Car-like Robot Research Articles

Learning Terrain Traversability for a Mobile Robot based on Information Fusion

In this work, we propose an approach to determine terrain traversability for a car-like robot. Our approach has two main modules: a neural network classifier that makes use of sensors' readings to assign traversability levels to control inputs of the robot, and a second neural network that, based on the outputs of the first network, mimics the control selection performed by a human driver. The approach incorporates sensor fusion from a variety of sources to enhance the traversability estimation, and it is trained employing a semi-supervised learning scheme with examples resulting from the interaction of the car with the environment. This semi-supervised scheme avoids exhausting manual labeling and is built on the premise that there is a correlation between the terrain traversability and the required and observed behaviors of the vehicle. The method is validated with data obtained from a physical electric car.

A Review on Sensor Technologies and Control Methods for Mobile Robot with Obstacle Detection System

Obstacle detection system is a system that reacts to the object in the path and perform action such as stopping robot movement and collision prevention according to the design of algorithm which enhance the safety level of robot. This paper examines the overview of sensor technology that associates with obstacle detection system and car-like robot. This review summarizes the effectiveness and weakness of common type of sensors such as lidar, radar, ultrasonic sensor, infrared sensor, computer vision, sensor fusion and sensor array. This paper will also discuss on control methods for car-like robot that includes hand gestures, voice control, infrared remote control, Android based Bluetooth mobile control, and Wi-Fi based mobile control, outlining the effectiveness and limitation of each control method.

Non-linearity Measure for POMDP-based Motion Planning

Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies on a large number of forward simulations. For systems with complex dynamics, this generally requires costly numerical integrations, which significantly slows down the planning process. Linearization-based methods have been proposed that can alleviate the above problem. However, it is not clear how linearization affects the quality of the generated motion strategy, and when such simplifications are admissible. To answer these questions, we propose a non-linearity measure, called Statistical-distance-based Non-linearity Measure (SNM), that can identify where linearization is beneficial and where it should be avoided. We show that when the problem is framed as the Partially Observable Markov Decision Process, the value difference between the optimal strategy for the original model and the linearized model can be upper-bounded by a function linear in SNM. Comparisons with an existing measure on various scenarios indicate that SNM is more suitable in estimating the effectiveness of linearization-based solvers. To test the applicability of SNM in motion planning, we propose a simple online planner that uses SNM as a heuristic to switch between a general and a linearization-based solver. Results on a car-like robot with second order dynamics and 4-DOFs and 7-DOFs torque-controlled manipulators indicate that SNM can appropriately decide if and when a linearization-based solver should be used.

PiP-X: Online feedback motion planning/replanning in dynamic environments using invariant funnels

Computing kinodynamically feasible motion plans and repairing them on-the-fly as the environment changes is a challenging, yet relevant problem in robot navigation. We propose an online single-query sampling-based motion re-planning algorithm using finite-time invariant sets, commonly referred to as “ funnels”. We combine concepts from nonlinear systems analysis, sampling-based techniques, and graph-search methods to create a single framework that enables feedback motion re-planning for any general nonlinear dynamical system in dynamic workspaces. A volumetric network of funnels is constructed in the configuration space using sampling-based methods and invariant set theory; and an optimal sequencing of funnels from robot configuration to a desired goal region is then determined by computing the shortest-path subgraph (tree) in the network. Analyzing and formally quantifying the stability of trajectories using Lyapunov level-sets ensures kinodynamic feasibility and guaranteed set-invariance of the solution paths. Though not required, our method is capable of using a pre-computed library of motion primitives to speedup online computation of controllable motion plans that are volumetric in nature. We introduce a novel directed-graph data structure to represent the funnel-network and its inter-sequencibility; helping us leverage discrete graph-based incremental search to quickly rewire feasible and controllable motion plans on-the-fly in response to changes in the environment. We validate our approach on a simulated cart-pole, car-like robot, and 6DOF quadrotor platform in a variety of scenarios within a maze and a random forest environment. Using Monte Carlo methods, we evaluate the performance in terms of algorithm success, length of traversed trajectory, and runtime.

