This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices

A survey of deep learning techniques for autonomous driving

In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

https://journals.sagepub.com/doi/pdf/10.5772/57313

A Survey and Analysis of Multi-Robot Coordination

We present a novel, deeply embedded robotics middleware and programming environment. It uses a multithreaded, publish-subscribe design pattern and provides a Unix-like software interface for micro controller applications. We improve over the state of the art in deeply embedded open source systems by providing a modular and standards-oriented platform. Our system architecture is centered around a publish-subscribe object request broker on top of a POSIX application programming interface. This allows to reuse common Unix knowledge and experience, including a bash-like shell. We demonstrate with a vertical takeoff and landing (VTOL) use case that the system modularity is well suited for novel and experimental vehicle platforms. We also show how the system architecture allows a direct interface to ROS and to run individual processes either as native ROS nodes on Linux or nodes on the micro controller, maximizing interoperability. Our microcontroller-based execution environment has substantially lower latency and better hardware connectivity than a typical Robotics Linux system and is therefore well suited for fast, high rate control tasks.

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PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependencies, temporal dependencies, and exogenous dependencies need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependencies within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. The experimental results, validated on the real-world data provided by DiDi Chuxing, show that the FCL-Net achieves the better predictive performance than traditional approaches including both classical time-series prediction models and state-of-art machine learning algorithms (e.g., artificial neural network, XGBoost, LSTM and CNN). Furthermore, the consideration of exogenous variables in addition to the passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since they reduce the root mean squared error (RMSE) by 48.3%. It is also interesting to find that the feature selection reduces 24.4% in the training time and leads to only the 1.8% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.

Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

High-end vehicles are already equipped with safety systems, such as assistive braking and automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help in low-complexity driving situations. In this paper, we introduce a parallel autonomy , or shared control, framework that computes safe trajectories for an automated vehicle, based on human inputs. We minimize the deviation from the human inputs while ensuring safety via a set of collision avoidance constraints. Our method achieves safe motion even in complex driving scenarios, such as those commonly encountered in an urban setting. We introduce a receding horizon planner formulated as nonlinear model predictive control (NMPC), which includes the analytic descriptions of road boundaries and the configuration and future uncertainties of other road participants. The NMPC operates over both steering and acceleration simultaneously. We introduce a nonslip model suitable for handling complex environments with dynamic obstacles, and a nonlinear combined slip vehicle model including normal load transfer capable of handling static environments. We validate the proposed approach in two complex driving scenarios. First, in an urban environment that includes a left-turn across traffic and passing on a busy street. And second, under snow conditions on a race track with sharp turns and under complex dynamic constraints. We evaluate the performance of the method with various human driving styles. We consequently observe that the method successfully avoids collisions and generates motions with minimal intervention for parallel autonomy. We note that the method can also be applied to generate safe motion for fully autonomous vehicles.

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Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

A method for providing shared control over movement of a vehicle within a space. The method includes receiving user input related to a velocity and a direction for the vehicle within the space. The method includes processing the user input to selectively adjust the velocity and the direction desired by the user based on a set of predefined constraints to generate a trajectory for the vehicle for an upcoming time period. The method includes operating drive mechanisms in the vehicle based on the trajectory to move the vehicle from a first position to a second position within the space during the upcoming time period. A grid map defining locations of obstacles in the space may be used to define the trajectory to avoid collisions, and a guidance trajectory may be used to further control movement to achieve a desired throughput and control vehicle movement within particular portions of the space.

Shared control of semi-autonomous vehicles including collision avoidance in multi-agent scenarios

A method of docking and recharging using a base station and a station-mating frame on the multicopter. The base station includes an upward-facing camera that is used by a docking controller to detect the presence, position, and orientation of a frame, with infrared light-emitting diodes arranged in a predefined pattern. The controller of the base station acts to emit wireless signals to the multicopter to guide the multicopter with its station-mating frame to a predefined position above the base station. The controller transmits a wireless signal to the multicopter to reduce thrust, and the multicopter lowers itself onto a sloped receiving surface that may be arranged in a crown pattern to provide passive gravity-driven centering, which causes the station-mating frame to slide to a lowest vertical point of the receiving assembly. A locking mechanism engages to lock the frame in place and provide electrical contact for recharging.

Robust and autonomous docking and recharging of quadrotors

This paper describes work on multi-robot pattern formation. Arbitrary target patterns are represented with an optimal robot deployment, using a method that is independent of the number of robots. Furthermore, the trajectories are visually appealing in the sense of being smooth, oscillation free, and showing fast convergence. A distributed controller guarantees collision free trajectories while taking into account the kinematics of differentially driven robots. Experimental results are provided for a representative set of patterns, for a swarm of up to ten physical robots, and for fifty virtual robots in simulation.

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Multi-robot system for artistic pattern formation

This paper addresses decentralized motion planning among a homogeneous set of feedback-controlled, decision-making agents. It introduces the continuous control obstacle ( Cn-CO), which describes the set of Cn-continuous control sequences (and thus trajectories) that lead to a collision between interacting agents. By selecting a feasible trajectory from Cn-CO's complement, a collision-free motion is obtained. The approach represents an extension to the reciprocal velocity obstacle (RVO, ORCA) collision-avoidance methods so that trajectory segments verify Cn continuity rather than piecewise linearity. This allows the large class of robots capable of tracking Cn-continuous trajectories to employ it for partial motion planning directly-rather than as a mere tool for collision checking. This paper further establishes that both the original velocity obstacle method and several of its recently developed reciprocal extensions (which treat specific robot physiologies only) correspond to particular instances of Cn-CO. In addition to the described extension in trajectory continuity, Cn-CO thus represents a unification of existing RVO theory. Finally, the presented method is validated in simulation-and a parameter study reveals under which environmental and control conditions Cn-CO with admits significantly improved navigation performance compared with inflated approaches based on ORCA.

Javier Alonso-Mora

Papers

Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

Shared control of semi-autonomous vehicles including collision avoidance in multi-agent scenarios

Robust and autonomous docking and recharging of quadrotors

Multi-robot system for artistic pattern formation

Reciprocal Collision Avoidance With Motion Continuity Constraints