Javier Alonso-Mora

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment.

Significance Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min. Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

Image and animation display with multiple mobile robots

In this article we present a novel display that is created using a group of mobile robots. In contrast to traditional displays that are based on a fixed grid of pixels, such as a screen or a projection, this work describes a display in which each pixel is a mobile robot of controllable color. Pixels become mobile entities, and their positioning and motion are used to produce a novel experience. The system input is a single image or an animation created by an artist. The first stage is to generate physical goal configurations and robot colors to optimally represent the input imagery with the available number of robots. The run-time system includes goal assignment, path planning and local reciprocal collision avoidance, to guarantee smooth, fast and oscillation-free motion between images. The algorithms scale to very large robot swarms and extend to a wide range of robot kinematics. Experimental evaluation is done for two different physical swarms of size 14 and 50 differentially driven robots, and for simulations with 1,000 robot pixels.

Collision avoidance for aerial vehicles in multi-agent scenarios

This article describes an investigation of local motion planning, or collision avoidance, for a set of decision-making agents navigating in 3D space. The method is applicable to agents which are heterogeneous in size, dynamics and aggressiveness. It builds on the concept of velocity obstacles (VO), which characterizes the set of trajectories that lead to a collision between interacting agents. Motion continuity constraints are satisfied by using a trajectory tracking controller and constraining the set of available local trajectories in an optimization. Collision-free motion is obtained by selecting a feasible trajectory from the VO’s complement, where reciprocity can also be encoded. Three algorithms for local motion planning are presented—(1) a centralized convex optimization in which a joint quadratic cost function is minimized subject to linear and quadratic constraints, (2) a distributed convex optimization derived from (1), and (3) a centralized non-convex optimization with binary variables in which the global optimum can be found, albeit at higher computational cost. A complete system integration is described and results are presented in experiments with up to four physical quadrotors flying in close proximity, and in experiments with two quadrotors avoiding a human.

Reciprocal collision avoidance for multiple car-like robots

In this paper a method for distributed reciprocal collision avoidance among multiple non-holonomic robots with bike kinematics is presented. The proposed algorithm, bicycle reciprocal collision avoidance (B-ORCA), builds on the concept of optimal reciprocal collision avoidance (ORCA) for holonomic robots but furthermore guarantees collision-free motions under the kinematic constraints of car-like vehicles. The underlying principle of the B-ORCA algorithm applies more generally to other kinematic models, as it combines velocity obstacles with generic tracking control. The theoretical results on collision avoidance are validated by several simulation experiments between multiple car-like robots.

Reciprocal Collision Avoidance With Motion Continuity Constraints

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-COs 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.

Real-Time Motion Planning for Aerial Videography With Real-Time With Dynamic Obstacle Avoidance and Viewpoint Optimization

We propose a method for real-time trajectory generation with applications in aerial videography. Taking framing objectives, such as position of targets in the image plane, as input, our method solves for robot trajectories and gimbal controls automatically and adapts plans in real time due to changes in the environment. We contribute a real-time receding horizon planner that autonomously records scenes with moving targets, while optimizing for visibility under occlusion and ensuring collision-free trajectories. A modular cost function, based on the reprojection error of targets, is proposed that allows for flexibility and artistic freedom and is well behaved under numerical optimization. We formulate the minimization problem under constraints as a finite horizon optimal control problem that fulfills aesthetic objectives, adheres to nonlinear model constraints of the filming robot and collision constraints with static and dynamic obstacles and can be solved in real time. We demonstrate the robustness and efficiency of the method with a number of challenging shots filmed in dynamic environments including those with moving obstacles and shots with multiple targets to be filmed simultaneously.

Gesture based human - Multi-robot swarm interaction and its application to an interactive display

A taxonomy for gesture-based interaction between a human and a group (swarm) of robots is described. Methods are classified into two categories. First, free-form interaction, where the robots are unconstrained in position and motion and the user can use deictic gestures to select subsets of robots and assign target goals and trajectories. Second, shape-constrained interaction, where the robots are in a configuration shape that can be modified by the user. In the later, the user controls a subset of meaningful degrees of freedom defining the overall shape instead of each robot directly. A multi-robot interactive display is described where a depth sensor is used to recognize human gesture, determining the commands sent to a group comprising tens of robots. Experimental results with a preliminary user study show the usability of the system.

Shared control of autonomous vehicles based on velocity space optimization

This paper presents a method for shared control of a vehicle. The driver commands a preferred velocity which is transformed into a collision-free local motion that respects the actuator constraints and allows for smooth and safe control. Collision-free local motions are achieved with an extension of velocity obstacles that takes into account dynamic constraints and a grid-based map representation. To limit the freedom of the driver, a global guidance trajectory can be included, which specifies the areas where the vehicle is allowed to drive in each time instance. The low computational complexity of the method makes it well suited for multi-agent settings and high update rates and both a centralized and a distributed algorithm are provided that allow for real-time control of tens of vehicles. Extensive experimental results with real robotic wheelchairs at relatively high speeds in tight scenarios are presented.

Independent vs. joint estimation in multi-agent iterative learning control

This paper studies iterative learning control (ILC) in a multi-agent framework, wherein a group of agents simultaneously and repeatedly perform the same task. The agents improve their performance by using the knowledge gained from previous executions. Assuming similarity between the agents, we investigate whether exchanging information between the agents improves an individuals learning performance. That is, does an individual agent benefit from the experience of the other agents? We consider the multi-agent iterative learning problem as a two-step process of: first, estimating the repetitive disturbance of each agent; and second, correcting for it. We present a comparison of an agents disturbance estimate in the case of (I) independent estimation, where each agent has access only to its own measurement, and (II) joint estimation, where information of all agents is globally accessible. We analytically derive an upper bound of the performance improvement due to joint estimation. Results are obtained for two limiting cases: (i) pure process noise, and (ii) pure measurement noise. The benefits of information sharing are negligible in (i). For (ii), a performance improvement is observed when a high similarity between the agents is guaranteed.

Collision-Free Reactive Mission and Motion Planning for Multi-robot Systems

This paper describes a holistic method for automatically synthesizing controllers for a team of robots operating in an environment shared with other agents. The proposed approach builds on recent advances in Reactive Mission Planning using Linear Temporal Logic, and Local Motion Planning using convex optimization. A local planner enforces the dynamic constraints of the robot and guarantees collision avoidance in 2D and 3D workspaces. A reactive mission planner takes a high-level specification that captures complex motion sequencing, and generates a correct-by-construction controller guaranteed to achieve the specified behavior and be reactive to sensor events. If there is no controller that fulfills the specification because of possible deadlock in the local planner, a minimal set of human-readable assumptions is generated as a certificate of the conditions on deadlock where the task is guaranteed. This is truly a synergistic method: the low-level motion planner enables scalability of the high-level plan synthesis with respect to dynamic obstacles, and the high-level mission planner enforces correctness of the low-level motion. We provide formal guarantees for our approach and demonstrate it via physical experiments with ground robots and simulations with a team of quadrotors.