Least Conservative Linearized Constraint Formulation for Real-Time Motion Generation

Today robotics has shown many successful strategies to solve several navigation problems. However, moving into a dynamic environment is still a challenging task. This paper presents a novel method for motion generation in dynamic environments based on real-time nonlinear model predictive control (NMPC). At the core of our approach is a least conservative linearized constraint formulation built upon the real-time iteration (RTI) scheme with Gauss- Newton Hessian approximation. We demonstrate that the proposed constraint formulation is less conservative for planners based on Newton-type method than for those based on a fully converged NMPC method. Additionally, we show the performance of our approach in simulation, in a scenario where the Crazyflie nanoquadcopter avoids balls and reaches its desired goal in spite of the uncertainty about when the balls will be thrown. The numerical results validate our theoretical findings and illustrate the computational efficiency of the proposed scheme

Stereo vision-based obstacle avoidance module on 3D point cloud data

This paper deals in building a 3D vision-based obstacle avoidance and navigation. In order for an autonomous system to work in real life condition, a capability of gaining surrounding environment data, interpret the data and take appropriate action is needed. One of the required capability in this matter for an autonomous system is a capability to navigate cluttered, unorganized environment and avoiding collision with any present obstacle, defined as any data with vertical orientation and able to take decision when environment update exist. Proposed in this work are two-step strategy of extracting the obstacle position and orientation from point cloud data using plane based segmentation and the resultant segmentation are mapped based on obstacle point position relative to camera using occupancy grid map to acquire obstacle cluster position and recorded the occupancy grid map for future use and global navigation, obstacle position gained in grid map is used to plan the navigation path towards target goal without going through obstacle position and modify the navigation path to avoid collision when environment update is present or platform movement is not aligned with navigation path based on timed elastic band method

Timed-Elastic-Band Based Variable Splitting for Autonomous Trajectory Planning

Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination. In this article, we address autonomous trajectory planning problems, which aims at developing innovative solutions to enhance the adaptability and robustness of unmanned systems in navigating complex and dynamic environments. We first introduce the variable splitting (VS) method as a constrained optimization method to reimagine the renowned Timed-Elastic-Band (TEB) algorithm, resulting in a novel collision avoidance approach named Timed-Elastic-Band based variable splitting (TEB-VS). The proposed TEB-VS demonstrates superior navigation stability, while maintaining nearly identical resource consumption to TEB. We then analyze the convergence of the proposed TEB-VS method. To evaluate the effectiveness and efficiency of TEB-VS, extensive experiments have been conducted using TurtleBot2 in both simulated environments and real-world datasets

Timed-Elastic Bands for Manipulation Motion Planning

© 2019 IEEE. Motion planning is one of the main problems studied in the field of robotics. However, it is still challenging for the state-of-the-art methods to handle multiple conditions that allow better paths to be found. For example, considering joint limits, path smoothness and a mixture of Cartesian and joint-space constraints at the same time pose a significant challenge for many of them. This letter proposes to use timed-elastic bands for representing the manipulation motion planning problem, allowing to apply continuously optimized constraints to the problem during the search for a solution. Due to the nature of our method, it is highly extensible with new constraints or optimization objectives. The proposed approach is compared against state-of-the-art methods in various manipulation scenarios. The results show that it is more consistent and less variant, while performing in a comparable manner to that of the state of the art. This behavior allows the proposed method to set a lower-bound performance guarantee for other methods to build upon

Optimal-horizon model-predictive control with differential dynamic programming

We present an algorithm, based on the Differential Dynamic Programming framework, to handle trajectory optimization problems in which the horizon is determined online rather than fixed a priori. This algorithm exhibits exact one-step convergence for linear, quadratic, time-invariant problems and is fast enough for real-time nonlinear model-predictive control. We show derivations for the nonlinear algorithm in the discrete-time case, and apply this algorithm to a variety of nonlinear problems. Finally, we show the efficacy of the optimal-horizon model-predictive control scheme compared to a standard MPC controller, on an obstacle-avoidance problem with planar robots.Undergraduat

Trajectory optimization for mobile manipulator motion planning

State-of-the-art robotics research has been progressively focusing on autonomous robots that can operate in unconstrained environments and interact with people. Specifically, manipulation tasks in Ambient Assisted Living environments are complex, involving an unknown number of parameters. Recent years show a trend of successfully applied machine learning approaches affecting day-to-day life. Similar tendencies are perceivable in robotics, existing methods being enhanced with learning-based components. This thesis studies approaches for incorporating task-specific knowledge into the motion planning process that can be shared across a heterogeneous fleet of robots. A step towards data-driven strategies will allow the field to break away from manuallytweaked, heuristics- or state-machine-based solutions and provide good scaling properties, while maintaining operation safety around humans at a very high level. The presented work proposes a motion planning framework employing Learning from Demonstration to encode task-specific motions, facilitating skill-transfer and improving state-of-the-art in motion planning. Resulting algorithms are compared against other methods in a series of everyday tasks. While different optimisation methods have different benefits, it is possible to build them into systems that both generalise and scale well with the number of tasks and number of robot platforms. This thesis shows that optimisation-based planners are ideal for incorporating prior knowledge into a motion-planning system