Inverse kinematics algorithms are commonly used in robotic systems to transform tasks to joint references, and several methods exist to ensure the achievement of several tasks simultaneously. The multiple task-priority inverse kinematics framework allows tasks to be considered in a prioritized order by projecting task velocities through the null spaces of higher-priority tasks. This paper extends this framework to handle set-based tasks, i.e., tasks with a range of valid values, in addition to equality tasks, which have a specific desired value. Examples of set-based tasks are joint limit and obstacle avoidance. The proposed method is proven to ensure asymptotic convergence of the equality task errors and the satisfaction of all high-priority set-based tasks. The practical implementation of the proposed algorithm is discussed, and experimental results are presented where a number of both set-based and equality tasks have been implemented on a 6 degree of freedom UR5, which is an industrial robotic arm from Universal Robots. The experiments validate the theoretical results and confirm the effectiveness of the proposed approach.

Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the conditions UAVs can operate in and the types of missions they can accomplish autonomously. This paper proposes a deep reinforcement learning (DRL) controller to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) algorithm is developed, and is shown to be capable of stabilizing a fixed-wing UAV from a large set of initial conditions to reference roll, pitch and airspeed values. The training process is outlined and key factors for its progression rate are considered, with the most important factor found to be limiting the number of variables in the observation vector, and including values for several previous time steps for these variables. The trained reinforcement learning (RL) controller is compared to a proportional-integralderivative (PID) controller, and is found to converge in more cases than the PID controller, with comparable performance. Furthermore, the RL controller is shown to generalize well to unseen disturbances in the form of wind and turbulence, even in severe disturbance conditions.

Unmanned marine crafts constitute a priority area within several fields of study, and there are still many challenges related to making such vessels autonomous. A basic task of an autonomous marine craft is to follow a general path in the presence of unknown ocean currents. This paper presents a method to achieve this for surface vessels. The results are an extension of the results in [1] regarding path following of space curves when no ocean currents are present, and introduce a virtual Serret-Frenet reference frame that is anchored in and propagates along the desired path. The closed-loop system consists of an ocean current observer, a guidance law, a controller and an update law to drive the Serret-Frenet frame along the path, and is shown to be UGAS. Simulation results are presented to verify the theoretical results.

Abstract-A cornerstone ability of an autonomous unmanned surface vessel (USV) is to avoid collisions with stationary obstacles and other moving vehicles while following a predefined path. USVs are typically underactuated, and this paper extends recent results in set-based guidance theory to an underactuated surface vessel, resulting in a switched guidance system with a path following mode and a collision avoidance mode. This system can be used with any combination of path following and collision avoidance guidance laws. Furthermore, a specific guidance law for collision avoidance is suggested that ensures tracking of a safe radius about a moving obstacle. The guidance law is specifically designed to assure collision avoidance while abiding by the International Regulations for Preventing Collisions at Sea (COLREGs). It is proven that the USV successfully circumvents the obstacles in a COLREGs compliant manner and that path following is achieved in path following mode. Simulations results confirm the effectiveness of the proposed approach.

Abstract-Inverse kinematics algorithms are commonly used in robotic systems to accomplish desired behavior, and several methods exist to ensure the achievement of several tasks simultaneously. The multiple task-priority inverse kinematics framework allows tasks to be considered in a prioritized order by projecting task velocities through the nullspaces of higher priority tasks. This paper extends this framework to handle setbased tasks, i.e. tasks with a range of valid values, in addition to equality tasks, which have a specific desired value. Examples of such tasks are joint limit and obstacle avoidance. The proposed method is proven to ensure asymptotic convergence of the equality task errors and the satisfaction of all high-priority set-based tasks. Simulations results confirm the effectiveness of the proposed approach.