This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision-free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50% more successful scenarios than deep RL and up to 75% less extra time to reach goal than FMP.

This paper considers the problem of attitude tracking control for uncertain rigid spacecraft subject to control input magnitude and rate saturation (MRS). Firstly, a smooth input MRS model is proposed. Then, a robust attitude tracking control scheme is designed based on the backstepping and finitetime disturbance observer techniques. Finally, the effectiveness of the control scheme derived here is illustrated by numerical simulations

Fault-tolerant attitude tracking control for rigid-flexible coupled spacecraft is studied in this article. Taking actuator faults, measurement errors of attitude and angular velocity, unmeasured modal displacements, model uncertainties, and external disturbances into consideration, an adaptive fault-tolerant attitude tracking control is designed. Then, based on the sequential Lyapunov method, the stability of the closed-loop system is analyzed showing that if the attitude and angular velocity measurement errors are bounded, then successively less conservative bounds on the attitude tracking error and the angular velocity error can be obtained.

The concurrent position and attitude (pose) tracking of a rigid spacecraft is addressed in the presence of actuator faults, mass and inertia uncertainties, and unknown external disturbances. The control design relies on a novel hybrid dual-quaternion integral sliding mode that incorporates a hysteretic switching to avoid the quaternion unwinding problem. On the sliding mode, the pose tracking error is globally finite-time convergent. The resultant control law has a simple structure and consists of a nominal control input, which realizes the sliding mode in the fault-free uncertainty-free case, and an adaptive part, which dynamically compensates for the perturbations due to actuator faults and other uncertainties. Moreover, a real-time control allocation algorithm is devised to deliver the control command to position and attitude actuators in proportion to the effectiveness degree of each actuator. Rigorous proof shows that the proposed strategies can stabilize the spacecraft pose trajectory to a small neighborhood of the sliding mode in finite time. Formation flying of earth-orbiting spacecraft and asteroid body-fixed hovering are simulated to demonstrate the efficacy and broad applicability of the proposed method.

This article presents a distributed, efficient, scalable, and real-time motion planning algorithm for a large group of agents moving in 2-D or 3-D spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: 1) collision avoidance and 2) navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm can find collision-free motions with lower transition time, free from velocity state information of neighboring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2-D and 3-D benchmark simulation scenarios, with results outperforming existing methods.