Probabilistic Collision Constraint for Motion Planning in Dynamic Environments

Online generation of collision free trajectories is of prime importance for autonomous navigation. Dynamic environments, robot motion and sensing uncertainties adds further challenges to collision avoidance systems. This paper presents an approach for collision avoidance in dynamic environments, incorporating robot and obstacle state uncertainties. We derive a tight upper bound for collision probability between robot and obstacle and formulate it as a motion planning constraint which is solvable in real time. The proposed approach is tested in simulation considering mobile robots as well as quadrotors to demonstrate that successful collision avoidance is achieved in real time application. We also provide a comparison of our approach with several state-of-the-art methods.Comment: Accepted for presentation at the 16th International Conference on Intelligent Autonomous Systems (IAS-16

Magnetic motion control and planning of untethered soft grippers using ultrasound image feedback

Soft miniaturized untethered grippers can be used to manipulate and transport biological material in unstructured and tortuous environments. Previous studies on control of soft miniaturized grippers employed cameras and optical images as a feedback modality. However, the use of cameras might be unsuitable for localizing miniaturized agents that navigate within the human body. In this paper, we demonstrate the wireless magnetic motion control and planning of soft untethered grippers using feedback extracted from B-mode ultrasound images. Results show that our system employing ultrasound images can be used to control the miniaturized grippers with an average tracking error of 0.4±0.13 mm without payload and 0.36±0.05 mm when the agent performs a transportation task with a payload. The proposed ultrasound feedback magnetic control system demonstrates the ability to control miniaturized grippers in situations where visual feedback cannot be provided via cameras

Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty

We introduce a novel optimization-based motion planner, Stochastic Extended LQR (SELQR), which computes a trajectory and associated linear control policy with the objective of minimizing the expected value of a user-defined cost function. SELQR applies to robotic systems that have stochastic non-linear dynamics with motion uncertainty modeled by Gaussian distributions that can be state- and control-dependent. In each iteration, SELQR uses a combination of forward and backward value iteration to estimate the cost-to-come and the cost-to-go for each state along a trajectory. SELQR then locally optimizes each state along the trajectory at each iteration to minimize the expected total cost, which results in smoothed states that are used for dynamics linearization and cost function quadratization. SELQR progressively improves the approximation of the expected total cost, resulting in higher quality plans. For applications with imperfect sensing, we extend SELQR to plan in the robot's belief space. We show that our iterative approach achieves fast and reliable convergence to high-quality plans in multiple simulated scenarios involving a car-like robot, a quadrotor, and a medical steerable needle performing a liver biopsy procedure

High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning

As sampling-based motion planners become faster, they can be re-executed more frequently by a robot during task execution to react to uncertainty in robot motion, obstacle motion, sensing noise, and uncertainty in the robot’s kinematic model. We investigate and analyze high-frequency replanning (HFR), where, during each period, fast sampling-based motion planners are executed in parallel as the robot simultaneously executes the first action of the best motion plan from the previous period. We consider discrete-time systems with stochastic nonlinear (but linearizable) dynamics and observation models with noise drawn from zero mean Gaussian distributions. The objective is to maximize the probability of success (i.e., avoid collision with obstacles and reach the goal) or to minimize path length subject to a lower bound on the probability of success. We show that, as parallel computation power increases, HFR offers asymptotic optimality for these objectives during each period for goal-oriented problems. We then demonstrate the effectiveness of HFR for holonomic and nonholonomic robots including car-like vehicles and steerable medical needles