Autonomous planning system based on temporal constraint satisfaction

In order to realize spacecraft autonomy activity duration and complex temporal relations must be taken into consideration. In the space mission planning system, the traditional planners are unable to describe this knowledge, so an object-oriented temporal knowledge representation method is proposed to model every activity as an object to describe the activity's duration, start-time, end-time and the temporal relations with other activities. The layered planning agent architecture is then designed for spacecraft autonomous operation, and the functions of every component are given. A planning algorithm based on the temporal constraint satisfaction is built in detail using this knowledge representation and system architecture. The prototype of Deep Space Mission Autonomous Planning System is implemented. The results show that with the object-oriented temporal knowledge description method, the space mission planning system can be used to describe simultaneous activities, resource and temporal constraints, and produce a complete plan for exploration mission quickly under complex constraints.

Development of ground control station and path planning for autonomous MUAV

For autonomous MUAV,the Ground Control Station(GCS)including hardware and modular software programming such as control modular,navigation modular,display modular and monitor modular becomes important equipment to be developed.This paper emphasizes the global planning and the local replanning arithmetic based on three-dimensional velocity potential field for the moving threats.During the test on the ground and in the sky,GCS show the remote sensing information precisely and send the control command in time.The system can be used to assist in the function of autonomous complex task for MUAV.

Timeline based autonomous mission planning system for deep space exploration

In order to realize the explorer autonomy, the software architecture of autonomous mission management system (AMMS) is given for the deep space explorer, and the autonomous mission planning system, the kernel part of this architecture, is designed in detail. In order to describe the parallel activity, the state timeline is introduced to build the formal model of the planning system and based on this model, the temporal constraint satisfaction planning algorithm is proposed to produce the explorer’s activity sequence. With some key subsystems of the deep space explorer as examples, the autonomous mission planning simulation system is designed. The results show that this system can calculate the executable activity sequence with the given mission goals and initial state of the explorer.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Cooperative path planning for multi-AUV in time-varying ocean flows

For low-speed underwater vehicles, the ocean currents has a great influence on them, and the changes in ocean currents is complex and continuous, thus whose impact must be taken into consideration in the path planning. There are still lack of authoritative indicator and method for the cooperating path planning. The calculation of the voyage time is a difficult problem in the time-varying ocean, for the existing methods of the cooperating path planning, the computation time will increase exponentially as the autonomous underwater vehicle（AUV） counts increase, rendering them unfeasible. A collaborative path planning method is presehted for multi-AUV under the influence of time-varying ocean currents based on the dynamic programming algorithm. Each AUV cooperates with the one who has the longest estimated time of sailing, enabling the arrays of AUV to get their common goal in the shortest time with minimum timedifference. At the same time, they could avoid the obstacles along the way to the target. Simulation results show that the proposed method has a promising applicability.

Relevant experience learning:A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles(UAVs)play a vital role in military warfare.In a variety of battlefield mission scenarios,UAVs are required to safely fly to designated locations without human intervention.Therefore,finding a suitable method to solve the UAV Autonomous Motion Planning(AMP)problem can improve the success rate of UAV missions to a certain extent.In recent years,many studies have used Deep Reinforcement Learning(DRL)methods to address the AMP problem and have achieved good results.From the perspective of sampling,this paper designs a sampling method with double-screening,combines it with the Deep Deterministic Policy Gradient(DDPG)algorithm,and proposes the Relevant Experience Learning-DDPG(REL-DDPG)algorithm.The REL-DDPG algorithm uses a Prioritized Experience Replay(PER)mechanism to break the correlation of continuous experiences in the experience pool,finds the experiences most similar to the current state to learn according to the theory in human education,and expands the influence of the learning process on action selection at the current state.All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV.The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm,while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.

Framework,model and algorithm for the global control of urban automated driving traffic

Automated driving has recently attracted significant attention.While considerable research has been conducted on the technologies and societal acceptance of autonomous vehicles,investigations into the control and scheduling of urban automated driving traffic are still nascent.As automated driving gains traction,urban traffic control logic is poised for substantial transformation.Presently,both manual and automated driving predominantly operate under a local decision-making traffic mode,where driving decisions are based on the vehicle’s status and immediate environment.This mode,however,does not fully exploit the potential benefits of automated driving,particularly in optimizing road network resources and traffic efficiency.In response to the increasing adoption of automated driving,it is essential for traffic bureaus to initiate proactive dialogs regarding urban traffic control from a global perspective.This paper introduces a novel global control mode for urban automated driving traffic.Its core concept involves the central scheduling of all autonomous vehicles within the road network through vehicle-infrastructure cooperation,thereby optimizing traffic flow.This paper elucidates the mechanism and process of the global control mode.Given the operational complexity of expansive road networks,the paper suggests segmenting these networks into multiple manageable regions.This mode is conceptualized as an autonomous vehicle global scheduling problem,for which a mathematical model is formulated and a modified A-star algorithm is developed.The experimental findings reveal that(i)the algorithm consistently delivers high-quality solutions promptly and(ii)the global scheduling mode significantly reduces traffic congestion and equitably distributes resources.In conclusion,this paper presents a viable and efficacious new control mode that could substantially enhance urban automated traffic efficiency.