Real-time sampling-based motion planning with dynamic obstacles

Autonomous robots are increasingly becoming incorporated in everyday human activities, and this trend does not show any signs of slowing down. One task that autonomous robots will need to reliably perform among humans and other dynamic objects is motion planning. That is, to reliably navigate a robot to a desired pose as quickly as possible while minimizing the probability of colliding with other objects. This involves not only planning around the predicted future trajectories of dynamic obstacles, but doing so in a real-time manner so that the robot can remain reactive to its surroundings. Current methods do not directly address this problem. This thesis proposes a new real-time planning algorithm called real-time R* (RTR*). RTR* is based on the R* search algorithm that couples random sampling with heuristic search and has been shown to work well in several different robotics domains. Several modifications needed to transform R* into a real-time algorithm are described. Additional modifications that were developed specifically for this problem domain are also detailed. An empirical evaluation is given comparing RTR* with several state-of-the-art motion planning and real-time search algorithms. RTR* shows promising performance and improves on R*, however it underperforms the current state-of-the-art. Several enhancements are discussed that could improve the behavior of RTR*

3D Dynamic Motion Planning for Robot-Assisted Cannula Flexible Needle Insertion into Soft Tissue

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning. © 2016 Author

Real-Time Tube-Based Non-Gaussian Risk Bounded Motion Planning for Stochastic Nonlinear Systems in Uncertain Environments via Motion Primitives

We consider the motion planning problem for stochastic nonlinear systems in uncertain environments. More precisely, in this problem the robot has stochastic nonlinear dynamics and uncertain initial locations, and the environment contains multiple dynamic uncertain obstacles. Obstacles can be of arbitrary shape, can deform, and can move. All uncertainties do not necessarily have Gaussian distribution. This general setting has been considered and solved in [1]. In addition to the assumptions above, in this paper, we consider long-term tasks, where the planning method in [1] would fail, as the uncertainty of the system states grows too large over a long time horizon. Unlike [1], we present a real-time online motion planning algorithm. We build discrete-time motion primitives and their corresponding continuous-time tubes offline, so that almost all system states of each motion primitive are guaranteed to stay inside the corresponding tube. We convert probabilistic safety constraints into a set of deterministic constraints called risk contours. During online execution, we verify the safety of the tubes against deterministic risk contours using sum-of-squares (SOS) programming. The provided SOS-based method verifies the safety of the tube in the presence of uncertain obstacles without the need for uncertainty samples and time discretization in real-time. By bounding the probability the system states staying inside the tube and bounding the probability of the tube colliding with obstacles, our approach guarantees bounded probability of system states colliding with obstacles. We demonstrate our approach on several long-term robotics tasks.Comment: International Conference on Intelligent Robots and Systems (IROS), 202

An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research communit

Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns

This paper presents a real-time path planning algorithm which can guarantee probabilistic feasibility for autonomous robots subject to process noise and an uncertain environment, including dynamic obstacles with uncertain motion patterns. The key contribution of the work is the integration of a novel method for modeling dynamic obstacles with uncertain future trajectories. The method, denoted as RR-GP, uses a learned motion pattern model of the dynamic obstacles to make long-term predictions of their future paths. This is done by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation method which ensures dynamic feasibility. This prediction model is then utilized within chance-constrained rapidly-exploring random trees (CC-RRT), which uses chance constraints to explicitly achieve probabilistic constraint satisfaction while maintaining the computational benefits of sampling-based algorithms. With RR-GP embedded in the CC-RRT framework, theoretical guarantees can be demonstrated for linear systems subject to Gaussian uncertainty, though the extension to nonlinear systems is also considered. Simulation results show that the resulting approach can be used in real-time to efficiently and accurately execute safe paths

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

Real-time motion planning and simulation of cranes in construction

Real-time planning the motion of heavy equipment (e.g. cranes) is an important issue in construction projects, where rapid and accurate planning directly affects the safety and productivity of operation. The work presented in this thesis is directed towards automatically generating an accurate motion plan in space and time for cranes by: (1) Investigating and utilizing motion planning algorithms to generate feasible paths with respect to all considered constraints; (2) Extending the efficiency of motion planning under complex global constraints (Le. geometrical constraints) that represent static and dynamic obstacles found in the construction site; and (3) Considering local constraints that are related to the stability of the crane itself. Local constraints include engineering constraints (e.g. workloads for cranes) in addition to kinematic and dynamic constraints for the crane joints. The methodology presented in this thesis was applied to develop a specialized motion planning system for construction equipment called Intelligent Construction Equipment motion Planner (ICE-Planner). This system was integrated into the 3D software to define, solve and visualize motion planning in real time. The proposed methodology provides: (1) A motion planning framework for supporting cranes with the ability of generalizing over different types of equipment; (2) practical equipment planning which is aware of local constraints derived from engineering and kinematics properties of the equipment itself; (3) more accurate and realistic motion planning with efficiency in re-planning dynamic cases found in actual sites; and (4) the ability of visualizing and simulating motion planning results in real-time