Obround trees: Sparsity enhanced feedback motion planning of differential drive robotic systems

© 2021 Turkiye Klinikleri. All rights reserved.Robot motion planning & control is one of the most critical and prevalent problems in the robotics community. Even though original motion planning algorithms had relied on "open-loop" strategies and policies, researchers and engineers have been focusing on feedback motion planning and control algorithms due to the uncertainties, such as process and sensor noise of autonomous robotic applications. Recently, several studies proposed some robust feedback motion planning strategies based on sparsely connected safe zones. In this class of planning and control policies, local control policy inside a single zone computes and feeds the control actions that can drive the robot to a different connected region while guaranteeing that the robot never exceeds the boundaries of the active area until convergence. While most of these studies apply only to holonomic robotic models, a recent motion planning method (RCT) can solve the motion planning and navigation problems for unicycle like robotic systems based on a randomly connected circular region tree. In this paper, we propose a new/updated feedback motion planning algorithm that substantially enhances the sparsity, computational feasibility, and input effort compared to their methodology. The new algorithm generates a sparse neighborhood tree as a set of connected obround zones. Obround regions cover larger areas inside the environment, thus leads to a more sparse tree structure. During navigation, we modify the nonlinear control policy adopted in RCT method to handle the obround shaped zones. The feedback control policy navigates the robot model from one obround zone to the adjacent area in the tree structure, ensuring it stays inside the active region's boundaries and asymptotically reaches the connected obround. We demonstrate the effectiveness and validity of the algorithm on simulation studies. Our Monte Carlo simulations show that our enhancement to the original algorithm probabilistically improves the sparsity, and produces smoother trajectories compared to two motion planning algorithms that rely on sampling based neighborhood structures

An Efficient Implementation of Online Model Predictive Control With Field Weakening Operation in Surface Mounted PMSM

Model-predictive-controller (MPC), one of the optimal control policies, has gained more attention in servo drive and other industrial applications in recent years due to evident control performance benefits compared to more classical control methods. However, an MPC algorithm solves a constrained optimization problem at each step that brings a substantial computational burden over classical control policies. This study focuses on improving the computational efficiency of an online MPC algorithm and then demonstrates its practical feasibility on the field weakening operation in high-speed PMSM control applications where the sampling frequency is in the order of mu s. We implement the existing dual active set solver by replacing two standard methods in the matrix update step to reduce the overall computational cost of the algorithm. We also rearrange the linear approximation for the constraints on voltage and current by taking the tradeoff between accuracy and speed into account. We finally verify the efficiency and effectiveness of the proposed structure via processor-in-the-loop simulations and physical platform experiments

Variability in locomotor dynamics reveals the critical role of feedback in task control.

Animals vary considerably in size, shape, and physiological features across individuals, but yet achieve remarkably similar behavioral performances. We examined how animals compensate for morphophysiological variation by measuring the system dynamics of individual knifefish (Eigenmannia virescens) in a refuge tracking task. Kinematic measurements of Eigenmannia were used to generate individualized estimates of each fish's locomotor plant and controller, revealing substantial variability between fish. To test the impact of this variability on behavioral performance, these models were used to perform simulated 'brain transplants'-computationally swapping controllers and plants between individuals. We found that simulated closed-loop performance was robust to mismatch between plant and controller. This suggests that animals rely on feedback rather than precisely tuned neural controllers to compensate for morphophysiological variability

Altı bacaklı pronklama davranışının dinamik olarak gömülmüş yaylı ters sarkaç şablonu ile kontrolü.

Pronking is a legged locomotory gait in which all legs are used in synchrony, usually resulting in slow speeds but long flight phases and large jumping heights that may potentially be useful for mobile robots locomoting in cluttered natural environments. Instantiations of this gait for robotic systems suffer from severe pitch instability either due to underactuated leg designs, or the open-loop nature of proposed controllers. Nevertheless, both the kinematic simplicity of this gait and its dynamic nature suggest that the Spring-Loaded Inverted Pendulum Model (SLIP), a very successful predictive model for both natural and robotic runners, would be a good basis for more robust and maneuverable robotic pronking. In the scope of thesis, we describe a novel controller to achieve stable and controllable pronking for a planar, underactuated hexapod model, based on the idea of “template-based control”, a controller structure based on the embedding of a simple dynamical template within a more complex anchor system. In this context, high-level control of the gait is regulated through speed and height commands to the SLIP template, while the embedding controller based on approximate inverse-dynamics and carefully designed passive robot morphology ensures the stability of the remaining degrees of freedom. We show through extensive simulation experiments that unlike existing open-loop alternatives, the resulting control structure provides stability, explicit maneuverability and significant robustness against sensor noise.M.S. - Master of Scienc

