Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page

Verifiable control of a swarm of unmanned aerial vehicles

This article considers the distributed control of a swarm of unmanned aerial vehicles (UAVs) investigating autonomous pattern formation and reconfigurability. A behaviour-based approach to formation control is considered with a velocity field control algorithm developed through bifurcating potential fields. This new approach extends previous research into pattern formation using potential field theory by considering the use of bifurcation theory as a means of reconfiguring a swarm pattern through a free parameter change. The advantage of this kind of system is that it is extremely robust to individual failures, is scalpable, and also flexible. The potential field consists of a steering and repulsive term with the bifurcation of the steering potential resulting in a change of the swarm pattern. The repulsive potential ensures collision avoidance and an equally spaced final formation. The stability of the system is demonstrated to ensure that desired behaviours always occur, assuming that at large separation distances the repulsive potential can be neglected through a scale separation that exists between the steering and repulsive potential. The control laws developed are applied to a formation of ten UAVs using a velocity field tracking approach, where it is shown numerically that desired patterns can be formed safely ensuring collision avoidance

On-orbit assembly using superquadric potential fields

The autonomous on-orbit assembly of a large space structure is presented using a method based on superquadric artificial potential fields. The final configuration of the elements which form the structure is represented as the minimum of some attractive potential field. Each element of the structure is then considered as presenting an obstacle to the others using a superquadric potential field attached to the body axes of the element. A controller is developed which ensures that the global potential field decreases monotonically during the assembly process. An error quaternion representation is used to define both the attractive and superquadric obstacle potentials allowing the final configuration of the elements to be defined through both relative position and orientation. Through the use of superquadric potentials, a wide range of geometric objects can be represented using a common formalism, while collision avoidance can make use of both translational and rotation maneuvers to reduce total maneuver cost for the assembly process

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAV) using nonlinear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant

Autonomous Obstacle Collision Avoidance System for UAVs in rescue operations

The Unmanned Aerial Vehicles (UAV) and its applications are growing for both civilian and military purposes. The operability of an UAV proved that some tasks and operations can be done easily and at a good cost-efficiency ratio. Nowadays, an UAV can perform autonomous tasks, by using waypoint mission navigation using a GPS sensor. These autonomous tasks are also called missions. It is very useful to certain UAV applications, such as meteorology, vigilance systems, agriculture, environment mapping and search and rescue operations. One of the biggest problems that an UAV faces is the possibility of collision with other objects in the flight area. This can cause damage to surrounding area structures, humans or the UAV itself. To avoid this, an algorithm was developed and implemented in order to prevent UAV collision with other objects. “Sense and Avoid” algorithm was developed as a system for UAVs to avoid objects in collision course. This algorithm uses a laser distance sensor called LiDAR (Light Detection and Ranging), to detect objects facing the UAV in mid-flights. This light sensor is connected to an on-board hardware, Pixhawk’s flight controller, which interfaces its communications with another hardware: Raspberry Pi. Communications between Ground Control Station or RC controller are made via Wi-Fi telemetry or Radio telemetry. “Sense and Avoid” algorithm has two different modes: “Brake” and “Avoid and Continue”. These modes operate in different controlling methods. “Brake” mode is used to prevent UAV collisions with objects when controlled by a human operator that is using a RC controller. “Avoid and Continue” mode works on UAV’s autonomous modes, avoiding collision with objects in sight and proceeding with the ongoing mission. In this dissertation, some tests were made in order to evaluate the “Sense and Avoid” algorithm’s overall performance. These tests were done in two different environments: A 3D simulated environment and a real outdoor environment. Both modes worked successfully on a simulated 3D environment, and “Brake” mode on a real outdoor, proving its concepts.Os veículos aéreos não tripulados (UAV) e as suas aplicações estão cada vez mais a ser utilizadas para fins civis e militares. A operacionalidade de um UAV provou que algumas tarefas e operações podem ser feitas facilmente e com uma boa relação de custo-benefício. Hoje em dia, um UAV pode executar tarefas autonomamente, usando navegação por waypoints e um sensor de GPS. Essas tarefas autónomas também são designadas de missões. As missões autónomas poderão ser usadas para diversos propósitos, tais como na meteorologia, sistemas de vigilância, agricultura, mapeamento de áreas e operações de busca e salvamento. Um dos maiores problemas que um UAV enfrenta é a possibilidade de colisão com outros objetos na área, podendo causar danos às estruturas envolventes, aos seres humanos ou ao próprio UAV. Para evitar tais ocorrências, foi desenvolvido e implementado um algoritmo para evitar a colisão de um UAV com outros objetos. O algoritmo "Sense and Avoid" foi desenvolvido como um sistema para UAVs de modo a evitar objetos em rota de colisão. Este algoritmo utiliza um sensor de distância a laser chamado LiDAR (Light Detection and Ranging), para detetar objetos que estão em frente do UAV. Este sensor é ligado a um hardware de bordo, a controladora de voo Pixhawk, que realiza as suas comunicações com outro hardware complementar: o Raspberry Pi. As comunicações entre a estação de controlo ou o operador de comando RC são feitas via telemetria Wi-Fi ou telemetria por rádio. O algoritmo "Sense and Avoid" tem dois modos diferentes: o modo "Brake" e modo "Avoid and Continue". Estes modos operam em diferentes métodos de controlo do UAV. O modo "Brake" é usado para evitar colisões com objetos quando controlado via controlador RC por um operador humano. O modo "Avoid and Continue" funciona nos modos de voo autónomos do UAV, evitando colisões com objetos à vista e prosseguindo com a missão em curso. Nesta dissertação, alguns testes foram realizados para avaliar o desempenho geral do algoritmo "Sense and Avoid". Estes testes foram realizados em dois ambientes diferentes: um ambiente de simulação em 3D e um ambiente ao ar livre. Ambos os modos obtiveram funcionaram com sucesso no ambiente de simulação 3D e o mode “Brake” no ambiente real, provando os seus conceitos