Sampling-based Motion Planning for Active Multirotor System Identification

This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model identification in an active setting, where the vehicle autonomously decides what actions to take in order to quickly identify the model. Our algorithm approximates the belief dynamics of the system around a candidate trajectory using an extended Kalman filter (EKF). It uses sampling-based motion planning to explore the space of possible beliefs and find a maximally informative trajectory within a user-defined budget. We validate our method in simulation and on a real system showing the feasibility and repeatability of the proposed approach. Our planner creates trajectories which reduce model parameter convergence time and uncertainty by a factor of four.Comment: Published at ICRA 2017. Video available at https://www.youtube.com/watch?v=xtqrWbgep5

Automated Scenario Generation Using Halton Sequences for the Verification of Autonomous Vehicle Behavior in Simulation

As autonomous vehicles continue to develop, verifying their safety remains a large hurdle to mass adoption. One component of this is testing, however it has been shown that it is impractical to statistically prove an autonomous vehicle’s safety using real-world testing alone. Therefore, simulation tools and other virtual testing methods are being employed to assist with the verification process. Testing in simulation still faces some of the challenges of the real world, such as the difficulty in exhaustively testing the system in all scenarios it will encounter. Manual scenario creation is time consuming and does not guarantee scenario coverage. Pseudo-random scenario generation is a faster option, but still does not ensure coverage of the state space. Therefore, this study proposes the use of Halton sequences to automatically generate scenarios for autonomous vehicle testing in simulation. It compares these scenarios against a set of pseudo-randomly generated scenarios and assesses the performance of each method to cover the simulation state space and provide an accurate depiction of the capabilities of the system-under-test. These tests are carried out in the CARLA simulation environment on an open source, published driving model called “Learning by Cheating” which takes place as the system-under-test. This study concludes that the scenario set generated by the Halton sequence is better at providing an accurate representation of the capabilities of the system-under-test than the pseudo-random scenario generation method

An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification

Simulation-based verification is beneficial for assessing otherwise dangerous or costly on-road testing of autonomous vehicles (AV). This paper addresses the challenge of efficiently generating effective tests for simulation-based AV verification using software testing agents. The multi-agent system (MAS) programming paradigm offers rational agency, causality and strategic planning between multiple agents. We exploit these aspects for test generation, focusing in particular on the generation of tests that trigger the precondition of an assertion. On the example of a key assertion we show that, by encoding a variety of different behaviours respondent to the agent's perceptions of the test environment, the agency-directed approach generates twice as many effective tests than pseudo-random test generation, while being both efficient and robust. Moreover, agents can be encoded to behave naturally without compromising the effectiveness of test generation. Our results suggest that generating tests using agency-directed testing significantly improves upon random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure

Sampling-Based Threat Assessment Algorithms for Intersection Collisions Involving Errant Drivers

This paper considers the decision-making problem for a vehicle crossing a road intersection in the presence of other, potentially errant, drivers. This problem is considered in a game-theoretic framework, where the errant drivers are assumed to be capable of causing intentional collisions. Our approach is to simulate the possible behaviors of errant drivers using RRT-Reach, a modi ed application of rapidly-exploring random trees. A novelty in RRT-Reach is the use of a dual exploration-pursuit mode, which allows for e cient approximation of the errant reachability set for some xed time horizon. Through simulation and experimental results with a small autonomous vehicle, we demonstrate that this threat assessment algorithm can be used in real-time to minimize the risk of collision

Goal Directed Approach to Autonomous Motion Planning for Unmanned Vehicles

Advancement in the field of autonomous motion planning has enabled the realisation of fully autonomous unmanned vehicles. Sampling based motion planning algorithms have shown promising prospects in generating fast, effective and practical solutions to different motion planning problems in unmanned vehicles for both civilian and military applications. But the goal bias introduced by heuristic probability shaping to generate faster solution may result in local collisions. A simple, real-time method is proposed for goal direction by preferential selection of a state from a sampled pair of random state, based on the distance to goal. This limits the graph motions resulting in smaller data structure, making the algorithm optimised for time and solution length. This would enable unmanned vehicles to take shorter paths and avoid collisions in obstacle rich environment. The approach is analysed on a sampling based algorithm, rapidly-exploring random tree (RRT) which computes motion plans under constrain of time. This paper proposes an algorithm called ’goal directed RRT (GRRT)’ building on the basic RRT algorithm, providing an alternative to probabilistic goal biasing, thereby avoiding local collision. The approach is evaluated by benchmarking it with RRT algorithm for kinematic car, dynamic car and a quadrotor and the results show improvements in length of the motion plans and the time of computing

Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles

In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem, or solves it by using an optimistic global planner. We present a conservative trajectory optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201