Feedback motion planning of unmanned surface vehicles via random sequential composition

In this paper, we propose a new motion planning method that aims to robustly and computationally efficiently solve path planning and navigation problems for unmanned surface vehicles (USVs). Our approach is based on synthesizing two different existing methodologies: sequential composition of dynamic behaviours and rapidly exploring random trees (RRT). The main motivation of this integrated solution is to develop a robust feedback-based and yet computationally feasible motion planning algorithm for USVs. In order to illustrate the main approach and show the feasibility of the method, we performed simulations and tested the overall performance and applicability for future experimental applications. We also tested the robustness of the method under relatively extreme environmental uncertainty. Simulation results indicate that our method can produce robust and computationally feasible solutions for a broad class of USVs

Tork ile Harekete Geçen Tek Bacaklı Dağıtıcı Yaylı Kütle Modelinin Kontrolü

Yaylı Ters Sarkaç (YTS) gibi basit, yaylı kütle sistemlerinin, bacaklı hareket dinamiği için uygun bir model olduğu uzun zamandır bilinmektedir. Fakat bu alanda yapılmış çoğu çalışma, kayıpsız ve çok bacaklı modeller üzerinedir. Yapılan çalışmaların tek bacaklı modellerindeyse, bacağın gövdeye bağlandı- ğı nokta ile gövdenin kütle merkezi aynı yere konumlandırılmıştır. Bu bildiride, özgün, tek bacaklı, ayarlanabilir kalça momentiyle sönümlenen ve hareketle- nen, kütle merkezi ile bacak eklem noktası farklı olan bir YTS modeli sunmayı amaçlıyoruz. Daha sonra bu sistemin limit döngülerini tanımlamak için analitik bir yaklaşım geliştireceğiz. Bu çalışmada, hareketin kontrolü hız ve yükseklik verileri ile yapılırken, gömü- lü denetleyici, yaklaşık ters dinamik yöntemleri ve serbestlik derecelerinin pasif yapısal özelliklerle kontrol edilmesine dayanmaktadır. Bu kontrol yapısı ile tasarladığımız tek bacaklı, yaylı kütle modelinin, çok bacaklı modeller kadar iyi bir manevra kabiliyeti ve dengeye sahip olacağını, yaptığımız ve devam ede- ceğimiz simülasyonlar ile kanıtlayacağız. Sonuç olarak, daha önce elde edil- memiş bir hareket performansına ulaşacağız

Feedback Motion Planning For a Dynamic Car Model via Random Sequential Composition

Autonomous cars and car-like robots have gained huge popularity recently due to the recent advancements in technology and AI industry. Motion and path planning is one of the most fundamental problems for such systems. In the literature, kinematic models are widely adopted for planning and control for these type of robots due to their simplicity (control and analysis) and fewer computational requirements. Though, applicability of kinematic models are limited to very low speeds or some specific cases, which can be easily violated in real life scenarios. Furthermore, most of the dynamical car models found in the literature assume that they are driven only in forward direction, at constant high speeds. In this study, we present a car model that captures the dynamics of both forward and backward driving, at low and high speeds. After creating the car model, we addressed the motion planning problem on this model, where we adopted a framework which combines Sequential Composition of Controllers (SCC) and Rapidly Exploring Random Trees (RRT). We performed simulations to show the effectiveness and robustness of our method, and results are promising for future experimental studies

Stride-to-stride energy regulation for robust self-stability of a torque-actuated dissipative spring-mass hopper

In this paper, we analyze the self-stability properties of planar running with a dissipative spring-mass model driven by torque actuation at the hip. We first show that a two-dimensional, approximate analytic return map for uncontrolled locomotion with this system under a fixed touchdown leg angle policy and an open-loop ramp torque profile exhibits only marginal self-stability that does not always persist for the exact system. We then propose a per-stride feedback strategy for the hip torque that explicitly compensates for damping losses, reducing the return map to a single dimension and substantially improving the robust stability of fixed points. Subsequent presentation of simulation evidence establishes that the predictions of this approximate model are consistent with the behavior of the exact plant model. We illustrate the relevance and utility of our model both through the qualitative correspondence of its predictions to biological data as well as its use in the design of a task-level running controller. (c) 2010 American Institute of Physics. [doi: 10.1063/1.3486803