Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Trajectory Planning and Generation
Optimal Acceleration-Bounded Trajectory Planning in Dynamic Environments Along a Specified PathVehicles that cross lanes of traffic encounter the problem of navigating around dynamic obstacles under actuation constraints. This paper presents an optimal, exact, polynomial-time planner for optimal bounded-acceleration trajectories along a fixed, given path with dynamic obstacles. The planner constructs reachable sets in the path-velocity-time (PVT) space by propagating reachable velocity sets between obstacle tangent points in the path-time (PT) space. The terminal velocities attainable by endpoint-constrained trajectories in the same homotopic class are proven to span a convex interval, so the planner merges contributions from individual homotopic classes to find the exact range of reachable velocities and times at the goal. A reachability analysis proves that running time is polynomial given reasonable assumptions, and empirical tests demonstrate that it scales well in practice and can handle hundreds of dynamic obstacles in a fraction of a second on a standard PC.
Robot Excitation Trajectories for Dynamic Parameter Estimation Using Optimized B-SplinesIn this paper we adressed the problem of finding exciting trajectories for the identification of manipulator link inertia parameters. This can be formulated as a constraint nonlinear optimization problem. The new approach in the presented method is the parameterization of the trajectories with optimized B-splines. Experiments are carried out on a 7 joint Light-Weight robot with torque sensoring in each joint. Thus, unmodeled joint friction and noisy motor current measurements must not be taken into account here. The estimated dynamic model is verified on a different validation trajectory. The results show a clearly improvement of the estimated dynamic model compared to a CAD-valued model.
On-Line Trajectory Generation: Nonconstant Motion ConstraintsA concept of on-line trajectory generation for robot motion control systems enabling instantaneous reactions to unforeseen sensor events was introduced in a former publication. This previously proposed class of algorithms requires constant kinematic motion constraints, and this paper extends the approach by the usage of time-variant motion constraints, such that low-level trajectory parameters can now abruptly be changed, and the system can react instantaneously within the same control cycle (typically one millisecond or less). This feature is important for instantaneous switchings between state spaces and reference frames at sensor-dependent instants of time, and for the usage of the algorithm as a control submodule in a hybrid switched robot motion control system. Real-world experimental results of two sample use-cases highlight the practical relevance of this extension.
Setpoint Scheduling for Autonomous Vehicle ControllersThis paper considers the problem of controlling an autonomous vehicle to arrive at a specific position on a road at a given time and velocity. This ability is particularly useful for a recently introduced autonomous intersection management protocol, called AIM, which has been shown to lead to lower delays than traffic signals and stop signs. Specifically, we introduce a setpoint scheduling algorithm for generating setpoints for the PID controllers for the brake and throttle actuators of an autonomous vehicle. The algorithm constructs a feasible setpoint schedule such that the vehicle arrives at the position at the correct time and velocity. Our experimental results show that the algorithm outperforms a heuristic-based setpoint scheduler that does not provide any guarantee about the arrival time and velocity.
A Real-Time Motion Planner with Trajectory Optimization for Autonomous VehiclesIn this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cost functions. Then an iterative optimization is applied to both the path and speed of the resultant trajectory. The post-optimization is of low computational complexity and is able to converge to a higherquality solution within a few iterations. Compared with the planner without optimization, this framework can reduce the planning time by 52% and improve the trajectory quality. The proposed motion planner is implemented and tested both in simulation and on a real autonomous vehicle in three different scenarios. Experiments show that the planner outputs highquality trajectories and performs intelligent driving behaviors.
Improved Non-Linear Spline Fitting for Teaching Trajectories to Mobile RobotsIn this paper, we present improved spline fitting techniques with the application of trajectory teaching for mobile robots. Given a recorded reference trajectory, we apply non-linear least-squares optimization to accurately approximate the trajectory using a parametric spline. The fitting process is carried out without fixed correspondences between data points and points along the spline, which improves the fit especially in sharp curves. By using a specific path model, our approach requires substantially fewer free parameters than standard approaches to achieve similar residual errors. Thus, the generated paths are ideal for subsequent optimization to reduce the time of travel or for the combination with autonomous planning to evade obstacles blocking the path. Our experiments on real-world data demonstrate the advantages of our method in comparison with standard approaches.