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On the truck dispatching problem

where xt+1 is the position and orientation of the robot (with respect to a reference frame) at time t + 1, with (ξ, η) giving the x and y coordinates and θ the angle (with respect to the x-axis) that the robot is facing. The robot has two drive wheels, of radius r on an axle of length d. During time period t the right wheel is believed to rotate at a velocity of φRt and the left at a velocity of φLt. In this example, these velocities are fixed with φRt = 0.4 and φLt = 0.1. The state update function, F , calculates where the robot should be at each time point, given its previous position. However, in reality, there is some random fluctuation in the velocity of the wheels, for example, due to slippage. Therefore the actual position of the robot and the position given by equation F will differ. The magnitude of these random fluctuations is included in the model through Σx.

Unscented Kalman Filterの計測への応用に関する研究

This paper implements robust decentralized model predictive control (DMPC) for a team of cooperating uninhabited aerial vehicles (UAVs). The problem involves vehicles with independent dynamics but with coupled constraints to capture required cooperative behavior. Using a recently-developed form of DMPC, each vehicle plans only for its own actions, but feasibility of the sub-problems and satisfaction of the coupling constraints are guaranteed throughout, despite the action of unknown but bounded disturbances. UAVs communicate relevant plan data to ensure that decisions are consistent across the team. Collision avoidance is used as an example of coupled constraints and the paper shows how the speed, turn rate and avoidance distance limits in the optimization should be modified in order to guarantee robust constraint satisfaction. Integer programming is used to solve the non-convex problem of path-planning subject to avoidance constraints. Numerical simulations compare computation time and target arrival time under decentralized and centralized control and investigate the impact of decentralization on team performance. The results show that the computation required for DMPC is significantly lower than for its centralized counterpart and scales better with the size of the team, at the expense of only a small increase in UAV flight times.

Decentralized model predictive control of co-operating UAVs

Unmanned Aerial Vehicles, commonly known as drones, have attained considerable interest in recent years due to the potential of revolutionizing transport and logistics. Amazon were among the first to introduce the idea of using drones to deliver goods, followed by several other distribution companies working on similar services. The Traveling Salesman Problem, frequently used for planning last-mile delivery operations, can easily be modified to incorporate drones, resulting in a routing problem involving both the truck and aircraft. Introduced by Murray and Chu (2015) , the Flying Sidekick Traveling Salesman Problem considers a drone and truck collaborating. The drone can be launched and recovered at certain visits on the truck route, making it possible for both vehicles to deliver goods to customers in parallel. This generalization considerably decreases the operational cost of the routes, by reducing the total fuel consumption for the truck, as customers on the routes can be serviced by drones without covering additional miles for the trucks, and hence increase productivity. In this paper a mathematical model is formulated, defining a problem similar to the Flying Sidekick Traveling Salesman Problem, but for the capacitated multiple-truck case with time limit constraints and minimizing cost as objective function. The corresponding problem is denoted the Vehicle Routing Problem with Drones. Due to the difficulty of solving large instances to optimality, an Adaptive Large Neighborhood Search metaheuristic is proposed. Finally, extensive computational experiments are carried out. The tests investigate, among other things, how beneficial the inclusion of the drone-delivery option is compared to delivering all items using exclusively trucks. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.

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An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones

Mobile robot pose tracking is mostly based on odometry. However, with time, odometric pose tracking accumulates errors in an unbounded fashion. This paper describes a way to decrease the odometry error by using an extended Kalman filter (EKF) for fusion of calibrated odometry data and sonar readings. Common approaches for calibrated odometry and sonar fusion use a feature based map which has two uncertainties in the measurement process. One uncertainty is related to the sonar range reading and the other one to the feature/range reading assignment. Our approach is adapted to an occupancy grid map which has only the sonar range reading uncertainty in the measured process. Experimental results on the mobile robot Pioneer 2DX show improved accuracy of the pose estimation compared to the calibrated odometry.

Extended Kalman filter based mobile robot pose tracking using occupancy grid maps

The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities’ sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions.

Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions

Odometry is a widely used method for estimation of the momentary pose of a mobile robot with respect to its starting pose. It provides easily accessible real-time pose information between periodic absolute pose measurements or between pose corrections using additional sensors. Odometric localization accumulates errors in an unbounded fashion with quadratic increase of error variance with traversed distance. Odometry errors consist of systematic and non-systematic parts. While non-systematic errors cannot be predicted and therefore compensated, systematic errors can be compensated by means of calibration, which can be off-line and on-line. This paper describes an approach to off-line odometry calibration for differential drive mobile robots, which is based on simple experiments combined with optimization methods. Two variants of the proposed calibration method are examined: one with 3 calibration parameters and other one with 2 calibration parameters. Experimental results obtained using Pioneer 2 DX differential drive mobile robot show that both methods significantly increase accuracy of the pose estimation. Due to simplicity and suitability for later on-line adaptation, calibration method with two parameters is preferred.

Simple off-line Odometry Calibration of Differential Drive Mobile Robots

Robust and accurate global vision system for real time tracking of multiple mobile robots

In order to perform useful tasks the mobile robot's current pose must be accurately known. Problem of finding and tracking the mobile robot's pose is called localization, and can be global or local. In this paper we address local localisation or mobile robot pose tracking with prerequisites of known starting pose, robot kinematic and world model. Pose tracking is mostly based on odometry, which has the problem of accumulating errors in an unbounded fashion. To overcome this problem sensor fusion is commonly used. This paper describes two methods for calibrated odometry and sonar sensor fusion based on Kalman filter theory and occupancy grid maps as used world model. Namely, we compare the pose tracking or pose estimation performances of both most commonly used nonlinear-model based estimators: extended and unscented Kalman filter. Since occupancy grid maps are used, only sonar range measurement uncertainty has to be considered, unlike feature based maps where an additional uncertainty regarding the feature/range reading assignment must be considered. Thus the numerical complexity is reduced. Experimental results on the Pioneer 2DX mobile robot show similar and improved accuracy for both pose estimation techniques compared to simple odometry.

Edouard Ivanjko

Papers

Extended Kalman filter based mobile robot pose tracking using occupancy grid maps

Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions

Simple off-line Odometry Calibration of Differential Drive Mobile Robots

Robust and accurate global vision system for real time tracking of multiple mobile robots

Kalman filter theory based mobile robot pose tracking using occupancy grid maps