We consider the problem of coordinating a collection of robots at an intersection area taking into account dynamical constraints due to actuator limitations. We adopt the coordination space approach, which is standard in multiple robot motion planning. Assuming the priorities between robots are assigned in advance and the existence of a collision-free trajectory respecting those priorities, we propose a provably safe trajectory planner satisfying kinodynamic constraints. The algorithm is shown to run in real time and to return safe (collision-free) trajectories. Simulation results on synthetic data illustrate the benefits of the approach.
The ambition of this framework is to enable more robustness and distribution in future automated intersection management systems. Indeed, existing intersection management systems such as proposed in @cite_5 @cite_13 @cite_12 plan the complete trajectories of robots through the intersection and ensuring safety requires robots to follow precisely the planned trajectory. By contrast, if priorities only are planned, the priority graph can be conserved even if some unpredictable event requires a robot to slow down for some time.
@cite_5 @cite_13 @cite_12
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This contribution presents a novel approach for efficient online planning of topologically distinctive mobile robot trajectories. Trajectory optimization deforms an initial coarse path drafted by a global planner with respect to robot motion related objectives and constraints. The primary objective is to reach a goal state in minimal time on a collision free path that adheres to the kinematic and dynamic constraints of the mobile robot. Conventional local planners get often stuck in a local optimal trajectory as they are unable to transit across obstacles. Our approach seeks the globally optimal trajectory as it maintains and optimizes a subset of admissible candidate trajectories of distinctive topologies in parallel. In case of dynamic environments the planner switches to the current globally optimal trajectory among the candidate set. The online trajectory planning with timed elastic bands is tightly integrated with the robot motion feedback control. The comparative analysis with conventional local planners confirms the advantages of maintaining distinctive topologies to circumnavigate dynamic obstacles. This thesis describes a scalable robotic navigation system that builds a map of the robot's environment on the fly. This problem is also known as Simultaneous Localization and Mapping (SLAM). The SLAM problem has as inputs the control of the robot's motion and sensor measurements to features in the environment. The desired output is the path traversed by the robot (localization) and a representation of the sensed environment (mapping). The principal contribution of this thesis is the introduction of a framework, termed Atlas, that alleviates the computational restrictions of previous approaches to SLAM when mapping extended environments. The Atlas framework partitions the SLAM problem into a graph of submaps, each with its own coordinate system. Furthermore, the framework facilitates the modularity of sensors, map representations, and local navigation algorithms by encapsulating the implementation specific algorithms module that matches submaps and a verification procedure that trades latency in loop closing with a lower chance of incorrect loop detections inherent with symmetric environments. The framework is demonstrated with several datasets that map large indoor and urban outdoor environments using a variety of sensors: a laser scanner, sonar rangers, and omni-directional video. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.) Recently, several artificial intelligence labs have suggested the use of fully driverless vehicles with the capability of sensing the surrounding environment to enhance the roadway safety. The idea of having fully automated vehicles running in streets was inapplicable for many years until recently when researchers succeeded in releasing fully automated vehicles (without human drivers). Consequently, the paper develops a heuristic optimization algorithm for driverless vehicles at unsignalized intersections using a multi-agent system. The proposed system models the driverless vehicles as autonomous agents controlled by the intersection controller (manager agent). The input information of the system consists of vehicles’ current location, speed and acceleration in addition to the surrounding environment (weather, intersection characteristics, etc.). The intersection controller processes the input information using a built-in simulator: “OSDI” (Optimization Simulator for Driverless vehicles at Intersections). The simulator objective is to optimize the movements of vehicles to reduce the total delay time for the entire intersection and prevent crashes simultaneously. Thereafter, the intersection controller uses the simulator output for controlling the speed profile of the driverless vehicles within the intersection study zone. The proposed system is compared to two different intersection control scenarios: an All-way stop control (AWSC) and an Intersection manager with built-in OSDI. For both scenarios, it is assumed that there are four driverless vehicles (one vehicle per approach) willing to cross a four-legged unsignalized intersection concurrently. The results show using Monte Carlo simulation show that the proposed system reduces the total delay by 35 seconds on average compared to traditional AWSC. This research is considered as a first step in developing an unmanned vehicle technology system. One of the standing challenges in multi-robot systems is the ability to reliably coordinate motions of multiple robots in environments where the robots are subject to disturbances. We consider disturbances that force the robot to temporarily stop and delay its advancement along its planned trajectory which can be used to model, e.g., passing-by humans for whom the robots have to yield. Although reactive collision-avoidance methods are often used in this context, they may lead to deadlocks between robots. We design a multi-robot control strategy for executing coordinated trajectories computed by a multi-robot trajectory planner and give a proof that the strategy is safe and deadlock-free even when robots are subject to delaying disturbances. Our simulations show that the proposed strategy scales significantly better with the intensity of disturbances than the naive liveness-preserving approach. The empirical results further confirm that the proposed approach is more reliable and also more efficient than state-of-the-art reactive techniques.