A New Approach towards Non-holonomic Path Planning of Car-like Robots using Rapidly Random Tree Fixed Nodes(RRT*FN)

Autonomous car driving is gaining attention in industry and is also an ongoing research in scientific community. Assuming that the cars moving on the road are all autonomous, this thesis introduces an elegant approach to generate non-holonomic collision-free motion of a car connecting any two poses (configurations) set by the user. Particularly this thesis focusses research on "path-planning" of car-like robots in the presence of static obstacles. Path planning of car-like robots can be done using RRT and RRT*. Instead of generating the non-holonomic path between two sampled configurations in RRT, our approach finds a small incremental step towards the next random configuration. Since the incremental step can be in any direction we use RRT to guide the robot from start configuration to end configuration. This "easy-to-implement" mechanism provides flexibility for enabling standard plan- ners to solve for non-holonomic robots without much modifications. Thus, strength of such planners for car path planning can be easily realized. This thesis demon- strates this point by applying this mechanism for an effective variant of RRT called as RRT - Fixed Nodes (RRT*FN). Experiments are conducted by incorporating our mechanism into RRT*FN (termed as RRT*FN-NH) to show the effectiveness and quality of non-holonomic path gener- ated. The experiments are conducted for typical benchmark static environments and the results indicate that RRT*FN-NH is mostly finding the feasible non-holonomic solutions with a fixed number of nodes (satisfying memory requirements) at the cost of increased number of iterations in multiples of 10k. Thus, this thesis proves the applicability of mechanism for a highly constrained planner like RRT*-FN, where the path needs to be found with a fixed number of nodes. Although, comparing the algorithm (RRT*FN-NH) with other existing planners is not the focus of this thesis there are considerable advantages of the mechanism when applied to a planner. They are a) instantaneous non-holonomoic path generation using the strengths of that particular planner, b) ability to modify on-the-fly non-holomic paths, and c) simple to integrate with most of the existing planners. Moreover, applicability of this mechanism using RRT*-FN for non-holonomic path generation of a car is shown for a more realistic urban environments that have typical narrow curved roads. The experiments were done for actual road map obtained from google maps and the feasibility of non-holonomic path generation was shown for such environments. The typical number of iterations needed for finding such feasible solutions were also in multiple of 10k. Increasing speed profiles of the car was tested by limiting max speed and acceleration to see the effect on the number of iterations

Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement

In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error. This enables a higher accuracy in the initial part of the receding horizon, which is more relevant to NMPC, while keeping bounded the number of discretization points. By combining this feature with an efficient Least Square formulation, our solver is also extremely time-efficient, generating trajectories of multiple seconds within only a few milliseconds. The performance of the proposed approach has been validated in a high fidelity simulation environment, by using an UAV platform. We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR 2018

When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning

The hierarchy of global and local planners is one of the most commonly utilized system designs in autonomous robot navigation. While the global planner generates a reference path from the current to goal locations based on the pre-built static map, the local planner produces a kinodynamic trajectory to follow the reference path while avoiding perceived obstacles. To account for unforeseen or dynamic obstacles not present on the pre-built map, ``when to replan'' the reference path is critical for the success of safe and efficient navigation. However, determining the ideal timing to execute replanning in such partially unknown environments still remains an open question. In this work, we first conduct an extensive simulation experiment to compare several common replanning strategies and confirm that effective strategies are highly dependent on the environment as well as the global and local planners. Based on this insight, we derive a new adaptive replanning strategy based on deep reinforcement learning, which can learn from experience to decide appropriate replanning timings in the given environment and planning setups. Our experimental results demonstrate that the proposed replanner can perform on par or even better than the current best-performing strategies in multiple situations regarding navigation robustness and efficiency.Comment: 7 pages, 3 figure

Modeling and Control Strategies for a Two-Wheel Balancing Mobile Robot

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic model for the mobile robot. The robot dynamics is derived using the classical Euler-Lagrange method, where motion can be described using potential and kinetic energies of the bodies. Non-holonomic constraints are included in the model to achieve desired motion, such as non-drifting of the mobile robot. These non-holonomic constraints are included using the method of Lagrange multipliers. Navigation for the robot is developed using artificial potential field path planning to generate a map of velocity vectors that are used for the set points for linear velocity and yaw rate. The second part of the thesis focuses on developing and evaluating three different control strategies for the mobile robot: PID controllers, Hierarchical Sliding Mode Control, and Deep-Q-Learning. The performances of the different control strategies are evaluated and compared based on various metrics, such as stability, robustness to mass variations and disturbances, and tracking accuracy. The implementation and evaluation of these strategies are modeled tested in a MATLAB/SIMULINK virtual environment

Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation

Modeling and Control Strategies for a Two-Wheel Balancing Mobile Robot

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic model for the mobile robot. The robot dynamics is derived using the classical Euler-Lagrange method, where motion can be described using potential and kinetic energies of the bodies. Non-holonomic constraints are included in the model to achieve desired motion, such as non-drifting of the mobile robot. These non-holonomic constraints are included using the method of Lagrange multipliers. Navigation for the robot is developed using artificial potential field path planning to generate a map of velocity vectors that are used for the set points for linear velocity and yaw rate. The second part of the thesis focuses on developing and evaluating three different control strategies for the mobile robot: PID controllers, Hierarchical Sliding Mode Control, and Deep-Q-Learning. The performances of the different control strategies are evaluated and compared based on various metrics, such as stability, robustness to mass variations and disturbances, and tracking accuracy. The implementation and evaluation of these strategies are modeled tested in a MATLAB/SIMULINK virtual environment