A Secure Robot Learning Framework for Cyber Attack Scheduling and Countermeasure

The problem of learning-based control for robots has been extensively studied, whereas the security issue under malicious adversaries has not been paid much attention to. Malicious adversaries can invade intelligent devices and communication networks used in robots, causing incidents, achieving illegal objectives, and even injuring people. This article first investigates the problems of optimal false data injection attack scheduling and countermeasure design for car-like robots in the framework of deep reinforcement learning. Using a state-of-the-art deep reinforcement learning approach, an optimal false data injection attack scheme is proposed to deteriorate the tracking performance of a robot, guaranteeing the tradeoff between the attack efficiency and the limited attack energy. Then, an optimal tracking control strategy is learned to mitigate attacks and recover the tracking performance. More importantly, a theoretical stability guarantee of a robot using the learning-based secure control scheme is achieved. Both simulated and real-world experiments are conducted to show the effectiveness of the proposed schemes.

Visual-RRT: Integrating IBVS as a steering method in an RRT planner

In this paper, we present a new approach to robot motion planning that anticipates the use of vision-based feedback control during task execution. We accomplish this by incorporating an image-based visual servo (IBVS) controller directly into the steering function used by a Rapidly Exploring Random Tree (RRT) planner. Our approach requires a number of extensions to traditional RRT-style planning. First, we derive a new sampling strategy that augments the usual state information by including image features that will be used by the IBVS control law. These augmented samples are then used by our new IBVS steering function, which simulates an IBVS control law to generate local trajectories that extend the current tree. These trajectories must be validated to ensure that they are collision-free and that all image features remain unoccluded and within the camera field of view throughout the local trajectory. We also provide a formal proof showing that the proposed approach is probabilistically complete. We have applied our approach to the problem of planning trajectories for three different systems: a robotic arm, an unmanned aerial vehicle (UAV) and a car-like robot, which are equipped with an IBVS control law. We explore performance trade-offs in the control design via simulation studies and demonstrate real-world effectiveness via experiments in which a small-scale car-like robot uses IBVS to navigate a track that includes a number of obstacles and potential occlusions. By exploring performance trade-offs, we mean that several elements, such as the metric used to identify nearest neighbors in the RRT and the steering method used to generate nodes, are tested and compared.

Trajectory tracking nonlinear [formula omitted] controller for wheeled mobile robots with disturbances observer

Wheeled Mobile Robots (WMRs) are systems with multiple industrial and civilian applications. Trajectory tracking is essential in many applications, such as surveillance, monitoring, and autonomous driving. However, in practical applications, a WMR is always affected by kinematic disturbances, state estimation error, and measurement noise, which may diminish the system’s performance. Hence, this work proposes a novel observer-based H∞ controller that is robust against matched and unmatched disturbances. The proposed methodology compensates for disturbances through a disturbance observer, transforming the closed-loop system into a new one affected by uniformly bounded disturbances. Then, an H∞ controller is designed to make the WMR track a desired reference signal. A formal stability proof demonstrates the feasibility of the new proposal. Also, feedback and finite-time controllers are used to assess the novel controller. Numerical simulations and experimental results with a scaled autonomous car-like robot demonstrate the novel controller’s efficiency and outstanding performance, despite disturbances when compared against finite-time and feedback controllers.

A Travelling Salesman Problem Approach to Efficiently Navigate Crop Row Fields with a Car-Like Robot

In recent years, interest in the use of mobile robots in the agricultural industry has increased, both to address labor shortages in rural areas and to increase food production in a more sustainable way. In order to have an efficient navigation system to cover long crop row fields, a path planner algorithm must consider maneuvering restrictions of the targeted robot. Most state-of-the-art works in agricultural navigation systems are intended for robots with a high degree of maneuverability that can typically make in-place turnings. This work aims to fill the gap in terms of the development of an efficient navigation system for car-like robots with limited turning radius in crop row fields. For this, we combine the global path planner A* and the local trajectory planner Timed Elastic Band (TEB). Additionally, we state the problem of finding an optimal path that covers the entire field as a Travelling Salesman Problem (TSP) that is based on the different turning maneuvers the robot can perform at field headlands. The solution of the TSP results in a time efficient coverage strategy that aligns with the robot's kinematics. Experiments performed in the Gazebo simulation environment show a reduction in field completion times of up to 20%, compared to trivial coverage paths. On the other hand, deviation of the robot with respect to the center of the field furrows was in all cases less than 10cm, which proves that the entire system operates with sufficient accuracy to avoid damaging the crops.

RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments

Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in both simulation and real-world settings. Experimental results show that the proposed method generates smooth collision-free trajectories with less computation time compared with other benchmarks and performs robustly in cluttered environments. The source code is available at https://github.com/hanruihua/RDA_planner.

Fisheye-based visual aligned regulation for wheeled mobile robots

In this paper, a fisheye-based visual servo regulation method is proposed for wheeled mobile robots, which utilizes motion planning algorithms to provide trajectories for driving the robot to desired poses in an aligned pattern. To satisfy the wide field-of-view (FOV) requirement in robotic systems, the image projection model of fisheye is analyzed, and derivation and estimation of homography for fisheye vision are implemented to facilitate robotic pose calculation. Compared with traditional visual regulation schemes, motion planning and trajectory tracking techniques are combined to make mobile robots reach given poses in the frontal aligned style, which guarantees the robot to move smoothly and is extendable such as for car-like robots. Desired image trajectories are calculated reversely by the fisheye model with respect to the planned geometric path, and then a tracking controller is employed for maneuvering the mobile robot. Simulation and comparative experimental results are collected to verify effectiveness of the proposed strategy.

Spatial Path Smoothing for Car-like Robots Using Corridor-Based Quadratic Optimization

The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be a high quality one. Two novel designs contribute to the proposed approach. One is a direct corridor construction method that provides an optimization region for path optimization. Based on a redefined path representation in the generated corridor, the second one is a core spatial path optimization method, where the optimization problem is formulated as a multi-objective quadratic programming (QP) with corridor and maximum-curvature constraints. Meanwhile, integrating a two-steps strategy into an optimization process yields a good trade-off between efficiency and quality. Experiment results validate that the proposed approach has the ability to online generate a high quality path.

Coordinated Cooperative Distributed Decision-Making Using Synchronization of Local Plans

Centralized decision-making for a Networked Control System (NCS) suffers from a high computational burden on the planning agent. Distributed agents, which compute cooperative decision-making, increase computational performance. In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans are inconsistent with the local plans of other agents. This inconsistency leads to the infeasibility of plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making. The algorithm enables parallel decision-making and achieves feasible decisions. The algorithm consists of two iterative steps: local planning and global synchronization. In the local planning step, the agents parallelly compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using global synchronization. The globally synchronized plans act as reference decisions for the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The parallel algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that globally feasible decisions are achieved.

Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles

This letter addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles – a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub-problems and combining the local solutions into the global one. The crux of any planning method for non-holonomic robots is the generation of motion primitives that generates solutions to local planning sub-problems. In this work we introduce a novel learnable steering function (policy), which takes into account kinodynamic constraints of the robot and both static and dynamic obstacles. This policy is efficiently trained via the policy optimization. Empirically, we show that our steering function generalizes well to unseen problems. We then plug in the trained policy into the sampling-based and lattice-based planners, and evaluate the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion Primitives) in a range of challenging setups that involve a car-like robot operating in the obstacle-rich parking-lot environments. We show that POLAMP is able to plan collision-free kinodynamic trajectories with success rates higher than 92%, when 50 simultaneously moving obstacles populate the environment showing better performance than the state-of-the-art competitors.

The Use of Hough Transform Method and Knot-Like Turning for Motion Planning and Control of an Autonomous Agricultural Vehicle

This study focuses on motion planning and reference trajectory tracking control of an autonomous agricultural vehicle to achieve precise row following and turning. The smooth time-varying feedback control method was adapted to the system to generate the required control commands. The mathematical representations for motion planning and controllers were constructed based on the car-like robot model. An algorithm to detect trees and rows of trees of an orchard was developed using the Hough transform approach. A new type of turning procedure, called knot-like turning, was proposed to perform turning from one row to another. A simulation environment was created to test and analyze the developed system. To obtain the real data from a field, the trees and rows of trees of a cherry orchard were scanned using a laser scanner rangefinder sensor. Then, the scanned data were moved to the simulation environment to generate the desired trajectory, which was followed by an autonomous agricultural vehicle. The simulation environment made it possible to determine the performance of the proposed motion planning, reference trajectory generation, tracking control and turning procedures. The results presented here indicate that the proposed methodology could be used for desired trajectory tracking tasks for agricultural operations in the case that minimum tracking errors in both straight and turning motions are needed.

