Zongyuan Shen

Zongyuan Shen, Yaming Ou, Shalabh Gupta et al.

Motion planning in dynamic environments requires robots to continuously adapt their paths in response to environmental changes for safe and uninterrupted navigation. While many surveys have reviewed planning in static settings, systematic reviews focused on dynamic environments remain limited. This paper presents a comprehensive survey of 138 works, primarily published between 2015 and 2025, spanning both classical and learning-based approaches. The motion planning methods are grouped into five categories based on the concepts of sampling, graph search, model predictive control, learning, and additional classical local planning approaches, including velocity obstacles, potential fields and dynamic windows. The learning techniques include supervised learning and reinforcement learning. We also discuss the role of dynamic perception in motion planning, covering techniques for detecting and modeling moving obstacles using cameras, LiDAR, and event-based sensors. The survey analyzes the principles, strengths, and limitations of each method, with particular attention to challenges unique to dynamic environments, such as prediction uncertainty, human-robot interaction, and the freezing robot problem. The survey provides researchers with a structured understanding of motion planning methods in dynamic environments.

Priyanshu Agrawal, Shalabh Gupta, Zongyuan Shen

This paper presents SMART-3D, an extension of the SMART algorithm to 3D environments. SMART-3D is a tree-based adaptive replanning algorithm for dynamic environments with fast moving obstacles. SMART-3D morphs the underlying tree to find a new path in real-time whenever the current path is blocked by obstacles. SMART-3D removed the grid decomposition requirement of the SMART algorithm by replacing the concept of hot-spots with that of hot-nodes, thus making it computationally efficient and scalable to 3D environments. The hot-nodes are nodes which allow for efficient reconnections to morph the existing tree to find a new safe and reliable path. The performance of SMART-3D is evaluated by extensive simulations in 2D and 3D environments populated with randomly moving dynamic obstacles. The results show that SMART-3D achieves high success rates and low replanning times, thus highlighting its suitability for real-time onboard applications.

Zongyuan Shen, James Wilson, Ryan Harvey et al.

This paper addresses the fast replanning problem in dynamic environments with moving obstacles. Since for randomly moving obstacles the future states are unpredictable, the proposed method, called SMARRT, reacts to obstacle motions and revises the path in real-time based on the current interfering obstacle state (i.e., position and velocity). SMARRT is fast and efficient and performs collision checking only on the partial path segment close to the robot within a feasibility checking horizon. If the path is infeasible, then tree parts associated with the path inside the horizon are pruned while maintaining the maximal tree structure of already-explored regions. Then, a multi-resolution utility map is created to capture the environmental information used to compute the replanning utility for each cell on the multi-scale tiling. A hierarchical searching method is applied on the map to find the sampling cell efficiently. Finally, uniform samples are drawn within the sampling cell for fast replanning. The SMARRT method is validated via simulation runs, and the results are evaluated in comparison to four existing methods. The SMARRT method yields significant improvements in travel time, replanning time, and success rate compared against the existing methods.

Zongyuan Shen, Palash Agrawal, James P. Wilson et al.

This paper presents a deep-learning based CPP algorithm, called Coverage Path Planning Network (CPPNet). CPPNet is built using a convolutional neural network (CNN) whose input is a graph-based representation of the occupancy grid map while its output is an edge probability heat graph, where the value of each edge is the probability of belonging to the optimal TSP tour. Finally, a greedy search is used to select the final optimized tour. CPPNet is trained and comparatively evaluated against the TSP tour. It is shown that CPPNet provides near-optimal solutions while requiring significantly less computational time, thus enabling real-time coverage path planning in partially unknown and dynamic environments.

James P. Wilson, Zongyuan Shen, Shalabh Gupta

In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform partitioning scheme that divides the area into obstacle-free convex cells. The partitioning results in large cells in obstacle-free areas and small cells in obstacle-dense areas. Subsequently, the boundaries of these cells are used for sampling; thus significantly reducing the burden of uniform sampling. When compared with a standard uniform sampler, this smart sampler significantly 1) reduces the size of the sampling space while providing completeness and optimality guarantee, 2) provides sparse sampling in obstacle-free regions and dense sampling in obstacle-rich regions to facilitate faster exploration, and 3) eliminates the need for expensive collision-checking with obstacles due to the convexity of the cells. This sampling framework is incorporated into the RRT* path planner. The results show that RRT* with the non-uniform sampler gives a significantly better convergence rate and smaller memory footprint as compared to RRT* with a uniform sampler.

