addressed with the assistance of robotic systems that automatically compensate for these perturbations by performing motion planning and feedback control of the needle in a closed-loop fashion under sensory feedback. We present two approaches for efficient closed-loop guidance of steerable needles to targets within clinically acceptable accuracy while safely avoiding sensitive or impenetrable anatomical structures. The first approach uses a fast motion planning algorithm that unifies planning and control by continuously replanning, enabling correction for perturbations as they occur. We evaluate our method using a needle steering system in phantom and ex vivo animal tissues. The second approach integrates motion planning and feedback control of steerable needles in highly deformable environments. We demonstrate that this approach significantly improves the probability of success compared to prior approaches that either consider uncertainty or deformations but not both simultaneously. We also propose a data-driven method to estimate parameters of stochastic models of steerable needle motion. These models can be used to create realistic medical simulators for clinicians wanting to train for steerable needle procedures and to improve the effectiveness of existing planning and control methods. This dissertation advances the state of the art in planning and control of steerable needles and is an important step towards realizing needle steering in clinical practice. The methods developed in this dissertation also generalize to important applications beyond medical needle steering, such as manipulating deformable objects and control of mobile robots.Doctor of Philosoph