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions.

An Optimization-Based Motion Planner for Car-like Logistics Robots on Narrow Roads.

Thanks to their strong maneuverability and high load capacity, car-like robots with non-holonomic constraints are often used in logistics to improve efficiency. However, it is difficult to plan a safe and smooth optimal path in real time on the restricted narrow roads of the logistics park. To solve this problem, an optimization-based motion planning method inspired by the Timed-Elastic-Band algorithm is proposed, called Narrow-Roads-Timed-Elastic-Band (NRTEB). Three optimization modules are added to the inner and outer workflow of the Timed-Elastic-Band framework. The simulation results show that the proposed method achieves safe reversing planning on narrow roads while the jerk of the trajectory is reduced by 72.11% compared to the original method. Real-world experiments reveal that the proposed method safely and smoothly avoids dynamic obstacles in real time when navigating forward and backward. The motion planner provides a safer and smoother trajectory for car-like robots on narrow roads in real time, which greatly enhances the safety, robustness and reliability of the Timed-Elastic-Band planner in logistics parks.

EKF-DWA for car-like robot navigation in presence of moving obstacles

EKF-DWA for car-like robot navigation in presence of moving obstacles

Online on-Road Motion Planning Based on Hybrid Potential Field Model for Car-Like Robot.

The application of Middle-sized Car-like Robots (MCRs) in indoor and outdoor road scenarios is becoming broader and broader. To achieve the goal of stable and efficient movement of the MCRs on the road, a motion planning algorithm based on the Hybrid Potential Field Model (HPFM) is proposed in this paper. Firstly, the artificial potential field model improved with the eye model is used to generate a safe and smooth initial path that meets the road constraints. Then, the path constraints such as curvatures and obstacle avoidance are converted into an unconstrained weighted objective function. The efficient least-squares & quasi-Newton fusion algorithm is used to optimize the initial path to obtain a smooth path curve suitable for the MCR. Finally, the speed constraints are converted into a weighted objective function based on the path curve to get the best speed profile. Numerical simulation and practical prototype experiments are carried out on different road scenes to verify the performance of the proposed algorithm. The results show that re-planned trajectories can satisfy the path constraints and speed constraints. The real-time re-planning period is 184 ms, which demonstrates the proposed approach’s effectiveness and feasibility.

Performance Evaluation of Remote Monitoring Car-like Mobile Robot System with Grey Prediction Model

Performance Evaluation of Remote Monitoring Car-like Mobile Robot System with Grey Prediction Model

Mobile robot path planning using fuzzy enhanced improved Multi-Objective particle swarm optimization (FIMOPSO)

This paper introduces a method for car-like mobile robot path planning (CRPP). The robot works in both dynamic and static situations. The aim of this method is to explore the best safe path with minimum path length, minimum motor torque, minimum travel time, minimum robot acceleration and maximum obstacle avoidance. Kinodynamic and non-holonomic constraints related with car-like robot are considered. Fuzzy enhanced Improved Multi-objective Particle Swarm Optimization (FIMOPSO) algorithm is proposed to solve the CRPP problem. Fuzzy inference system is used for obstacle avoidance. In the proposed FIMOPSO, five improvements are made. Proposed technique is compared with Multi-objective Strength Pareto Evolutionary Algorithm 2 (MOSPEA2) technique. Experiments on a custom-made car-like robot are ensuring the quality of proposed technique. This research works show that proposed FIMOPSO is another alternative technique to CRPP problems. Paths dictated by FIMOPSO are safe, collision free, feasible, and possible and can be practically implemented. Fuzzy inference system works well for safe robot travel. FIMOPSO simulation paths are acceptable. Since, the deviation between experiment and simulation is less than 2%.