Zongyuan Shen, James P. Wilson, Ryan Harvey et al.

This paper presents a novel algorithm, called MRRT, which uses multiple rapidly-exploring random trees for fast online replanning of autonomous vehicles in dynamic environments with moving obstacles. The proposed algorithm is built upon the RRT algorithm with a multi-tree structure. At the beginning, the RRT algorithm is applied to find the initial solution based on partial knowledge of the environment. Then, the robot starts to execute this path. At each iteration, the new obstacle configurations are collected by the robot's sensor and used to replan the path. This new information can come from unknown static obstacles (e.g., seafloor layout) as well as moving obstacles. Then, to accommodate the environmental changes, two procedures are adopted: 1) edge pruning, and 2) tree regrowing. Specifically, the edge pruning procedure checks the collision status through the tree and only removes the invalid edges while maintaining the tree structure of already-explored regions. Due to removal of invalid edges, the tree could be broken into multiple disjoint trees. As such, the RRT algorithm is applied to regrow the trees. Specifically, a sample is created randomly and joined to all the disjoint trees in its local neighborhood by connecting to the nearest nodes. Finally, a new solution is found for the robot. The advantages of the proposed MRRT algorithm are as follows: i) retains the maximal tree structure by only pruning the edges which collide with the obstacles, ii) guarantees probabilistic completeness, and iii) is computational efficient for fast replanning since all disjoint trees are maintained for future connections and expanded simultaneously.

Zongyuan Shen, Junnan Song, Khushboo Mittal et al.

This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in trajectory length, energy consumption, and reconstruction error.

James P. Wilson, Zongyuan Shen, Shalabh Gupta et al.

In this paper, we develop a new algorithm, called T$^{\star}$-Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T$^{\star}$-Lite algorithm is a significantly faster version of the previously developed T$^{\star}$ algorithm. T$^{\star}$-Lite uses the novel time-risk cost function of T$^{\star}$; however, instead of a grid-based approach, it uses an asymptotically optimal sampling-based motion planner. Furthermore, it utilizes the recently developed Generalized Multi-speed Dubins Motion-model (GMDM) for sample-to-sample kinodynamic motion planning. The sample-based approach and GMDM significantly reduce the computational burden of T$^{\star}$ while providing reasonable solution quality. The sample points are drawn from a four-dimensional configuration space consisting of two position coordinates plus vehicle heading and speed. Specifically, T$^{\star}$-Lite enables the motion planner to select the vehicle speed and direction based on its proximity to the obstacle to generate faster and safer paths. In this paper, T$^{\star}$-Lite is developed using the RRT$^{\star}$ motion planner, but adaptation to other motion planners is straightforward and depends on the needs of the planner

Zongyuan Shen, James P. Wilson, Shalabh Gupta

This paper presents a novel algorithm, called $ε^*$+, for online coverage path planning of unknown environments using energy-constrained autonomous vehicles. Due to limited battery size, the energy-constrained vehicles have limited duration of operation time. Therefore, while executing a coverage trajectory, the vehicle has to return to the charging station for a recharge before the battery runs out. In this regard, the $ε^*$+ algorithm enables the vehicle to retreat back to the charging station based on the remaining energy which is monitored throughout the coverage process. This is followed by an advance trajectory that takes the vehicle to a near by unexplored waypoint to restart the coverage process, instead of taking it back to the previous left over point of the retreat trajectory; thus reducing the overall coverage time. The proposed $ε^*$+ algorithm is an extension of the $ε^*$ algorithm, which utilizes an Exploratory Turing Machine (ETM) as a supervisor to navigate the vehicle with back and forth trajectory for complete coverage. The performance of the $ε^*$+ algorithm is validated on complex scenarios using Player/Stage which is a high-fidelity robotic simulator.