Boundary Gap Based Reactive Navigation in Unknown Environments

Due to the requirements for mobile robots to search or rescue in unknown environments,reactive navigation which plays an essential role in these applications has attracted increasing interest.However,most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the environment.This paper aims to address the local minimum problem by employing the proposed boundary gap(BG)based reactive navigation method.Specifically,the narrowest gap extraction algorithm(NGEA)is proposed to eliminate the improper gaps.Meanwhile,we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local minima.Moreover,in order to enhance the smoothness of generated trajectories,we take the robot dynamics into consideration by using the modified dynamic window approach(DWA).Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness.

Safe Navigation for UAV-Enabled Data Dissemination by Deep Reinforcement Learning in Unknown Environments

Unmanned aerial vehicles(UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAVenabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV’s task completion time while satisfying the data transmission task requirement and the UAV’s feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue,we propose a new solution approach by utilizing the most advanced dueling double deep Q network(dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.

A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

Compliant interaction control is a key technology for robots performing contact-rich manipulation tasks.The design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth sensors.This paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled robots.Inspired by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position feedback.Meanwhile,its stability and force-tracking capability are theoretically proven.In addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing tasks.The proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

Path Planning Approach in Unknown Environment

This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.

Obstacle Avoidance Capability for Multi-Target Path Planning in Different Styles of Search

This study investigates robot path planning for multiple agents,focusing on the critical requirement that agents can pursue concurrent pathways without collisions.Each agent is assigned a task within the environment to reach a designated destination.When the map or goal changes unexpectedly,particularly in dynamic and unknown environments,it can lead to potential failures or performance degradation in various ways.Additionally,priority inheritance plays a significant role in path planning and can impact performance.This study proposes a ConflictBased Search(CBS)approach,introducing a unique hierarchical search mechanism for planning paths for multiple robots.The study aims to enhance flexibility in adapting to different environments.Three scenarios were tested,and the accuracy of the proposed algorithm was validated.In the first scenario,path planning was applied in unknown environments,both stationary and mobile,yielding excellent results in terms of time to arrival and path length,with a time of 2.3 s.In the second scenario,the algorithm was applied to complex environments containing sharp corners and unknown obstacles,resulting in a time of 2.6 s,with the algorithm also performing well in terms of path length.In the final scenario,the multi-objective algorithm was tested in a warehouse environment containing fixed,mobile,and multi-targeted obstacles,achieving a result of up to 100.4 s.Based on the results and comparisons with previous work,the proposed method was found to be highly effective,efficient,and suitable for various environments.

Path Optimization Method for the Spherical Underwater Robot in Unknown Environment

One of the major respects of the autonomous capability of underwater robots in unknown environment is to be capable of global path planning and obstacles avoiding when encountering abrupt events.For the Spherical Underwater Robot(SUR)to fulfill autonomous task execution,this paper proposed a novel fuzzy control method that incorporates multi-sensor technology to guide underwater robots in unknown environment.To attain the objective,a SUR we designed is used to design the controller.According to its kinematic model,the safety distance was calculated and sensors(US1000-21 A)were arranged.The novel fuzzy control method was then explored for robot's path planning in an unknown environment through simulation.The simulation results demonstrate the capability of the proposed method to guide the robot,and to generate a safe and smooth trajectory in an unknown environment.The effectiveness of the proposed method was further verified through experiments with a SUR in a real platform.The real environment experiments by using the novel fuzzy control method were compared with the basic control method.The experimental results show that in unknown environments,the proposed method improves the execution efficiency and flexibility of the SUR.